Data-Driven Investigation of Factors Affecting Surface Transit Speed and Reliability in Toronto

Graham Andrew Devitt

A thesis submitted in conformity with the requirements for the degree of Master of Applied Science Department of Civil Engineering University of Toronto

Graham Devitt

Master of Applied Science

Department of Civil Engineering University of Toronto

2019 Abstract

Cities are increasingly interested in implementing low-cost, small-scale spot treatments to improve transit performance. This research uses a data-driven approach to understand where and why poor transit performance occurs, which is necessary when selecting locations to treat and their appropriate treatments. Automatic vehicle location, general transit feed specification, and ride check data are used to generate a descriptive analysis measuring performance of eight high- frequency bus routes in Toronto at the route, segment, stop, and intersection levels. Clustering and regression models of transit performance at intersections along these routes are developed to determine which features most affect bus speeds and delays. These analyses show that locations with greater traffic cycle split have significantly higher speeds and lower delays, and thus signal timing adjustments are suggested as an effective transit-priority spot treatment. The analyses also suggest queue jump lanes and turning restrictions to be effective when signal timing cannot be adjusted.

Acknowledgments First and foremost, I would like to extend the biggest thank you to my supervisor, Dr. Amer Shalaby, for the constant support, encouragement, and guidance. I’m forever thankful for you accepting me, a not-so-recent graduate of a completely different field with no Transportation or even Civil Engineering experience, into your research group. Thank you for your faith in my work and giving me the opportunity to experience work in the transit field – before working with you I never imagined how this hobby interest of mine could become my lifelong career!

Also from UTTRI, I would like to thank Mahmood Nesheli for your extreme patience with me starting out on the STOIS project. You never hesitated to help me along the way and allowed me to learn how projects can run here. Thanks for letting me pop into your office with nonstop questions. A big thank you to Ehab Diab who got me started on the technical parts of this project and for being the reason I actually know a thing or two about ArcGIS. Thanks as well to Tiggy Chen for carrying out the tedious task of looking at over 100 intersections on Google Satellite and compiling their geometric features into an Excel file.

A very big thank you to the Parsons team: Rita Hu for your patience, guidance, and kindness at every step of the project despite my inexperience, Yannis Stogios for inviting me to work at your office and for helping get this research off the ground, and Sara Khawaja and Steve Chiu for your immense help with the mountains of data processing. From the City of Toronto, I would like to thank Allan Abrogena and David Kuperman for giving the green light for this project and providing all of the data, as well as for your warmth and wisdom at progress meetings.

And of course, my friends, family, and fiancé have been a huge part of my journey at U of T. Thank you to my friends here at UTTRI – I know I have relied on many of you for research advice or frantic questions about upcoming exams. I would not be here without you. Also to my friends around Toronto for helping me take my mind off work when the going got rough. My parents, brother, and sister have always been there for a phone call and to listen to me blab on about my very exciting research. Finally, huge thanks to Colin for always keeping me smiling through the toughest times, and for encouraging me and giving me a reason to strive for a bright future.

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Contents 1 Introduction ...... 1 1.1 Overview ...... 1 1.2 Spot Improvement Programs ...... 1 1.3 Surface Transit Operational Improvement Studies – Phase 3 ...... 2 1.4 Thesis Objectives ...... 3 1.5 Thesis Organization...... 4 2 Literature Review ...... 5 2.1 Transit Quality...... 5 2.2 Assessing Speed and Reliability from Data ...... 6 2.3 Improving Speed and Reliability...... 7 2.3.1 Signal Timing and TSP ...... 8 2.3.2 QJ Lanes...... 9 2.3.3 Stop Location ...... 10 2.3.4 Turning Restrictions...... 11 3 Background Information ...... 12 3.1 STOIS Phase 3 Project ...... 12 3.2 Thesis Overlap with STOIS ...... 13 3.3 Routes in the Scope of Study ...... 13 4 Descriptive Analysis of Transit Performance ...... 18 4.1 Data ...... 18 4.1.1 General Traffic Feed Specification (GTFS) ...... 18 4.1.2 Bus Route Stop List ...... 18 4.1.3 Traffic Signals List ...... 18 4.1.4 Automatic Vehicle Location (AVL) ...... 18 4.1.5 Contractor Collected ...... 19 4.2 Data Processing ...... 20 4.2.1 AVL Processing ...... 20 4.2.2 Contractor Collected Data Processing ...... 24 4.3 Results and Discussion ...... 29 4.3.1 Average Operating Speed ...... 30 4.3.2 On-Time Performance ...... 36 4.3.3 Running Time Variation ...... 40 4.3.4 Signal Delay ...... 43

4.3.5 Delay Analysis ...... 48 5 Analysis of Factors Affecting Transit Performance ...... 55 5.1 Data ...... 55 5.1.1 Geometric Configurations ...... 55 5.1.2 Signal Timing Plans (STP) ...... 55 5.1.3 Turning Movement Count (TMC) ...... 56 5.2 Data Frame Construction ...... 56 5.3 Modeling Methodology ...... 60 5.3.1 Clustering ...... 60 5.3.2 Regression ...... 61 5.4 Results and Discussion ...... 63 5.4.1 Clustering ...... 63 5.4.2 Ordinary Least Squares Regressions ...... 76 5.4.3 Regression Tree Analysis ...... 80 6 Summary and Conclusions ...... 85 6.1 Summary of Thesis...... 85 6.2 Conclusions ...... 85 6.2.1 Descriptive Analysis ...... 85 6.2.2 Clustering and Regression Analyses ...... 87 6.3 Limitations and Future Work ...... 89 7 References ...... 91 8 Appendix ...... 95 8.1 STOIS Phase 3 Evaluation Framework ...... 95 8.1.1 Introduction ...... 95 8.1.2 Main Objectives ...... 95 8.1.3 Evaluation Framework ...... 96 8.2 Operating Speed Data...... 110 8.2.1 Tables ...... 110 8.2.2 Maps ...... 115 8.3 On-Time Performance Data ...... 118 8.3.1 Tables ...... 118 8.3.2 Maps ...... 125 8.4 Signal Delay Data...... 128 8.4.1 Tables ...... 128

8.4.2 Maps ...... 133 8.5 Segment-Level Delay Data ...... 136 8.5.1 Tables ...... 136 8.5.2 Maps ...... 141 8.6 Clusters ...... 144 8.6.1 Cluster Lists ...... 144 8.6.2 Cluster Maps ...... 151 8.7 Regression Trees ...... 159

List of Tables

Table 3-1: The four east-west corridors of study ...... 15 Table 3-2: Properties of primary routes on study corridors ...... 17 Table 4-1: Summary of quantitative metrics to be calculated ...... 29 Table 4-2: Westbound segments with lowest operating speeds for each route in the evening peak period ...... 31 Table 4-3: Westbound segments with lowest operating speeds over all study routes in the evening peak period ...... 32 Table 4-4: TTC's OTP definitions for surface transit (Toronto Transit Commission, 2017) ...... 36 Table 4-5: On-time performance of westbound timing points in the evening peak period ...... 38 Table 4-6: Mean running time variations for all routes, directions, peak periods ...... 42 Table 4-7: Westbound signalized approaches with highest signal delays for each route in the evening peak period ...... 44 Table 4-8: Westbound signalized approaches with highest signal delays over all study routes in the evening peak period ...... 45 Table 4-9: Mean route-level delays for all routes, directions, peak periods ...... 49 Table 4-10: Westbound segments with highest delays for each route in the evening peak period ...... 51 Table 4-11: Westbound segments with highest delays over all study routes in the evening peak period ...... 52 Table 5-1: Features used in the data frame for this chapter ...... 57 Table 5-2: Number of intersection approaches on each corridor for this chapter’s analysis ...... 58 Table 5-3: Geometric feature averages for each cluster ...... 65 Table 5-4: Dynamic feature averages for each cluster ...... 66 Table 5-5 : Target variable averages for each cluster ...... 67 Table 5-6: Comparison of features between Clusters 11 and 15 ...... 68 Table 5-7: Consistently poorly performing approaches in Cluster 5 ...... 71 Table 5-8: Comparison of features between Clusters 5 and 6 ...... 72 Table 5-9: Best performing approaches in Clusters 3 and 13 ...... 75 Table 5-10: Comparison of features between Clusters 1, 3, and 13 ...... 76 Table 5-11: R-Squared values of OLS regressions for each target variable ...... 76 Table 5-12: Summary of OLS regression results for PM target variables ...... 77 Table 5-13: Comparison of green signal time and cycle length features ...... 79 Table 5-14: Optimal tree parameters for each target variable ...... 80 Table 5-15: Summary of operating speed tree ...... 81 Table 5-16: Summary of signal delay tree ...... 82 Table 5-17: Summary of segment-level delay tree ...... 84

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List of Figures Figure 3-1: Sections of primary bus routes running along study corridors ...... 16 Figure 4-1: Westbound evening peak period segment speeds mapped ...... 33 Figure 4-2: Westbound timing points displaying on-time performance in the evening peak period ...... 37 Figure 4-3: Running time variation distributions for all routes, directions, peak periods ...... 41 Figure 4-4: Westbound evening peak period signal delays mapped...... 46 Figure 4-5: Distribution of route-level delays for all routes, directions, peak periods ...... 49 Figure 4-6: Westbound evening peak period segment-level delays mapped ...... 53 Figure 5-1: Locations of intersection approaches studied in this chapter ...... 59 Figure 5-2: Elbow curve with ideal K-value highlighted ...... 63 Figure 5-3: Target variable distributions for each cluster ...... 64 Figure 5-4: Locations of approaches in Clusters 11 and 15 ...... 68 Figure 5-5: Locations of approaches in Clusters 5 and 6 ...... 70 Figure 5-6: Locations of approaches in Clusters 1, 3, and 13 ...... 74

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1 Introduction

1.1 Overview An extensive and robust public transportation network is a vital component of a liveable modern city. Public transit agencies aim to provide an equitable means of transportation for a city or region’s citizens as a competitive mode to private automobiles. While in-vehicle travel times of trips using private vehicles are generally lower than those using transit, the benefits of a good transit network are apparent in terms of reduced user cost, reduction of roadway congestion, and environmental benefits. For these reasons, governments and transit agencies actively work to fund and improve public transit performance in order to encourage a shift in transportation mode choice towards transit and away from private automobiles.

Traditionally, public transit planning in North America has revolved around a top-down approach of route design and network overhaul. While such projects may yield overwhelmingly positive results, the capital costs and implementation times are typically large, which is an undesirable sell from authorities to the public. However, jurisdictions are increasingly studying and implementing bottom-up, data-driven transit-priority spot improvement programs. These programs apply low-cost, minimally intrusive adjustments to existing transit routes and infrastructure identified by performance data to incrementally improve transit speed, reliability, capacity, and safety.

1.2 Spot Improvement Programs Seattle’s King County and the Seattle Department of Transportation have partnered to study and implement at least 20 spot improvements annually to Seattle’s transit network (Seattle Department of Transportation, 2016). Their goal is to improve transit speed, reliability, safety, and overall customer experience through methods that are affordable, innovative, and considerate of the network as a whole. These improvements are meant as a general transit- priority initiative, with continuous evaluation of conditions and public consultation before and after implementation. Examples of spot improvements include transit-only lanes, curb extensions, transit signal priority (TSP) implementation, lane widening, and improved lighting

2 and accessibility at bus stops. San Francisco Municipal Transportation Agency (SFMTA) has initiated the Muni Forward project, identifying spot improvements across San Francisco’s transportation network, with a specific Transit Priority Projects section (SFMTA, 2015). These projects include treatments such as signal timing adjustments, vehicle parking and turning restrictions, and lane designations, with the goal of reducing transit delay and increasing transit reliability across 19 bus and Muni Metro routes. A similar transportation network program, the Transportation Improvement Program (TIP) is currently under implementation in Washington DC by the National Capital Region Transportation Planning Board (TPB, 2018). A project outlined in the TIP is the Bus Priority Plan and Program, wherein data is analyzed to identify corridors with the greatest need for transit improvement based on performance indicators, and a set of possible improvement strategies for each corridor is determined to improve reliability and ridership. Following public consultation and environmental impact studies, the strategies are implemented.

1.3 Surface Transit Operational Improvement Studies – Phase 3 The City of Toronto (the City) and the Toronto Transit Commission (TTC) have begun similar spot improvement programs, which are referred to as the Surface Transit Operational Improvement Studies (STOIS). This thesis is in direct collaboration with Phase 3 of STOIS, focusing on four major thoroughfares with high-frequency and poorly performing transit: Steeles Avenue, Finch Avenue, Sheppard Avenue, and Lawrence Avenue. Parsons Corporation is the consulting firm selected for STOIS Phase 3, with assistance from the Shalaby Group of University of Toronto Transportation Research Institute (UTTRI). The STOIS Phase 3 project consists of a diagnosis stage and an evaluation stage. The diagnosis stage identifies and ranks problematic locations based on severity for each corridor by analyzing quantitative and qualitative data and determining performance metrics at the route, segment, and intersection levels. The evaluation stage suggests potential treatment options for each identified location, and qualitatively ranks these options based on potential transit benefits and planning and capital costs following an evaluation framework.

With these types of programs becoming more common, it is in a transit agency’s great interest to effectively identify the key problematic locations with the greatest need for improvement and

3 determine which low-cost treatments should be considered for an identified problematic location. Given the recent and continuous strides in data science, machine learning, and automation, as well as the growing quantities of data collected by transit agencies, continuous monitoring of transit performance and suggestion of improvement strategies is the direction in which many such agencies are looking to head.

1.4 Thesis Objectives This thesis presents two main objectives with the goal of paving the way towards automated transit monitoring and improvement algorithms:

1) To establish methodologies for measuring performance and conduct a descriptive analysis of the performance of surface transit routes at the route, segment, and intersection levels through processing large amounts of available and collected data. 2) To identify differences between well and poorly performing locations of a route based on roadway layout and traffic dynamics, in turn allowing for a methodological recommendation approach of treatments to improve transit performance with a transit- priority focus.

The first part of the thesis will outline the methodologies developed to complete the diagnosis stage of STOIS Phase 3. This process will use various datasets including Automatic Vehicle Location (AVL), General Transit Feed Specification (GTFS), and onboard vehicle-collected GPS and event data. Performance metrics will be measured for eight routes along the four study corridors of STOIS Phase 3 based on available data quantity and quality and will describe each route’s speed and reliability. They will be compared within the route, against other routes, and against TTC service standards where appropriate.

The second part of this study will work towards the ultimate goal of suggesting treatment options for locations of poor transit performance based on layout and roadway conditions. This analysis will be conducted at the intersection level due to data availability. To do this, key differences between well performing intersections of a route and poorly performing intersections of similar configurations and dynamics must be determined. This step will apply data science techniques to develop a clustering model grouping unidirectional intersection approaches with similar

4 attributes. These clusters will be determined from information obtained from Signal Timing Plans (STP), Turning Movement Counts (TMC), and geometric features from satellite images. Intersection approaches within and between clusters will be compared based on their associated performance metrics determined from the descriptive analysis part of the thesis, and feature differences between the well and poorly performing approaches will be noted. Regression analysis will also be conducted both through ordinary least squares (OLS) and regression trees. These models aim to describe the performance of intersection approaches using their key features. Features of particular interest in these analyses include the existence and length of queue jump lanes, traffic signal timings and split, and vehicle through and turning volumes.

1.5 Thesis Organization This thesis has been organized into 6 chapters. To properly perform the outlined tasks of this thesis, Chapter 2 will delve into a literature review to give insight into defining and calculating appropriate transit speed and reliability metrics, as well as identifying successful transit-priority treatment options. Chapter 3 will outline the background of the project, specifically how this research stems and continues from the STOIS Phase 3 project and which locations will be studied. Chapter 4 will focus on the first research objective, the descriptive analysis of studied routes, complete with data descriptions, processing methodology, results, and discussion. Chapter 5, focusing on the second research objective, will follow a similar format discussing the data description and clustering and regression methodologies, followed by results and discussion. The target variables of this chapter will be some of the results found from Chapter 4. Finally, Chapter 6 will summarize this thesis with conclusions, key findings, and recommendations for future work. Results not presented in the body of the report can be found in the Appendix, Chapter 8.

2 Literature Review

2.1 Transit Quality The quality of a public transportation network is one factor that improves the livability of a city (TCRP, 1997). It is an essential service for many residents and claims a smaller environmental impact than personal vehicles (Murray, Davis, & Stimson, 1998). While no local or regional North American transit system fully recovers its costs on fare alone, greater transit ridership leads to greater revenue, which in turn can lead to transit service improvements. For these reasons, governments strive to attract users to public transportation.

In many ways, private automobiles are more attractive to commuters than public transit. Individuals may have varying reasons for choosing between private autos and transit, including but not limited to speed, reliability, ease of access, comfort, and safety. All these features fall under the umbrella of transit service quality (Redman, Friman, Garling, & Hartig, 2013). Transit riders can be classified as captive riders (with no other transportation alternative) or choice riders (choosing transit over at least one other mode). One study clustered transit users into four groups of captive and choice riders with regular and irregular travel patterns to determine which factors affect their transit experience (Krizek & El-Geneidy, 2007). They found that choice riders value reliability, time, and customer service moreso than captive riders. Another study of public rail service found that surveyed riders valued security and safety as the most important factors in high quality rail transit, with punctuality and frequency following (De Ona, Eboli, & Mazzulla, 2014). However, it is unknow how well this analysis translates to bus service. Taylor and Fink summarize in their review of literature that service quality has greater influence on determining transit ridership than fare (Taylor & Fink, 2003).

This study will focus on two performance-based aspects of transit service quality: speed and reliability. In terms of transit service, speed is well-defined compared to reliability. For transit operations, travel speed, or operating speed, is simply the distance traveled divided by the time it takes to travel. In other words, all times the transit vehicle is stopped, such as during passenger service times at stops and signal delays at red lights, are incorporated into the figure (TCRP, 2003).

Reliability, however, does not have a set definition. Many researchers have defined reliability in their work based on several factors. Headway variation is commonly reported as the key measurable performance indicator defining reliability (Abkowitz & Engelstein, 1984), (Abkowitz & Lepofsky, 1990), (Benezech & Coulombel, 2013). In their 1984 paper, Abkowitz and Engelstein take this assertion back one step by stating that headway variation is directly determined from variation in a vehicle’s scheduled running time. Others report schedule adherence and on-time performance, the percentage of trips that arrive on time, as one of the primary factors defining reliability (Carey, 1999), (Eboli & Mazzulla, 2011), (Redman, Friman, Garling, & Hartig, 2013). There is no set definition of what constitutes an on-time trip, and it is typically up to the discretion of individual transit authorities. The Transit Cooperative Research Program (TCRP) Report 100 reviewed many transit authorities across North America and found that the most common definition of on-time performance is arriving up to 5 minutes late (TCRP, 2003). The TTC defines three levels of on-time performance for surface transit vehicles depending on transit headway (the time between successive transit vehicles). Two of these definitions are based on headway variation and the other is based on schedule adherance (Toronto Transit Commission, 2017). These will be discussed in more detail in Chapter 4. As is clear, while the details of the definition of reliability differ between agencies, all definitions are similar in context. In this study, two reliability metrics will be analyzed: on-time performance and running time variation.

2.2 Assessing Speed and Reliability from Data With the wide-spread publication of transit service standards, it is in the best interests of transit agencies to be continuously assessing performance in order to properly allocate resources for transit improvement. In previous decades, most transportation researchers obtained transit quality data from point observations and in-vehicle observations. One study analyzing headway variability under base and control cases in Boston, Massachusetts, USA noted its limiting factor was sample size, as data collectors were restricted to one point or one vehicle (Abkowitz & Lepofsky, 1990). However, with the recent surge of GPS data collection technologies in transit vehicles and the drastic increase of storage capabilities in data centres, some transit agencies are suddenly experiencing too much data to process (Bertini & El-Geneidy, 2003).

TriMet, the transit authority of Portland, Oregon, USA, has been at the forefront of data collection with their bus dispatch system (BDS), employed on every bus, consisting of automatic vehicle location (AVL), automatic passenger counts (APC), computer-aided dispatching (CAD), radio signaling, and more. Well over 100,000,000 records from the BDS were recorded in 2007 alone. This AVL data was first used to determine performance metrics for TriMet routes including determining average route speed over time of day and schedule adherence (Bertini & El-Geneidy, 2003). A separate study used AVL and APC data to develop a model simulating and predicting dwell time at Portland transit stops (Dueker, Kimpel, Strathman, & Callas, 2004). Later, a more comprehensive study was completed with many performance metrics determined for a TriMet route including average speed, trip time, passenger loads, missed trips, and dwell times (Berkow, El-Geneidy, Bertini, & Crout, 2009).

Outside of Portland, AVL data has been put into practical use for transit analysis in many situations. One study used AVL data from the Massachusetts Bay Transportation Authority in Boston to describe a spectrum of performance metrics including running time variation, headway adherence, and passenger waiting times (Cham, 2003). Another used automatic fare payment and AVL data to determine bus operating speeds, travel time reliability, and headway deviation in Beijing (Ma & Wang, 2014). The study defined travel time reliability as the consistency of travel time for a given route traveling the same path. Their methods for determining average speed and headway variance involved noting the time the first boarding passenger paid using automatic fare payment. This method, however, would not work for Toronto buses since the assumption of at least one boarding passenger per stop is not valid. A study recently completed in Toronto used AVL data to locate bunching incidents, where the headway of consecutive transit vehicles is far lower than scheduled, of streetcar routes (Nguyen, Diab, & Shalaby, 2019). The frameworks for using AVL data to diagnose and monitor transit performance are present, and similar analyses will be conducted in this study.

2.3 Improving Speed and Reliability Knowing where transit routes are performing well and where performance is suboptimal is the first step towards transit improvement. Next, it is important to study spot improvements and the specific scenarios to which they should and should not be applied. In TCRP Report 183, A

Guidebook on Transit-Supportive Roadway Strategies, studies across many locations are aggregated to summarize and draw high-level comparisons between improvements for various scenarios. Such studied improvements span all levels, including at the route, segment, stop, and intersection levels. Operations-based strategies listed include route redesign, vehicle changes, stop relocation and consolidation, and changes between fare payment methods. Traffic-based strategies include turning restrictions, alterations to traffic signals and phases, transit signal priority, and improved enforcement. Other physical and infrastructure-based strategies include adding bus-only lanes, queue-jump lanes, curb extensions, boarding islands, and re-designating lanes to better suit transit operations (TCRP, 2016). While research has been done on a plethora of transit-priority improvements, this study will focus on a subset of these options to align with data availability and the scope of the STOIS project and the intersection level for which data is available. In particular, signal timing and transit signal priority (TSP), queue-jump (QJ) lanes, stop location choice, and turning restrictions will be examined.

2.3.1 Signal Timing and TSP Regardless of transit usage, cities should constantly re-evaluate signal timing plans as traffic volumes and patterns can regularly change (TCRP, 2016). Transit users benefit more with longer green times in the signal cycle (TCRP, 2016). TCRP 183 also states that shorter signal cycles typically benefit buses more than longer signal cycles, as red signal delays are reduced in magnitude. A transit-priority strategy would be to design cycle lengths reflecting transit ridership. However, this must be considered as a corridor solution as uncoordinated consecutive signals, or consecutive signals of varying cycle lengths, can lead to no improvement or even greater delays for buses (TCRP, 2016).

Various algorithms of TSP exist, with the two most common being green extension and red truncation (also known as early green). Green extension holds the green signal phase, up to a maximum specified time, as the bus is still serving a near-side stop. Red truncation changes a green signal to red and hurries through the opposing signal phase, within a minimum time allowance, to anticipate a green phase for the transit vehicle as it finishes serving passengers at a near-side stop. TSP can be costly, but under the right circumstances it is made up for in vehicle- delay and person-delay savings. TCRP 183 summarizes that for a near-side stop, 3-10 seconds of bus delay can be saved, and for a far-side stop, 3-6 seconds of delay savings can be attained

(TCRP, 2016). TSP requires slack green signal time in the cycle, however, and signal timing plans and coordination of downstream signals must be considered.

Other studies have examined the transit benefits of TSP. One such study on a bus route in Eindhoven, Netherlands tested the three cases of no TSP, absolute TSP (for all vehicles), and conditional TSP only for transit vehicles behind schedule. The study found that conditional TSP is the optimal solution to minimize bus delay and maintain reliability along the route and carries minimal costs in travel time for other road users (Furth & Muller, 2000). Another study examined transit priority through passive measures (adjusting and optimizing the signal timing plans) and active measures (through TSP), on a major arterial in San Francisco. While it reported a 14% reduction in bus delay for passive measures, the author notes these measures are typically constrained to simpler transit networks with good bus on-time performance (Skabardonis, 2000). Active measures were found to reduce delays by up to 6 seconds per signal per bus. However, it is noted that success is dependent on slack green time in the cycle and proper signal coordination with downstream signals (Skabardonis, 2000). A study examined a Vancouver corridor with TSP to determine which conditions maximize its effectiveness. They found that with TSP, near-side stops experience greater bus delays than far-side stops (Ngan, Sayed, & Abdelfatah, 2004). Additionally, the authors noted that unless exclusive left turn lanes and phases existed along the corridor, left turning traffic volumes increase delays of TSP intersections. They also confirmed that signal coordination is necessary for effective TSP implementation (Ngan, Sayed, & Abdelfatah, 2004).

2.3.2 QJ Lanes QJ lanes are lanes which buses can use to bypass queues of through traffic. They can be found as transit-only lanes, or more commonly due to capacity constraints, shared with right turning traffic as an exclusive right turning lane. TCRP 183 details that their effectiveness is heightened with existing far-side stops or near-side stops beginning before the QJ lane, as well as for intersections with low right turning volumes (TCRP, 2016). For cases where near-side stops close to the intersection are more prevalent, as is the case with Toronto, QJ lanes were found to be more effective at higher traffic volumes, reducing delay by 1.5 to 7 seconds, though minimally effective for low traffic volumes (TCRP, 2016).

Queue jumps are a common subject of simulation. Research simulating three busy mixed-modal intersections in Kolkata with minimal lane designations found that for traffic volumes of at least 75% of roadway capacity, QJ lanes reduced bus delay by as much as 60 seconds (Maitra, Bhattacharyya, Jose, & Boltze, 2015). However, this is a best case as the delay savings are heavily dependent on traffic and geometric conditions of the intersection. Depending on the intersection, vehicle delay either increased or decreased with the QJ lane. Another study simulated the implementation of consecutive queue jump lanes and found that bus delay savings were greater than simply the sum of individual savings of the QJ lanes and that additional delay was reduced in a QJ multiplier effect (Truong, Sarvi, & Currie, 2016). This effect was particularly pronounced at greater traffic volumes.

2.3.3 Stop Location Stop location is a hotly contended debate between transit authorities, with some favouring near- side stops and others favouring far-side stops. A mail-out survey of transit agencies over the United States found that roughly 65% of agencies do not follow industry guidelines or manuals for stop location, instead deciding in a more tradition-based manner (Fitzpatrick, Perkinson, & Hall, 1997). TCRP 183 concludes that while stop placement should be evaluated on a case-by- case basis, far-side stops are generally preferable to near-side stops. At busier stops and intersections, buses are more likely to arrive at a near-side stop during a green signal as traffic queues have cleared. However, signals are likely to turn red during the bus dwell time at the stop (TCRP, 2016). If some buses manage to pass through the green signal while others do not, reliability issues arise due to induced variability in signal delays and headways. TCRP 183 summarizes that depending on traffic volumes, stop relocation from near-side to far-side has been shown to reduce signal delay from 2.4 to 8.8 seconds (TCRP, 2016). In conjunction with TSP, relocating a stop to the far-side was found to have reduced delay by 6 to 15 seconds. In some situations, however, near-side stops do show benefits over far-side stops, particularly with low right turning volumes and short signal cycles (TCRP, 2016).

One study confirmed that near-side stops experience more delay than far-side stops (Furth & SanClemente, 2006). However, with the addition of a queue jump lane, near-side stops can in fact become more favourable than far-side stops. They state that delays arising from near-side stops can be reduced with lower traffic volumes as well as lowering the signal cycle time (Furth

& SanClemente, 2006). One analysis focused on stop location impacts on general vehicle traffic. The authors simulated stop location proximity to an intersection on the near side finding that stops located farther upstream from an intersection reduce vehicle delays (Gu, Cassidy, Gayah, & Ouyang, 2013). However, this may impact transferring passengers if walking times between bus stops increase. A further study into this found that far-side stops reduce bus delay while near-side stops reduce vehicle delay (Gu, Gayah, Cassidy, & Saade, 2014). The negative impacts, however, decreased drastically the farther a stop is from an intersection. These findings would be important in transit-priority design.

2.3.4 Turning Restrictions Turning restrictions limit where vehicles can turn, streamlining through traffic and reducing the presence of traffic queueing. TCRP 183 states that turning restrictions are most effective at areas with high bus delays due to queueing, especially where no exclusive right or left turn lanes exist. It also states that turn restrictions improve safety of pedestrians, cyclists, and oncoming traffic (TCRP, 2016). A study in Toronto simulated the base case against the case of left turn restrictions for the downtown section of a busy streetcar route. The authors found that against the base case, left turning restrictions reduced route cycle time by 12.8%, bunching incidents by 38.9% and increased operating speed by 12.5% (Shalaby, Abdulhai, & Lee, 2003). While this study was applied to streetcars which operate exclusively in the left lane, the principles could be similar to buses operating almost exclusively in the right lane. Additionally, coordination of downstream signals must be considered.

While great strides have been made by emerging spot improvement programs, little framework exists to methodologically identify problematic locations and potential treatment options. As outlined above, a multitude of studies have given insight into transit performance from various treatments and interventions. This work will seek to utilize the above information gathered from the literature, and automation and data science techniques, to develop streamlined approaches that can be used for the assistance of future transit-priority spot improvement programs.

3 Background Information

This research stems off the STOIS Phase 3 project for the City. The basis for this thesis is the data processing performed by UTTRI for the STOIS project. The City and Parsons have provided most of the data used in this thesis, and the bus routes studied in this research are those studied in STOIS. This chapter details the STOIS Phase 3 project, where this thesis overlaps with the project, and provides details of the study routes for the thesis.

3.1 STOIS Phase 3 Project STOIS Phase 3 aims to diagnose problematic locations and suggest transit-priority treatment options along four major corridors. The project was split into a diagnosis stage and an evaluation stage. A third-party group collected the ride-check data used for the thesis, which consists of GPS and event data for the primary bus routes serving these corridors. This data, alongside AVL data provided by the City, formed the basis of the diagnosis stage for determining which problematic locations to treat. Data was collected on weekdays during both morning and evening peak periods. These will be described in greater detail in Chapter 4. UTTRI assisted with the development on an evaluation framework and methodologies to process the given data into usable quantitative results. The evaluation framework lists a pool of potential treatment options within the scope of the City’s Request for Proposal. A modified version of this framework is found in Section 8.1 of the Appendix (Parsons; UTTRI, 2018). Treatment options to be considered for this project aim to improve an existing transit route’s speed and/or reliability. Once the data was processed, problematic locations were identified by determining the most consistently poorly performing areas based on threshold performance values determined by UTTRI and Parsons. Site inspections were conducted at these locations by UTTRI to confirm conditions and check feasibility and effectiveness of the list of treatment options. The evaluation stage of the project saw the determination of potential treatment options for the identified locations. For each location, if multiple treatment options were suggested, a high-level qualitative evaluation was conducted by UTTRI to determine the order of preference for the treatments. Consultation of parallel ongoing and future studies conducted by the City and TTC ensured that treatment options aligned with the City’s vision of the location.

3.2 Thesis Overlap with STOIS Given the goal of this thesis is to compare differences between well-performing and poorly- performing locations of a route, performance metrics must be determined at the route, segment, and intersection levels. As such, Chapter 4 of this thesis, covering the determination of performance metrics in a descriptive analysis of the routes of study, mirrors the diagnosis stage of STOIS Phase 3. This chapter follows the data, methodologies, and results determined by UTTRI for the STOIS project. Specifically, the results of average speed, on-time performance, running time variation, signal delay, and segment-level delay from this thesis were presented as the results of the diagnosis stage for the project. As such, the AVL and contractor-collected data provided for STOIS are used as the primary data sources for this thesis. However, Chapter 5, the investigation of factors affecting transit performance, is unique to this research. As Chapter 5 builds upon the results obtained from Chapter 4, the studied bus routes are those of the STOIS project. Chapter 5, however, aims to present results with broader implications for general bus routes.

3.3 Routes in the Scope of Study Four city-wide east-west corridors were selected to study for STOIS Phase 3: Steeles Avenue, Finch Avenue, Sheppard Avenue, and Lawrence Avenue/Dixon Road. All four corridors are busy uptown thoroughfares for traffic and public transit and are utilized by many commuters as their primary road to travel across Toronto. Combined, these corridors span Old Toronto, North York, Etobicoke, and Scarborough, and service urban, suburban, and industrial neighbourhoods of the city. Steeles Avenue forms the northern border of Toronto with the Regional Municipality of York, bordering Vaughan west of Yonge Street and Markham east of Yonge Street. All four corridors are designated with “West” or “East”, depending which side of Yonge Street a section is located. The West and East portions of each corridor are each served by a designated high- frequency TTC bus route, most of which terminate at Yonge Street in a Line 1 subway station. While other routes serve some portions of these corridors, they primarily exist to serve the surrounding neighbourhoods and are mostly infrequent and circuitous by design, and thus are not

14 considered in this study. Table 3-1 displays details of the corridors of study. Figure 3-1 displays the main transit routes studied in this research along the corridors, and Table 3-2 provides operational details of each transit route. It should be noted that only route operations along the study corridors will be considered. The Steeles routes, for example, terminate at Finch Station traveling along Yonge Street between Finch and Steeles Avenues. This section of Yonge Street is not considered in this research. Similarly, the portion of Route 54 Lawrence East along Eglinton Avenue East and Leslie Street is not considered.

The data collection and analysis for each route is conducted for both eastbound and westbound directions during both morning and evening peak periods. As such, four sets of results for each route were generated, referred to as the four “direction-peak combinations”

Table 3-1: The four east-west corridors of study

Corridor Scope of Study Road Type Scope Primary TTC Notes Boundaries (MA = major arterial, mA = Length Routes Serving minor arterial, C = Collector) (km) (City of Toronto Transportation Services, 2018) Steeles Highway 27 (west) MA (west of McCowan Rd) 30.5 60 Steeles West • Excludes Steeles Ave W west of Avenue Markham Road (east) mA (east of McCowan Rd) 53 Steeles East Highway 27 • Excludes Steeles Ave E east of Markham Rd Finch Finch West Station MA (west of Markham Rd) 22.7 36 Finch West • Excludes Finch Ave W west of Avenue (west) mA (east of Markham Rd) 39 Finch East Finch West Station due to Morningside Avenue planning for Finch West LRT (east) Sheppard Weston Road (west) MA 31.8 84 Sheppard West • Excludes Sheppard Ave E east of Avenue Meadowvale Road 85 Sheppard East Meadowvale Rd (east) • Limited data for section following Line 4 Sheppard (Yonge Street to Don Mills Road) Lawrence Highway 427/City MA (west of Morningside 36.8 52 Lawrence West • Excludes Lawrence Ave E Avenue/ Boundary (west) Ave) 54 Lawrence East between Yonge St and Leslie St Dixon Road Starspray Boulevard mA (Morningside Ave to • Lawrence Ave W and Dixon Rd (east) Port Union Rd) connected via Scarlett Rd C (east of Port Union Rd)

Figure 3-1: Sections of primary bus routes running along study corridors

Table 3-2: Properties of primary routes on study corridors

Route Avg Weekday Sections of Interest (west to east) Branches Serving Peak Headway Peak Ridership† (min)†† AM PM AM PM 60 Steeles West 7600 10200 Highway 27 to Pioneer Village Station 60D 7.33 7.5 Pioneer Village Station to Yonge Street 60A, 60D, 60E** 2.68 3 53 Steeles East 5600 9700 Yonge Street to Markham Road 53B, 53E**, 53F** 2.9 3.08 36 Finch West* 9000 13200 Finch West Station to Finch Station 36A 6 6.75 39 Finch East* 5100 7000 Finch Station to Victoria Park Avenue 39A, 39B, 39C 3.33 4.67 Victoria Park Avenue to Neilson Road 39A, 39B 5 7 Neilson Road to Morningside Avenue 39B 15 14 84 Sheppard West 5100 5700 Weston Road to Sheppard West Station 84A, 84C, 84D 4.25 5.75 Sheppard West Station to Sheppard-Yonge Station 84A, 84C, 84D, 84E** 2.88 3.65 85 Sheppard East* 4500 7000 Sheppard-Yonge Station to Don Mills Station 85J 15 16 Don Mills Station to Meadowvale Road 85A, 85C 6.5 6.25 52 Lawrence West 9200 13300 Highway 427 & Dixon Road to Scarlett Road 52A, 52B 6 5.92 Scarlett Road to Lawrence West Station 52A, 52B, 52F, 52G 3 2.92 Lawrence West Station to Lawrence Station 52A, 52B, 52F 4 3.9 54 Lawrence East 7100 11300 Leslie Street to Lawrence East Station 54A, 54B 4.5 5.13 Lawrence East Station to Orton Park Road 54A, 54B, 54E** 3 3.5 Orton Park Road to Starspray Boulevard 54B, 54E** 4.5 5.3 †Data collected between Apr 2013 and Sep 2018 by TTC. Figures represent entire route and not just portions within scope of study ††Average headway of all branches. May & June 2018 schedule data *Route had an accompanying “Rocket” express route at time of data collection which is not included in ridership or headway values. Rocket routes have since been replaced with 900-series express routes **Branch no longer exists and has been replaced with a 900-series express route. Included in ridership and headway values

4 Descriptive Analysis of Transit Performance

This chapter covers the first of two thesis objectives. It includes three sections: data description, data processing methodologies, and results and discussion.

4.1 Data 4.1.1 General Traffic Feed Specification (GTFS) GTFS refers to the standardized format of public transit scheduling data and information provided by transit agencies. The TTC offers this data publicly. The 28 August 2018 TTC Stops file, consisting of one record for every stop operated by the TTC, was accessed for this study.

Three fields from this data file were of interest. These include the “stop_code” field denoting the stop’s unique ID (Stop ID) value, as well as the stop’s latitude and longitude.

4.1.2 Bus Route Stop List A complete list of bus stops was provided for each route in this study by Parsons. This list includes attributes such as stop sequence, name, Stop ID, route branches serving it, stop location (near, far, midblock), and timing point status.

4.1.3 Traffic Signals List A list of traffic signals along each corridor of study was provided by Parsons. This list was pared from the City’s open data file containing all signals in Toronto. Attributes used in this study include the signal’s unique identification (PX) value, name of main road, name(s) of intersecting road(s), latitude, and longitude.

4.1.4 Automatic Vehicle Location (AVL) AVL data describes the real-time location information of all buses in the TTC network. GPS measurements are collected every 20 seconds noting the transit vehicle’s coordinates. In this study, AVL data for each route of interest was provided for the period of 19 March 2018 to 29 June 2018 during morning (AM) peak period (6:30am-10:30am) and evening (PM) peak period (3:00pm-7:00pm). This data displays both eastbound (EB) and westbound (WB) trips during both peak periods. Varying levels of aggregation exist for AVL data depending on the purpose of study. The AVL data used for this study, which was provided by the City via Parsons, was

19 aggregated to list one record per stop along a route, meaning no intermediate GPS points were shown.

4.1.5 Contractor Collected For each route of study, the contractors rode between 20 and 30 complete trips for every combination of direction (EB and WB) and peak period (AM and PM). These were collected between June 2018 and January 2019. Each run from the contractor data was assigned a unique 8-digit number. The first two digits noted the route number, the following two digits indicated the direction (01 for EB, 02 for WB), the following two digits indicated the peak period (01 for AM, 02 for PM), and the final two digits indicated the run number within the set of runs for a given route’s direction-peak combination. Data collected from the contractors was presented in the form of one Excel file per run. Each run’s file contained 3 worksheets titled GPS, Delay, and Event, and are each discussed in detail.

4.1.5.1 GPS

The GPS tab contains the GPS points of the run which were recorded every two, three, or five seconds depending on the device used. Each recorded point has a corresponding date, time, latitude and longitude. The average speed between the current point and previous point was calculated by the device from the previous point’s GPS coordinates.

The GPS data quality varied between runs, with some runs displaying spotty sets of points where time differences between subsequent points were far greater than the collection frequency. Every route experienced slips in quality to some degree, though some routes showed a larger number of poor quality runs than others. For each run, GPS points were observed manually in ArcGIS, and IDs of segments containing poor quality GPS data were noted in a separate Excel file, referred to as the route’s Binary Tables, to discount during the segment-level analyses. In the Binary Tables file, each segment for each run is assigned a 1 for good, usable data, or 0 for poor, unusable data.

4.1.5.2 Delay

The Delay tab is directly derived from the GPS tab. As per the guidelines stated by the City, delays are classified as bus movement below 5km/h. Speed values less than this 5km/h threshold in the GPS data were noted. A delay record denotes the latitudes and longitudes of the start and end points of consecutive GPS points with this condition. The GPS time stamp of the start point

20 was recorded, and the duration of this delay was calculated as the difference of time stamps between the end and start points.

Since the delay data is based directly on the GPS data, its reliability is hindered at locations of spotty GPS. These locations of poor-quality delay are not included in the analysis, as will be discussed in Section 4.2.2.

4.1.5.3 Event

The data collectors were tasked with recording events occurring on the runs which may influence the data analysis. Recurring events at a given location could possibly give insight into traffic dynamics or identified sources of issues. Some specific events were pre-set into the app used by surveyors, and the surveyors were also able to write their own events. Examples of pre-set events include “door open”, “door close”, “wheelchair/stroller”, “congestion”, “construction”, “rail crossing”, “bus ahead”, and “driver change”.

Due to the nature of data collection and variety of surveyors over several months, this data is highly subjective. Thus, runs and routes cannot be compared for quantities of event occurrences. Not every run includes an event file, indicating this data may not have been collected for all runs. Apart from “door open” and “door close” events, and events occurring during passenger service time such as “wheelchair/stroller”, the event files were used strictly qualitatively.

4.2 Data Processing Segment-level performance metrics are denoted by the segment’s end stop. For example, the segment from Stop A to Stop B is referred to as Segment B.

4.2.1 AVL Processing Daily AVL data was provided by the City between the dates of 19 March 2018 and 30 June 2018. Only data for peak periods (6:30-10:30am and 3:00-7:00pm) was studied. The methodology was determined, tested, and troubleshooted by UTTRI, while Parsons was responsible for the actual processing of AVL data. The following preprocessing steps were completed, preparing the determination of performance metrics.

1) Saturdays, Sundays, Mondays, and Fridays were removed from the data. Additionally, Tuesdays of weeks with holiday Mondays and Thursdays of weeks with holiday Fridays were removed. These days of the week are not considered representative of typical weekday peak period traffic conditions. 2) A unique ID was assigned to every trip combining the date and daily trip ID. Values in the provided “Tripid” field are reused on different dates, leading to the need for all trips in the data to be uniquely identifiable. 3) Data was split into four worksheets representing eastbound morning, eastbound evening, westbound morning, and westbound evening runs.

The following methodologies were applied in Excel to each of the four direction-peak combinations.

4.2.1.1 Average Segment Operating Speed

In general, average speed is calculated as the distance traveled divided by time.

∆푑 푣 = ∆푡

Operating speed includes dwell time, or passenger service time. Since each row denotes the bus departure from one segment and arrival at the next segment, the time difference can be calculated directly by subtracting departure time from arrival time. Due to GPS recordings of AVL data, this time is rounded to the nearest 20 seconds. Distance can be computed if the latitude and longitude values of each stop are known. The following steps were performed.

1) Incomplete trips and detours were removed from the data. This step was done by determining the Stop ID of the first and last stop of the run in the data and deleting those whose Stop ID values do not match those of the termini. This was done in order for the operating speed values to be representative of a typical run on that route. 2) Latitude and longitude values were matched to each departure and arrival bus stop. These values were obtained from GTFS data and were matched from the Stop ID values. 3) Distances between the arrival and departure stops were calculated from their respective geographic coordinates. This was calculated using the following formula.

−1 ∆푑 = 푅퐸 ∗ cos (cos(90 − 퐿푎푡퐷) ∗ cos(90 − 퐿푎푡퐴) + sin(90 − 퐿푎푡퐷) ∗ sin(90 − 퐿푎푡퐴)

∗ cos(퐿푛𝑔퐷 − 퐿푛𝑔퐴))

Here, RE represents the radius of the Earth (6371km), LatD and LatA represent the

latitudes of the departure and arrival stops respectively, and LngD and LngA represent the longitudes of the departure and arrival stops respectively. The distance is determined in kilometres. 4) The speed is then determined by dividing the distance found in step 3 by the difference between arrival time (“ActualTime_ArrivalStop”) and departure time (“ActualTime_DepartureStop”). 5) Average speed values for each arrival stop were found using a pivot table. By using a row’s arrival stop instead of departure stop, these values were assigned at the segment level.

4.2.1.2 On-Time Performance (OTP) Metrics

In this study, OTP is measured at a route’s timing points. The TTC’s definition of OTP for surface transit depends on a route’s headway. For high-frequency routes with headways of 10 minutes or less, OTP is based on headway deviation. For less frequent routes, OTP is defined by schedule adherence. All bus routes in this study fall into the high frequency (headway-based OTP) category for some or all sections of the route. Within the scope of this study, timing points with headways greater than 10 minutes are only found where a single branch of a multiple- branch route runs. The following steps were performed.

1) The data was cropped to only include timing points. These were found using the route’s stop list. 2) Scheduled headways at each timing point were determined from the TTC. If multiple branches of a route exist, the average operating headway was calculated using the following formula. 1 ℎ푎푣푔 = 푛 1 ∑푖=1 ( ) ℎ푖

Here, hi represents the individual branch headway, n represents the number of branches,

and havg represents the average headway along a section with multiple branches.

For timing points with headways greater than 10 minutes, the schedule adherence for each row was noted. Otherwise, steps 3-5 were followed. 3) The data was cropped to only include consecutive trips. Determining headway requires comparing consecutive trips at the same stop. If a trip is missing in the data, the headway of the following trip cannot be computed. To determine which trips are consecutive, the data was sorted by timing point, then by date and scheduled time. The difference between two consecutive rows’ “ScheduledTime_DepartureStop” values was calculated and compared to the TTC’s scheduled headway. If this calculated value was greater than the scheduled headway, that row was removed. In the case of multiple branches, where headways are not necessarily uniform, these were removed if the calculated headway was greater than the lowest scheduled headway of all branches. 4) Given the remaining consecutive rows, actual headway was determined. The data was re- sorted by actual time, as to account for any buses overtaking their previously scheduled trip and thus not yield negative headway values. Then, two consecutive rows’ “ActualTime_DepartureStop” values were subtracted to find actual headway values. 5) For each available data point, the headway deviation was determined. This was done by dividing the actual headway by the scheduled headway. These were the steps conducted for headway-based OTP.

4.2.1.3 Running Time Variation

Running time variation was found by determining the fraction of actual end-to-end travel time divided by scheduled end-to-end travel time of a run. The following steps were performed.

1) As with the average operating speed methodology, incomplete trips were removed from the data in the same manner. 2) The schedule adherence at the start and end of every trip was found. For every trip, the first row of the trip was identified and its “ScheduleAdherence_DepartureStop” value is noted. Similarly, the last row of the trip was identified and its “ScheduleAdherence_ArrivalStop” was noted. 3) The actual running time was calculated for every trip. This step was done using a pivot table displaying each run’s noted beginning and ending schedule adherence and subtracting the beginning adherence from the ending adherence.

4) The actual running time was divided by the trip’s scheduled running time, which was calculated by finding the difference between the trip’s final “ScheduledTime_ArrivalStop” and initial “ScheduledTime_DepartureStop”

4.2.2 Contractor Collected Data Processing Several software and tools were used to sift through the variety of collected data presented, including Excel, Python, and ArcGIS. Due to the limited number of runs (at most 30) in each time-direction combination as well as variations in data quality, the results produced from the following steps are primarily designed to supplement the results from the much larger sample size of AVL data. Prior to data processing, the GPS points of each run were examined and locations (segments, signals) of poor GPS quality for that run were noted in a table referred to as the Binary Table. Nevertheless, the collected data introduces real-time qualitative insights into the dynamics of the studied bus routes and corridor conditions.

4.2.2.1 Dwell Times

While this thesis does not perform a thorough analysis into dwell times of routes, these values must be approximated and subtracted from signal delay estimates. Signal delays occur when the bus is idling at a red signal or queue caused by red signal, but not when actively serving passengers. “Door open” and “door close” events were used to approximate dwell times. The following is a list of steps used to approximate dwell time at each stop for each run for a given route.

1) Using Python and manual judgement, the route’s event tables were filtered to isolate “door open” and “door close” related events and saved as separate “Doors” files. 2) On ArcGIS, the route’s EB and WB stops lists and Doors files for all runs were uploaded as Shapefiles based on their latitude and longitude values in the World Geodetic System (WGS) 1984 coordinate system. 3) 30-metre spatial buffers were placed around the stops to represent a reasonable passenger service area, as buses may stop short or ahead of the stop. This buffer size has been used in a previous similar study (Bertini & El-Geneidy, 2003). 4) Using the Spatial Join tool in ArcGIS and Python to automate this process, events from the Doors Shapefiles were joined to bus stop buffers in their respective direction of

travel. All Doors records, including those not joined to a stop buffer, were kept. The tables of the subsequently created Spatial Join Shapefiles were saved as Excel files, one file per run. 5) Using Python to automate this process, the dwell time at each stop for each run was determined: a. Within a stop buffer, the time of a “door open” event was subtracted from the time of a “door close” event. Dwell times occurred at a stop if at least one event of a “door open” – “door close” pair was joined to its buffer. b. For each run, a pivot table was created displaying the total dwell time at each stop. If no Doors events were recorded in a stop’s buffer, the dwell time for that run at that stop was 0. Only runs with at least one recorded “door open” – “door close” event pair were analysed as not every run contained event data. c. For each direction-peak combination, a summary table displaying the dwell times of all runs was created. All stops (referenced with Stop ID values) of a route were listed as the rows, and all runs (referenced with the run number) for that direction- peak combination were listed as the columns.

4.2.2.2 Signal Delay

Signal delay was determined as the delay time occurring at a traffic signal minus the dwell time. As per the City’s delay definition outlined previously, delay was considered when the bus was traveling at less than 5km/h. Due to the aggregation of the provided delay data, GPS data was used to determine for the delay time at the signals. While GPS data was available for every run, only runs with sufficient event data to determine dwell times were processed, as signal delay cannot be accurately determined without an approximation for dwell time of near-side stops. The following steps outline the process to determine signal delay for a given route.

1) On ArcGIS, the route’s traffic signals list and GPS files for all runs were uploaded as Shapefiles based on their latitude and longitude values in the WGS 1984 coordinate system. 2) For each traffic signal, a buffer was placed around it representing the signal queue area. This buffer was 100 metres for major and minor arterials and 50 metres for collector roads. These values were selected based on manual observation of queueing conditions at

peak periods, with consideration that smaller buffers would miss vital queueing data, while larger buffers would encompass delays not specific to signals. 3) Using the Spatial Join tool in ArcGIS and Python to automate this process, GPS Shapefiles were joined to traffic signal buffers. Due to the large processing times, only GPS points appearing within the buffers were kept. The tables of the subsequently created Spatial Join Shapefiles were saved as Excel files, one file per run. 4) Using Python to automate this process, the delay time at each signal for each run was determined: a. For every run of each direction-peak combination, previously noted locations of poor GPS quality were removed from study using the Binary Tables. b. Since traffic signal buffers are circular and encompass the far side of the intersection, all GPS points on the far half of the buffer were removed. This was done by removing all points with longitude greater than the signal’s longitude for eastbound runs and removing those with longitude less than the signal’s longitude for westbound runs. c. The GPS data was cleaned into delay data by removing all GPS points with speed greater than 5km/h. d. Delay times at a signal were calculated by subtracting the time of the first delay point from the time of the last delay point for a set of consecutive delay points within a buffer. e. For each run, a pivot table was created displaying the total delay time at each signal. If no delay times were recorded in a signal’s buffer, the delay time for that run at that signal was 0. f. For each direction-peak combination, a summary table displaying the delay times of all runs was created. All signals (referenced with PX values) of a route were listed as the rows, and all runs (referenced with the run number) for that direction- peak combination were listed as the columns. 5) Using the Identify tool in ArcGIS, all near-side stops located at signalized intersections were noted, with the Stop ID value of the near-side stop listed with the corresponding PX value of the traffic signal. This was created to link bus stops and traffic signals. Using

this PX-Stop ID list, the direction-peak summary tables of dwell times and delay times were linked. 6) Using Python to automate this process, signal delays were calculated for each run at each traffic signal: a. If a near-side bus stop was linked to that signal, the signal delay was calculated as the delay time minus the dwell time. If the dwell time was greater than the delay time, the signal delay was set as 0. b. If no near-side bus stop was linked to that signal, the signal delay equaled the delay time.

4.2.2.3 Delay

To supplement the segment-level speed data obtained from AVL, delay data from runs collected by the contractor was attributed to specific locations in order to determine consistently problematic segments. This data, listing aggregated records of consecutive GPS points, lists latitude and longitude as well as duration of delays, or continuous occurrences of bus speeds below 5km/h. Naturally, longer segments would accumulate more delay. Thus, delay was measured on a per distance basis, with units of min/km. The following steps show the methodology for determining the locations of these delay occurrences for a given route.

1) From the bus stop Shapefiles created from the dwell analysis, a route Shapefile was created from ArcGIS’s Route Builder, ordered by the stop sequence field. 2) This route Shapefile was segmented using the ArcGIS tool Split Line At Point, and each segment was joined with the stop list attributes of its end stop. Thus, the segments in the Shapefile were named in accordance with the segment nomenclature previously defined. 3) Using the Calculate Geometry tool on ArcGIS, the segment lengths in kilometres were determined for each segment. 4) The delay occurrences for all runs were uploaded as Shapefiles based on their latitude and longitude values in the WGS 1984 coordinate system. 5) Using the Near tool in ArcGIS and Python to automate this process, the delay occurrences were matched to the nearest segment along the segmented Shapefile within a tolerance of 50 metres. The tables resulting from the Near analysis were exported as Excel files, one file per run.

6) Using Python to automate this process, the delay durations at the segment level for each run were determined: a. For every run of each direction-peak combination, previously noted locations of poor GPS quality were removed from study using the Binary Tables. b. The resulting Excel files contain a “NEAR_FID” column. This indicates the FID, the unique ID value ArcGIS assigns to a data point, of the segmented Shapefile to which the Near analysis matched each delay occurrence. Segment ID values (the Stop ID value of the segment’s end bus stop) corresponding to the FID values in the segmented Shapefile were matched to the “NEAR_FID” values in these Excel files. c. A pivot table was created listing the sum of delay duration for each segment ID. d. For each time-direction combination, a summary table displaying the delay durations of all runs was created. All segments (referenced with segment ID values) of a route were listed as the rows, and all runs (referenced with the run number) for that time-direction combination were listed as the columns. e. For segment-level analysis, the delay durations in these summary tables were divided by the segment lengths determined from the segmented Shapefiles. For route-level, the total delay determined from the Near tool along the corridor of study for each run was divided by the length of the corridor served by that run’s route branch.

4.2.2.4 Other Events

Other events collected from the contractors, excluding those related to dwell times, were qualitatively analyzed. The location of each occurrence was determined at the segment level in a similar fashion to the delay duration. The following steps show the methodology for determining these event locations for a given route.

1) The events for all runs were uploaded as Shapefiles based on their latitude and longitude values in the WGS 1984 coordinate system. 2) Using the Near tool in ArcGIS and Python to automate this process, the events were matched to the nearest segment on the segmented Shapefile created in the delay analysis

section with a tolerance of 50 metres. The tables resulting from the Near analysis were exported as Excel files, one file per run. 3) Using Python to automate this process, the locations of each event for a given direction- peak combination of a route were compiled onto a master list. a. Segment ID values corresponding to the FID values in the segmented Shapefile were matched to the “NEAR_FID” values in these Excel files. b. Each event matched to a segment was added to a list containing the segment name, sequence, run number, and event. One list was created for each direction- peak combination for each route.

4.3 Results and Discussion

This section presents the results determined from the performance metric methodologies outlined in Section 4.2. These metrics are measured at the route, segment, stop, or intersection level. Table 4-1 displays a summary of the primary performance metrics in this investigation as well as the level at which the metric is measured.

Table 4-1: Summary of quantitative metrics to be calculated

Performance Metric Data Source Level(s) Average Operating Speed AVL Segment On-Time Performance AVL Stop Running Time Variation AVL Route Signal Delay Contractor Intersection Delay Contractor Route, Segment

Results of each of the listed metrics will be presented in graphical, tabular, and/or map form. Due to the immense quantity of results developed, this section will present tables and maps of segment-level, stop-level, and intersection-level metrics for the westbound evening peak period only. Tables and maps of results for the remaining direction-peak combinations are found in Sections 8.1 through 8.4 of the Appendix. Route-level metrics will be presented for all direction- peak combinations.

4.3.1 Average Operating Speed Average operating speeds were determined for all segments of each route for the four direction- peak combinations. The key assumption of the speed analysis is that the two stops forming the segment linearly follow the road driven by the bus. This assumption holds relatively true for most segments in this study, with exceptions including subway stations with bus loading areas. For this reason, station segments were discounted from the analysis. Table 4-2 displays the 5 segments with the lowest operating speeds along each route in the westbound evening peak period, named for their end stop, and Table 4-3 displays the 25 segments over all corridors with the lowest speeds. Figure 4-1 displays the average speed of all segments in the study scope during the evening peak period. Excluded from these results are segments between a near-side stop and a far-side stop of the same intersection, as these segments cover an intersection and nothing else, and thus speed values are not representative of those found in a segment-level analysis.

Table 4-2: Westbound segments with lowest operating speeds for each route in the evening peak period

Route Segment Speed Route Segment Speed (km/h) (km/h) 60 Steeles Keele St 6.73 53 Steeles Woodbine Ave 10.38 West Tandem Rd 7.45 East Don Mills Rd 14.04 Islington Ave 11.61 3160 Steeles Ave E 14.95 Highway 27 12.83 Kennedy Rd 15.77 Carpenter Rd 13.50 Townsend Rd 18.42 36 Finch Bathurst St 13.55 39 Finch East Don Mills Rd 11.41 West Greenview Ave 16.53 Skymark Dr 12.02 Alness St 17.31 Midland Ave 15.54 Ancona St 17.67 Warden Ave 16.62 Chesswood Dr 18.75 Birchmount Rd 16.68 84 Sheppard Bathurst St 8.86 85 Sheppard Midland Ave 9.37 West Jane St 12.08 East Kennedy Rd 10.90 Weston Rd 12.32 Birchmount Rd 12.17 Allen Rd 12.82 Doris Ave 12.88 1450 Sheppard Ave W 16.49 Bayview Ave 12.92 52 Lawrence Caledonia Rd 7.91 54 Lawrence Victoria Park Ave 8.20 West Avenue Rd 8.48 East Markham Rd 8.50 Shermount Ave 8.58 Pharmacy Ave 9.82 Brucewood Cres 9.72 Warden Ave 10.99 Dufferin St 10.21 Don Mills Rd 12.22

Table 4-3: Westbound segments with lowest operating speeds over all study routes in the evening peak period

Rank Segment Route Average Speed (km/h) 1 Keele St 60 Steeles West 6.73 2 Tandem Rd 60 Steeles West 7.45 3 Caledonia Rd 52 Lawrence West 7.91 4 Victoria Park Ave 54 Lawrence East 8.20 5 Avenue Rd 52 Lawrence West 8.48 6 Markham Rd 54 Lawrence East 8.50 7 Shermount Rd 52 Lawrence West 8.58 8 Bathurst St 84 Sheppard West 8.86 9 Midland Ave 85 Sheppard East 9.37 10 Brucewood Cres 52 Lawrence West 9.72 11 Pharmacy Ave 54 Lawrence East 9.82 12 Dufferin St 52 Lawrence West 10.21 13 Woodbine Ave 53 Steeles East 10.38 14 Kennedy Rd 85 Sheppard East 10.90 15 Warden Ave 54 Lawrence East 10.99 16 Don Mills Rd 39 Finch East 11.41 17 Islington Ave 60 Steeles West 11.61 18 Skymark Dr 39 Finch East 12.02 19 Jane St 84 Sheppard West 12.08 20 Birchmount Rd 85 Sheppard East 12.17 21 Don Mills Rd 54 Lawrence East 12.22 22 Weston Rd 84 Sheppard West 12.32 23 Goldberry Sq 54 Lawrence East 12.56 24 Kipling Ave 52 Lawrence West 12.61 25 Bolingbroke Rd 52 Lawrence West 12.77

Figure 4-1: Westbound evening peak period segment speeds mapped

For each route, many of the segments with the lowest operating speeds are segments at major intersections. It is understandable this is the case as these intersections yield high signal delays due to lower signal split for the corridor of study compared to minor intersections. Additionally, the time value used to calculate operating speed includes passenger service time, and intersections with major arterials are more likely to have bus routes of high ridership, causing longer dwell times for passengers wishing to transfer between routes, and in turn lowering the operating speeds. Some north-south streets commonly display low segment operating speeds between the four corridors of study, including Don Mills Road, Bathurst Street, and Jane Street. Each of these roads are served by high-frequency transit and continue northward into the Regional Municipality of York.

Occasionally, some multi-segment sections of a route consistently experience low speeds. Examples of these sections include the section between Pioneer Village Station and Highway 400 along Route 60 Steeles West, sections surrounding Highway 404 along Route 53 Steeles East and Route 85 Sheppard East, and the section surrounding Allen Road along Route 52 Lawrence West. The reasons for these low speeds can likely be attributed to long red signal times for through traffic causing long queues at these intersections. Buses also commonly experience more frequent red signals due to difficulty accessing near-side stops arising from long queues, as well as long passenger service times once the stop has been accessed. Additionally, when other upstream intersections are present in close proximity to major intersections, traffic volumes can reach the roadway capacity, and queues can propagate back rendering buses and other vehicles unable to cross these upstream intersections. As Table 4-3 shows, the two segments with the lowest speeds along all corridors are found next to one another along Steeles Avenue West: Keele Street, and Tandem Road, one segment upstream of Keele Street. While Tandem Road is not a major arterial, its proximity to Keele Street could explain its low speeds, as queues from Keele Street can extend back to the Tandem Road segment.

Figure 4-1 clearly shows Route 52 Lawrence West generally experiences lower operating speeds than other routes in the study scope, with most segments from Bathurst Street to Jane Street not reaching above an average operating speed of 20km/h in the evening peak period. Route 52 has the highest ridership of the eight routes of study, which suggests that overall more time is spent as passenger service time for this route than others. Additionally, most of this section of

Lawrence Avenue West consists of two through lanes despite the high transit ridership and traffic volumes. Many major intersections with long red signal phases and large traffic volumes turning onto Lawrence Avenue are found along this section. In particular, the traffic signal at Black Creek Drive has a cycle length of 144 seconds, with only 47 seconds dedicated towards through traffic of Lawrence Avenue West. Lawrence Avenue is also the closest of the four corridors to downtown Toronto and thus likely experiences greater traffic volumes along the major north-south cross streets. Additionally, its proximity to the parallel Eglinton Avenue West, which was under heavy construction at the time of data collection for the Crosstown LRT, may cause an increase in traffic volumes. For example, in the evening peak period, Bathurst Street had a northbound vehicle movement count of 2666 at Lawrence Avenue, and only 2313 at Sheppard Avenue, one corridor north. The proximity of Lawrence Avenue to downtown and to Eglinton Avenue could have an effect on the corridor’s overall speed and traffic volumes, which would in turn affect bus speeds.

Route 60 Steeles West and Route 54 Lawrence East also show lower speeds than other corridors. Steeles Avenue West is highly industrialized and is the origin and destination of many large trucks and other vehicles requiring more space on the road. Steeles Avenue is also the northern boundary of Toronto and is thus a gateway into the Regional Municipality of York. For this reason, Steeles Avenue is likely a highly trafficked road for commuters to and from other cities into Toronto. These factors could influence bus speeds and dwell times of passengers wishing to transfer to York Region Transit.

While Route 54 Lawrence East does not experience large multi-segment sections of low speeds, its major intersections, such as Victoria Park Avenue, Pharmacy Avenue, and Warden Avenue, display some of the lowest individual segment speeds across the study scope. Most of Lawrence Avenue East, including the section encompassing the above intersections, consists of three through lanes. Bus stops are found at the near side of each of the listed intersections in the rightmost through lane. No right turn lanes, which buses can use to jump queues of through- traffic, are found at these intersections. For buses to access these near-side stops, they must queue behind mixed through-traffic and right turning traffic. This type of configurations often leads to buses requiring an extra traffic signal to cross the intersection due to difficulty accessing

36 the stops and long dwell times. Thus, lower operating speeds at intersections of these configurations can be explained.

Direction of travel also has an effect on operating speeds. While the above tables and maps display westbound bus trips, those for eastbound evening peak trips, found in Section 8.2 of the Appendix, show lower operating speeds for east routes, as buses carry passengers away from Yonge Street and Line 1 towards the suburban areas of Scarborough.

4.3.2 On-Time Performance The TTC defines various service standards it aims to achieve for its transit performance. One such standard is on-time performance (OTP). The TTC’s service standard states that 60% of surface transit trips should be considered on-time. The definition of on-time varies with the scheduled headway of a route, with higher-frequency routes operating on a headway basis rather than a schedule basis. Table 4-4 shows the definitions of what the TTC considers to be an on- time trip.

Table 4-4: TTC's OTP definitions for surface transit (Toronto Transit Commission, 2017)

Headway TTC OTP Definition Less than 5 minutes Within 75% of scheduled headway Between 5 and 10 minutes (inclusive) Within 50% of scheduled headway Greater than 10 minutes Between 5 minutes late and 1 minute early

From AVL data, trips were determined to be on-time or not on-time at each timing point. Timing points were noted in the stop sequence lists provided by the City via Parsons. Figure 4-2 displays whether this service standard was met for each westbound timing point in the evening peak period. The definition of what constitutes an on-time trip is noted by the shape of the point on the map, and the colour displays whether at least 60% of the trips in the AVL data achieved OTP, and thus maintained the TTC’s service standard. Table 4-5 displays the proportion of trips that are considered on time for each timing point sequentially along each route, with those failing the TTC service standard written in red. Tables and maps of the remaining direction-peak combinations can be found in Section 8.3 of the Appendix.

Figure 4-2: Westbound timing points displaying on-time performance in the evening peak period It should be noted that for Route 53 Steeles East, three timing points (Old Kennedy Road, Leslie Street, and Yonge Street) have different headways than the others due to the express branches of the route not serving these stops.

Table 4-5: On-time performance of westbound timing points in the evening peak period

Timing Timing Point Combined OTP Definition % Trips Sequence Headway On-Time (min) 60 Steeles West 1 Yonge St 3 Within 75% of headway 65.9 2 Bathurst St 3 Within 75% of headway 63.7 3 Dufferin St 3 Within 75% of headway 62.1 4 Keele St 3 Within 75% of headway 54.2 5 Pioneer Village Stn 7.5 Within 50% of headway 33.6 6 Jane St 7.5 Within 50% of headway 36.8 7 Weston Rd 7.5 Within 50% of headway 32.2 8 Islington Ave 7.5 Within 50% of headway 29.8 9 Kipling Ave 7.5 Within 50% of headway 39.3 53 Steeles East 1 Middlefield Rd 3.08 Within 75% of headway 65.2 2 McCowan Rd 3.08 Within 75% of headway 63.6 3 Old Kennedy Rd 5.25 Within 50% of headway 49.1 4 Victoria Park Ave 3.08 Within 75% of headway 56.3 5 Woodbine Ave 3.08 Within 75% of headway 57.2 6 Don Mills Rd 3.08 Within 75% of headway 57.5 7 Leslie St 5.25 Within 50% of headway 40.4 8 Bayview Ave 3.08 Within 75% of headway 59.0 9 Yonge St 5.25 Within 50% of headway 38.4 36 Finch West 1 Finch Stn 6.75 Within 50% of headway 48.7 2 Bathurst St 6.75 Within 50% of headway 47.7 3 Dufferin St 6.75 Within 50% of headway 42.8 39 Finch East 1 Neilson Rd 14 5 min late to 1 min early 75.8 2 Sandhurst Cir E 7 Within 50% of headway 63.3 3 McCowan Rd 7 Within 50% of headway 61.6 4 Midland Ave 7 Within 50% of headway 55.6 5 Birchmount Rd 7 Within 50% of headway 47.7 6 Warden Ave 7 Within 50% of headway 43.9 7 Victoria Park Ave 7 Within 50% of headway 29.8 8 Don Mills Rd 4.67 Within 75% of headway 66.1 9 Bayview Ave 4.67 Within 75% of headway 56.3 84 Sheppard West

1 Bathurst St 3.65 Within 50% of headway 65.1 2 Sheppard West Stn 3.65 Within 50% of headway 31.6 3 Keele St 5.75 Within 75% of headway 41.2 4 Jane St 5.75 Within 75% of headway 39.8 85 Sheppard East (Meadowvale to Don Mills) 1 Morningside Ave 6.25 Within 50% of headway 60.0 2 Malvern St 6.25 Within 50% of headway 50.0 3 Markham Rd 6.25 Within 50% of headway 48.6 4 Scunthorpe Rd 6.25 Within 50% of headway 54.3 5 McCowan Rd 6.25 Within 50% of headway 57.0 6 Brimley Rd 6.25 Within 50% of headway 63.8 7 Midland Ave 6.25 Within 50% of headway 66.0 8 Victoria Park Ave 6.25 Within 50% of headway 60.4 85J Sheppard East (Don Mills to Yonge) 1 Don Mills Rd 16 5 min late to 1 min early 70.1 2 Bayview Ave 16 5 min late to 1 min early 47.8 52 Lawrence West 1 Dufferin St 2.92 Within 75% of headway 47.7 2 Keele St 2.92 Within 75% of headway 49.2 3 Culford Rd 2.92 Within 75% of headway 43.8 4 Jane St 2.92 Within 75% of headway 51.1 5 Weston Rd 2.92 Within 75% of headway 49.2 6 Islington Ave 5.92 Within 50% of headway 26.4 7 Kipling Ave 5.92 Within 50% of headway 23.9 54 Lawrence East 1 Starspray Loop 5.3 Within 50% of headway 44.4 2 Morningside Ave 5.3 Within 50% of headway 33.1 3 Kingston Rd 5.3 Within 50% of headway 33.3 4 Scarborough Golf Club Rd 3.5 Within 75% of headway 36.2 5 Markham Rd 3.5 Within 75% of headway 48.0 6 McCowan Rd 3.5 Within 75% of headway 53.6 7 Kennedy Rd 5.13 Within 50% of headway 34.6 8 Birchmount Rd 5.13 Within 50% of headway 33.4 9 Victoria Park Ave 5.13 Within 50% of headway 29.8 10 Don Mills Rd 5.13 Within 50% of headway 31.0

Not many timing points achieve the TTC’s OTP service standard for the evening peak period in the westbound direction. Over all trips studied, three routes (36 Finch West, 52 Lawrence West,

40 and 54 Lawrence East) do not meet the service standard at a single timing point, with the remaining routes only meeting the service standard at some of the timing points. As shown in Table 4-5, it is common for the number of on-time trips to decrease at timing points downstream along the route. This is likely due to highly variable traffic conditions on a route that cannot be predicted in scheduling. Additionally, bunching becomes more prevalent farther downstream on a route (Fonzone, Schmocker, & Liu, 2015). Bunching occurs when a bus falls slightly behind schedule, causing more passengers to arrive at downstream stops, lengthening dwell times, and further slowing the bus down. Due to the delay of the bus, passengers are being picked up later than scheduled, which causes the following bus to have lighter passenger loads than expected. The uneven distribution of passengers on consecutive buses causes them to catch up to one another, or “bunch”. Interestingly, the opposite appears to be true for Route 85 Sheppard East, as the OTP standard is met more consistently downstream in its route. This could be due to bus holding, where drivers try to maintain headway regularity by holding buses that are ahead of schedule at stops.

OTP is typically achieved more easily when the definition of on-time is broader. This is evident for stops of bus headway less than 5 minutes, as a bus can arrive between 25% and 175% of its scheduled headway and still be considered on-time. For example, Route 39 Finch East has better OTP for its final two timing points, Don Mills Road and Bayview Avenue, as the addition of the 39C branch increases the headway and relaxes the on-time definition.

4.3.3 Running Time Variation The final performance metric determined from AVL data was running time variation. A trip’s running time variation was measured as the running time of the trip from start to end divided by the scheduled running time of the trip. A value of 1 indicates the running time of the trip is the same as the scheduled running time, and thus is ideal for good transit reliability. Values less than 1 show that trips are quicker than scheduled, and greater than 1 show that trips are longer than scheduled. Figure 4-3 displays the route-level results for each direction-peak combination of all routes in the form of box-and-whisker plots. The median running time value is shown as the line in the middle of the box, with the box’s edges representing the 25th and 75th percentiles of the data. The whiskers span the 5th and 95th percentiles of all runs. Table 4-6 displays the mean running time of the routes in each direction-peak combination. Route 85J, serving Sheppard

Avenue East between Sheppard-Yonge and Don Mills Stations, was displayed as a separate route as there is no overlap between it and the other branches of Route 85.

Figure 4-3: Running time variation distributions for all routes, directions, peak periods

Table 4-6: Mean running time variations for all routes, directions, peak periods

Route Mean Running Time as Proportion of Scheduled Eastbound Westbound AM PM AM PM 60 Steeles West 0.982 0.917 0.883 0.992 53 Steeles East 0.875 0.928 1.004 0.933 36 Finch West 0.855 1.089 0.858 0.792 39 Finch East 0.932 1.013 0.971 1.001 84 Sheppard West 0.895 0.919 0.927 0.978 85 Sheppard East 0.903 0.929 0.976 0.937 (Meadowvale to Don Mills) 85J Sheppard East 0.816 0.861 0.865 0.847 (Don Mills to Yonge) 52 Lawrence West 1.010 0.998 1.014 1.008 54 Lawrence East 1.053 1.018 1.042 1.015

Overall, it appears that buses running more quickly than scheduled are more common than those running behind schedule. Bus schedules could be designed while considering slack time for delays experienced along the route. In uncongested conditions, buses would run faster than scheduled. This is evidenced by Route 52 Lawrence West, which was previously shown to display low operating speeds in the westbound direction and evening peak period. The average running time is only 0.8% higher than the scheduled running time according to the AVL data. Additionally, Routes 60 Steeles West and 54 Lawrence East, which also showed low speeds, are near their scheduled westbound evening running times at 0.8% lower and 1.5% higher respectively. Other routes with higher speeds, such as 36 Finch West, 85 Sheppard East, and 53 Steeles East show on average much quicker trips than scheduled, suggesting scheduled running times are based around poor transit conditions.

Routes 85J and 36, the two shortest routes in the study scope, display considerably lower running times than scheduled, with the exception of eastbound evening runs of Route 36. The box-and- whisker plots show high variability for these routes compared to others. Because the scheduled running time would be lower for shorter routes, variability of running time would likely be more common as a small amount of time difference carries more significance for these routes than longer routes.

Evening runs, particularly in the eastbound direction, show highly variable running times for Route 60 Steeles West. It is shown to be common for runs to be much quicker than scheduled, with 20% of its runs completing their trips between 50% and 90% of the scheduled running time. As discussed, this route experiences high amounts of traffic from large vehicles, requiring more roadway space than typical vehicles. This variability arise from the operating hours of these business and the driving schedules of these large vehicles. While the scheduled running time is likely designed to account for considerable delays, some evening trips could experience much lower roadway congestion and delays than others.

Routes 53 Steeles East, 39 Finch East, and 85 Sheppard East appear to be the least variable of those studied. All are routes east of Yonge Street, operating mostly in Scarborough. This could indicate that traffic patterns in this part of Toronto are more regular and predictable. One reason could be due to there being fewer connecting expressways in Toronto’s east (Highway 404) than in Toronto’s west (Highways 400 and 427, and Allen Road), which could reduce fluctuations in traffic volumes. Also, the road layout in Toronto’s east better resembles a grid than in the west, which could allow for greater traffic flow control through signal coordination.

As with OTP, bus holding strategies could help stabilize running times and maintain bus schedules, mainly for trips that are running much more quickly than scheduled. While passengers may not negatively perceive fast bus trips, these trips can still be a mark of unreliability in a transit system.

4.3.4 Signal Delay The first metric determined from the contractor-collected data was the average signal delay. This measurement approximates the amount of time a bus waits at a red signal, with passenger service time subtracted. Because dwell time must be considered, both GPS and event data are required for a run to calculate signal delay. Certain direction-peak period combinations of some routes lack sufficient event data, and thus signal delay cannot be accurately measured for some signals. In particular, relatively few runs of Routes 36 Finch West and 85 Sheppard East had event data recorded, and thus the values for signals along these routes are more approximate than others. In this section, only signals with at least 5 recorded signal delay measurements for westbound runs in the evening peak period are reported. Table 4-7 displays the 5 signals for each route with the greatest average signal delay for westbound evening runs. Table 4-8 displays the 25 signals over

44 all routes with the greatest signal delay, and Figure 4-4 maps the average signal delay for signals with at least 5 recorded measured values. Note that due to contractor data limitations, Sheppard Avenue East between Yonge Street and Don Mills Road does not have signal delays reported. Tables and maps of the remaining direction-peak combinations can be found in Section 8.4 of the Appendix.

Table 4-7: Westbound signalized approaches with highest signal delays for each route in the evening peak period

Route Signal Delay Route Signal Delay (sec) (sec) 60 Steeles Keele St 54.5 53 Steeles Woodbine Ave 44.1 West Dufferin St 53.0 East Kennedy Rd 41.5 Islington Ave 40.4 Hwy 404 SB Ramp 32.6 Jane St 39.1 Warden Ave 32.3 New Westminster Dr 33.6 McCowan Rd 32.0 36 Finch Bathurst St 21.5 39 Finch Don Mills Rd 48.9 West Alness St 8.7 East Midland Ave 44.1 Virgilwood Dr 8.4 Warden Ave 43.0 Tangiers Rd 7.6 McCowan Rd 40.4 Chesswood Dr 7.2 Seneca Hill Dr 32.9 84 Sheppard Bathurst St 56.2 85 Sheppard McCowan Rd 50.3 West Allen Rd 44.2 East Markham Rd 39.5 Jane St 31.1 Neilson Rd 37.1 Wilson Heights Blvd 20.5 Midland Ave 34.8 Magellan Dr 19.0 Kennedy Rd 32.4 52 Lawrence Allen Rd SB Ramp 99.0 54 Lawrence Warden Ave 93.2 West Jane St 63.4 East Don Mills Rd 84.3 Avenue Rd 60.3 Birchmount Rd 66.4 Caledonia Rd 53.8 Victoria Park Ave 66.1 Keele St 51.3 Pharmacy Ave 60.8

Table 4-8: Westbound signalized approaches with highest signal delays over all study routes in the evening peak period

Rank Signal Route Average Signal Delay (sec) 1 Allen Rd SB Ramp 52 Lawrence West 99.0 2 Warden Ave 54 Lawrence East 93.2 3 Don Mills Rd 54 Lawrence East 84.3 4 Birchmount Rd 54 Lawrence East 66.4 5 Victoria Park Ave 54 Lawrence East 66.1 6 Jane St 52 Lawrence West 63.4 7 Pharmacy Ave 54 Lawrence East 60.8 8 Avenue Rd 52 Lawrence West 60.3 9 Kingston Rd 54 Lawrence East 59.3 10 Bathurst St 84 Sheppard West 56.2 11 Keele St 60 Steeles West 54.5 12 Caledonia Rd 52 Lawrence West 53.8 13 Dufferin St 60 Steeles West 53.0 14 Brimley Rd 54 Lawrence East 51.6 15 Keele St 52 Lawrence West 51.3 16 Black Creek Dr 52 Lawrence West 50.5 17 Midland Ave 54 Lawrence East 50.3 18 McCowan Rd 85 Sheppard East 50.3 19 McCowan Rd 54 Lawrence East 48.9 20 Don Mills Rd 39 Finch East 48.9 21 Markham Rd 54 Lawrence East 47.0 22 Weston Rd 52 Lawrence West 46.3 23 Morningside Ave 54 Lawrence East 45.7 24 Allen Rd 84 Sheppard West 44.2 25 Woodbine Ave 53 Steeles East 44.1

Figure 4-4: Westbound evening peak period signal delays mapped

Many of the greatest signal delays are found from Routes 52 Lawrence West and 54 Lawrence East, respectively with 7 and 11 of the 25 highest signal delay values. This likely indicates that the current signal plans do not favour Lawrence Avenue appropriately given its traffic and bus volumes. One possibility is that traffic volumes may have increased along Lawrence Avenue due to construction along parallel Eglinton Avenue for the Crosstown LRT occurring during the time of data collection. Because of this, Lawrence Avenue, particularly in the east, could possibly be a candidate for adjustment of signal timing plans.

Transit signal priority (TSP), while increasingly common in Toronto, currently only exists to favour signals along Route 36 Finch West among the eight routes of study. As of June 2018, 6 signals along Finch Avenue West between Yonge and Keele Streets (Greenview Avenue, Talbot Road, Edithvale Drive, Torresdale Avenue, 300 metres west of Torresdale Avenue, and Wilmington Avenue) had TSP enabled. It can be seen that Route 36 has considerably lower signal delays than the other routes, indicating TSP can be a beneficial tool for reducing signal delay.

Many north-south roads commonly share long signal delays between corridors, including McCowan Road, Warden Avenue, Keele Street, and Jane Street. These four roads are major arterials traveling between Toronto and the Regional Municipality of York and have frequent transit operations. Any signal timing adjustments in favour of the east-west corridors of study could have negative effects for transit riders on these north-south corridors.

There are some drawbacks to the methodology employed for signal delay. Firstly, the sample sizes are quite small and rely on subjective event data collected by the contractor. This methodology assumes that the contractor recorded all “door open” and “door close” events with complete accuracy, and that all delay unaccounted for in dwell time is due to signal delay. There is no way to account for the occasional driver who keeps bus doors open throughout a red signal despite no passenger activity. Bus holding or driver changes are rarely recorded in event data, despite likely occurring more often than the data reflects. By ignoring these types of events, signal delays would be positively inflated beyond their actual values. One example is the signal at Steeles Avenue East and Middlefield Road. Some event data suggests that this is a common place for drivers to change. However, “driver change” events were rarely recorded and the signal delay values at this intersection were measured to be much higher than most other signals along

Route 53 Steeles East, particularly for eastbound evening runs and westbound morning runs (Section 8.4 of the Appendix).

Another drawback is the assumption of a queue length in the buffer. Buffer sizes were set to be not too big to capture delays that are not signal-related, and not too small to miss delays that are signal-related. It cannot be stated with full accuracy how large a buffer should be set for each signal, as conditions change depending on location and time of day. Additionally, there is no way to consider the far side of an intersection at full capacity, where buses are unable to cross during a green signal. While this type of occurrence would be considered a delay, it should not be considered signal delay. Nevertheless, it is still a good methodology for approximation and visualization of which signals yield greater delays than others.

4.3.5 Delay Analysis Delay analysis was conducted at both the route and the segment level. Route-level delay analysis examines the average and variation of delay duration per kilometre of each run, considering the specific branch of each run. Segment-level delay analysis considers the average delay at each segment similar to the operating speed analysis. Recall that a delay represents the duration of time that a bus is running below 5km/h.

Figure 4-5 displays box-and-whisker plots representing route level delay for all runs during the four direction-peak combinations. The centre line of the box represents the median run delay, while the edges of the box represent the 25th and 75th percentiles. The whiskers extend the entirety of the data range from greatest to least delay. Table 4-9 displays the mean delay for each run during each direction-peak period combination. Note that since this data is derived from the contractor’s data, Route 85J between Yonge Street and Don Mills Road is not considered.

Figure 4-5: Distribution of route-level delays for all routes, directions, peak periods

Table 4-9: Mean route-level delays for all routes, directions, peak periods

Route Mean Route Run Delay (min/km) Eastbound Westbound AM PM AM PM 60 Steeles West 1.37 1.73 1.18 2.15 53 Steeles East 0.99 1.02 1.18 0.85 36 Finch West 0.70 0.92 0.46 1.48 39 Finch East 0.78 1.29 1.01 1.05 84 Sheppard West 0.93 1.15 1.01 1.28 85 Sheppard East 0.83 1.22 1.00 1.09 52 Lawrence West 2.09 1.94 1.21 1.76 54 Lawrence East 1.08 1.70 1.42 1.61

Consistent with data shown from average operating speed, Routes 52 Lawrence West experiences the greatest amount of per-kilometre delay in the eastbound direction and among the highest in the westbound direction. During both morning and evening peak periods, the average eastbound Route 52 run experiences approximately 2 minutes of delay time per kilometre of the route. While some of this delay time is due to dwell times, much of it is likely due to congestion and signal delay from sources including Black Creek Drive, Allen Road, and other major arterials such as Bathurst Street, Keele Street, Jane Street, and Weston Road. Eastbound evening runs of Route 52 also show high variability in delay, with runs extending from almost no delay to around 5.5 minutes per kilometre of route. With Black Creek Drive serving as access to Highway 400, and Allen Road being a busy expressway, fluctuations in their traffic patterns can have great effects on Lawrence Avenue’s traffic volumes. General traffic volumes are a prevailing indicator of congestion and overall mixed right-of-way surface transit performance.

Route 60 Steeles West westbound evening runs experience the highest average delay of all runs within the study scope, at 2 minutes and 9 seconds per kilometre of route. From average speed data, the two westbound evening segments with the lowest speed are Keele Street and Tandem Road, located consecutively on Route 60 Steeles West. The operating speed map (Figure 4-1) shows various other locations of low speeds, including Bathurst Street, Jane Street, Weston Road, and Highway 27, which suggests high delays are rampant along this route. Additionally, as the boundary to Vaughan as well as an access point to Mississauga and Brampton, traffic volumes are likely high along this corridor. Route 60 also experiences great fluctuations in delay in the westbound evening peak period, with individual observed delays ranging from 0.5 minutes to over 4 minutes per kilometre of route. As previously noted, this route experiences congestion from trucks and other large vehicles due to the corridor’s heavy industrialization. Variations in traffic from these types of vehicles can have a larger effect on congestion levels due to their size and space occupied on the road.

Route 54 Lawrence East shows consistently fairly high delays, with the exception of eastbound morning runs, away from Yonge Street. The number of signals with high signal delays along Route 54 found in Table 4-8 suggests that much of these delays may arise from signal delay.

While Route 39 Finch East does not experience particularly high delays on average, eastbound evening runs have considerable variability. Much of the delays likely arise from traffic flow from

Highway 404. Finch Avenue East at the Highway 404 off-ramps consists only of two lanes in each direction, which may not be enough to appropriately serve this influx of traffic, and thus can generate delays.

Table 4-10 shows the 5 segments for each route with the greatest delays for westbound evening runs, and Table 4-11 shows the 25 segments overall in the study scope with the greatest average delay. Figure 4-6 displays the average westbound evening delay on the segment level for all segments in the study scope. Tables and maps of the remaining direction-peak combinations can be found in Section 8.5 of the Appendix.

Table 4-10: Westbound segments with highest delays for each route in the evening peak period

Route Segment Delay Route Segment Delay (min/km) (min/km) 60 Steeles 1900 Steeles Ave W 5.91 53 Steeles Woodbine Ave 4.22 West Keele St 5.43 East Don Mills Rd 3.88 Islington Ave 4.44 Kennedy Rd 2.85 Jane St 4.41 Warden Ave 2.03 Tandem Rd 3.94 Middlefield Rd 1.61 36 Finch Arnott Ave 3.75 39 Finch East Don Mills Rd 5.18 West Torresdale Ave 2.95 Skymark Dr 3.66 Bathurst St 2.90 Warden Ave 3.39 Virgilwood Dr 2.51 Midland Ave 2.91 Ancona St 2.41 Pharmacy Ave 2.65 84 Sheppard Bathurst St 5.53 85 Sheppard Midland Ave 3.81 West Jane St 4.65 East Scunthorpe Rd 3.12 Magellan Dr 4.19 Kennedy Rd 2.49 Allen Rd 2.57 Morningside Ave 2.48 Sentinel Rd 2.02 Brimley Rd 2.47 52 Lawrence Avenue Rd 6.38 54 Lawrence Don Mills Rd 7.86 West Dufferin St 5.57 East Victoria Park Ave 7.23 Shermount Ave 4.83 Markham Rd 7.01 Jane St 4.54 Pharmacy Ave 5.98 Caledonia Rd 4.24 Warden Ave 5.22

Table 4-11: Westbound segments with highest delays over all study routes in the evening peak period

Rank Segment Route Average Delay (min/km) 1 Don Mills Rd 54 Lawrence East 7.86 2 Victoria Park Ave 54 Lawrence East 7.23 3 Markham Rd 54 Lawrence East 7.01 4 Avenue Rd 52 Lawrence West 6.38 5 Pharmacy Ave 54 Lawrence East 5.98 6 1900 Steeles Ave W 60 Steeles West 5.91 7 Dufferin St 52 Lawrence West 5.57 8 Bathurst St 84 Sheppard West 5.53 9 Keele St 60 Steeles West 5.43 10 Warden Ave 54 Lawrence East 5.22 11 Don Mills Rd 39 Finch East 5.18 12 Shermount Ave 52 Lawrence West 4.83 13 Jane St 84 Sheppard West 4.65 14 Jane St 52 Lawrence West 4.54 15 Islington Ave 60 Steeles West 4.44 16 Jane St 60 Steeles West 4.41 17 Caledonia Rd 52 Lawrence West 4.24 18 Woodbine Ave 53 Steeles East 4.22 19 Magellan Dr 84 Sheppard West 4.19 20 Weston Rd 52 Lawrence West 3.95 21 Tandem Rd 60 Steeles West 3.94 22 Don Mills Rd 53 Steeles East 3.88 23 Brucewood Cres 52 Lawrence West 3.84 24 Midland Ave 85 Sheppard East 3.81 25 Islington Ave 52 Lawrence West 3.76

Figure 4-6: Westbound evening peak period segment-level delays mapped

Similar to previous results, many of the most poorly performing segments in the study scope are found on Routes 52 Lawrence West, 54 Lawrence East, and 60 Steeles West. The section of Lawrence Avenue West between Bathurst Street and Keele Street almost exclusively experiences delay of at least 2 minutes on average per kilometre of route. Additional considerable average segment delays are found on Route 84 Sheppard West, with Bathurst Street and Jane Street segments showing high delays, as well as the segment immediately east of Jane Street, Magellan Drive. While somewhat evident in operating speed and signal delay analyses, the delay analysis clearly shows north-south corridors whose segments along the study corridors consistently are high in delay. Examples of cross-corridor segments yielding high delay include Jane Street, Bathurst Street, Don Mills Road, Warden Avenue, and Kennedy Road. Due to the nature of these busy thoroughfares with high-frequency transit, delays due to dwell times, queues, and red signals are expected.

While the results from the delay and signal delay analyses are not fully independent on account of using the same GPS points from the contractor data, the delay analysis is useful to show areas where congestion delay not at an intersection may occur and how far back delays may propagate. Multi-segment sections of high delay are found on many corridors including Lawrence Avenue West, Finch Avenue West, and Steeles Avenue West. The results from the segment-level delay analysis, signal delay analysis, and operating speed analysis will be the basis of Chapter 5.

5 Analysis of Factors Affecting Transit Performance

This chapter will explore clustering and regression models of unidirectional (i.e. eastbound and westbound as independent data points) intersection approaches to determine the feature differences between well and poorly performing locations of the bus routes. Together, clustering and regression portray a more complete picture of the intersections. As an unsupervised machine learning technique, clustering yields more qualitative results with greater interpretive power. Regression analyses, which are supervised machine learning processes, are strong due to quantitatively showing the effects of features on target variables. The six target variables that will be studied are the average operating speed and delay on the segment level, and signal delay on the intersection level for the morning and evening peak periods, all determined from Chapter 4. Most intersection approaches have data for all six variables, however some of these values may be missing for some locations due to the data limitations discussed in Chapter 4. Throughout this section, scikit-learn, a Python machine learning library, is frequently used.

5.1 Data 5.1.1 Geometric Configurations Google Satellite was used to determine the geometric configurations of each eastbound and westbound signalized intersection approach within the scope of study. The noted features include the number of through lanes, existence of left turn lanes, existence and length (in metres) of right turn lanes which can act as a queue-jump lane, existence of channelized right turn lanes, and existence of far-side receiving bus bays. These values were matched to the PX value of each intersection’s signal.

5.1.2 Signal Timing Plans (STP) STP information for many of the signalized intersections was provided by the City via Parsons. These plans illustrate the various traffic signal phases that occur during a signal’s cycle as well as their duration during different times of day, pre-emption, and priority algorithms. The plans also contain information of amber and all-red signal phases as well as the times for crossing, flashing don’t walk, and don’t walk phases for pedestrians.

5.1.3 Turning Movement Count (TMC) TMC information for various intersections within the scope of study was provided by the City via Parsons. These forms display traffic counts for left turning, right turning, and through moving vehicles by time of day and direction. Each of these datasets was counted on a single weekday sometime in the period of July 2008 to April 2018.

5.2 Data Frame Construction Unidirectional signalized intersection approaches represent individual data points of the clustering and regression analyses. Each intersection approach is assigned a unique ID value, denoted by the traffic signal’s PX value and the direction of study (e.g. 1191-EB represents the eastbound approach of PX 1191, Steeles Avenue East and Warden Avenue). Using Python’s Tika text extraction package, STP and TMC values were scraped and matched to their respective intersection approach ID value. Table 5-1 displays the fields used in the data frame.

Table 5-1: Features used in the data frame for this chapter

Field Description ID Unique value for each intersection approach (PX-Direction) Thru Number of through lanes on the near side of the approach Left Turn Existence of a left turn lane (1=yes, 0=no) QJ Right Existence of a designated right turn lane branching off an upstream through lane (1=yes, 0=no) QJ Length Length in metres of the QJ Right (0 if QJ Right = 0) Right from Thru Existence of a through lane which becomes a right turn lane at the intersection approach (1=yes, 0=no) Chan Right Existence of a channelized right turn (1=yes, 0=no) Receiving Bay Existence of a far-side receiving bus bay (1=yes, 0=no) Stop Near Only near-side bus stop at approach (1=yes, 0=no) Stop Far Only far-side bus stop at approach (1=yes, 0=no) Stop Both Both near-side and far-side bus stops at approach (1=yes, 0=no) Stop None No bus stop at approach (1=yes, 0=no) TMC Left AM Aggregated left TMC count for AM peak period TMC Right AM Aggregated right TMC count for AM peak period TMC Thru AM Aggregated through TMC count for AM peak period TMC Left PM Aggregated left TMC count for PM peak period TMC Right PM Aggregated right TMC count for PM peak period TMC Thru PM Aggregated through TMC count for PM peak period Sig Time AM Time in seconds of green and amber through phases for AM peak period Split AM Proportion of cycle in direction of corridor of study for AM peak period Cycle Length AM Time in seconds of traffic signal cycle for AM peak period Sig Time PM Time in seconds of green and amber through phases for PM peak period Split PM Proportion of cycle in direction of corridor of study for PM peak period Cycle Length PM Time in seconds of traffic signal cycle for PM peak period

It should be noted that due to the dependent relationship between green signal time, split, and cycle length, only two of these (signal time and split) are used in the clustering process and regression models. Cycle lengths are still reported in the clustering results. Only intersections for which both STP and TMC data were available were included in this analysis. Overall, data for 100 intersections was available, yielding 200 intersection approaches (eastbound and westbound approaches at each intersection). Table 5-2 shows the number of intersection approaches with all specified available data for each corridor of study. Note that TMC data was not available for

58 intersections along Finch Avenue West, and thus no intersection approaches from that portion of the Finch corridor were studied in this section. Similarly, no TMC or STP data were available for the Dixon Road portion of the Lawrence corridor. Figure 5-1 displays all intersection approaches in this section of the analysis. Using the PX-Stop ID list generated in Chapter 4, operating speed and segment-level delay, originally associated with Stop ID values, were linked to an intersection’s PX value and thus each intersection approaches were joined with values for the six target variables.

Table 5-2: Number of intersection approaches on each corridor for this chapter’s analysis

Steeles Ave Finch Ave Sheppard Ave Lawrence Ave West East Total West East Total West East Total West East Total 30 28 58 0 40 40 16 28 44 22 36 58

Figure 5-1: Locations of intersection approaches studied in this chapter

5.3 Modeling Methodology 5.3.1 Clustering In general, clustering is the process of grouping data points within a dataset with other points of similar features. Clustering is considered unsupervised machine learning, as there is no target variable the algorithm is training to predict. Typical clustering algorithms involve mapping each row of data onto an m-dimensional set of coordinates, with m representing the number of features in the dataset, and calculating Euclidean distances between the points. K-means clustering was selected as the algorithm for clustering the 200 intersection approaches. K represents the number of desired clusters for the dataset and is the only input parameter for this algorithm. K-means was selected over other clustering algorithms due to its versatility without being restricted by other parameters, such as with density-based algorithms requiring maximum point distances and minimum cluster sizes. K cluster centres on the m-dimensional coordinate space are randomly generated. All data points are then assigned to their nearest cluster centre, forming clusters, and the geometric centres of these clusters are computed as the new cluster centres. This process repeats until the clusters do not change between iterations.

Prior to clustering, the data must be standardized as to ensure equal importance of each feature in the data frame, yielding a mean of 0 and a standard deviation of 1 for each column. Standardization is represented by the following formula.

푥 − 휇 푥 = 0 1 휎

Here, x1 represents the standardized value, x0 represents the original value, µ represents the original feature mean and σ represents the original feature standard deviation. Standardization was completed using scikit-learn’s Preprocessing Standard Scaler function.

Without a clear indication of the number of clusters in the analysis, K must be determined analytically. The elbow method was used, where the desired K-value is defined as the point where the marginal increase of explained (inter-cluster) variance is negligible with the addition of one more cluster (Kodinariya & Makwana, 2013). Scikit-learn’s KMeans function was used to test various K-values and the explained and unexplained variance values were calculated to determine the desired K-value. With this K-value selected, the data was then re-run using the KMeans function. Due to the random nature of cluster centre initialization, the clustering results

61 vary slightly with each run. Thus, the KMeans function was run 100 times, and the number of times any two data points were found in the same cluster was noted in a matrix. Scikit-learn’s AgglomerativeClustering function was then applied to this matrix, sorting the data into K clusters based on the frequency each data point was clustered with each other data point. This process ensures a more consistent and robust clustering process.

5.3.2 Regression Unlike clustering, regressions are a type of supervised machine learning as they attempt to predict a target value. Two types of regressions were performed in this study: ordinary least squares and regression trees. For both types of regression, time-specific features (traffic movement counts and signal times and splits) are only used as features for target variables of their corresponding peak period (e.g. AM Split is not used for PM operating speed).

5.3.2.1 Ordinary Least Squares

Ordinary Least Squares (OLS) is an algorithm through which linear regression is performed. Regression analysis aims to numerically describe a target variable, through an equation, based on its associated features. Generally, regressions are performed by minimizing a variance function, or cost function, showing that variation in target values can be described by their associated features. Each feature included in the regression analysis is assigned a corresponding coefficient value to which the feature value is multiplied. These coefficients are found by minimizing the OLS cost function. Due to its quadratic nature, minimizing the OLS cost function is closed form and can be done analytically. The following equation shows the vectorized (matrix form) solution to finding the coefficients through OLS.

훽̂ = (푋푇푋)−1푋푇푦

Here, X represents the feature matrix of dimensions n (the number of data points) by m (the number of features) and y represents the target matrix of dimensions n by 1. The coefficient matrix β is found to be of dimensions 1 by m.

Python’s statsmodels.api was used as the OLS algorithm for this study. The data frame was used as the feature matrix for each of the six target variables. R2 values were determined for each regression and the statistically significant parameters (through a two-tailed t-test at the 95% confidence level) were noted.

5.3.2.2 Regression Trees

Trees are another data science method to separate data points into groups. They can be applied to categorical data, where algorithms are trained to predict a data point’s target class based on following a series of binary decisions regarding the point’s features. The physical structure of these binary decisions resembles a tree, where the final sorted class is referred to as a leaf. Each binary decision is referred to as a node. Trees can also be applied to discrete and continuous data, where they are referred to as decision tree regressors, or regression trees. Rather than predicting a point’s target class, regression trees attempt to sort data into leaves of distinct average values of the target variable. Like clustering, the goal of the process is to minimize the intra-group variance and maximize the inter-group variance. This study will create separate decision trees for the six target variables (AM and PM operating speed, segment-level delay, and signal delay). Trees were studied since they are able to capture conditional observations which may not be modelled through OLS.

A number of input parameters can be specified for trees, and this study worked with two in particular: the minimum number of samples required in a leaf, and the maximum depth, or number of consecutive nodes, of the tree. Without any parameter specification, trees can become excessively large as they sort many non-identical data points into individual leaves of size 1. To determine the optimal parameter inputs for each target variable, a grid search with 5-fold cross- validation was conducted using scikit-learn’s GridSearchCV function. Grid searching is the process of running the algorithm with every combination of pre-specified parameter values. K- fold cross-validation splits the data into K mutually exclusive and collectively exhaustive training-testing sets. Each training set forms the model where it is then applied to the testing set and the accuracy score is calculated. The average of the K testing scores is then reported. The grid search with cross-validation process was used to determine the parameter set yielding the greatest testing accuracy. Following the determination of the optimal parameters for each target variable, regression trees with these parameters as inputs were created for each using scikit- learn’s DecisionTreeRegressor function.

5.4 Results and Discussion 5.4.1 Clustering 5.4.1.1 Determination of Clusters

Using K-values ranging from 1 to 39, the data frame was clustered and the inter-cluster variance as a percentage of total variance was determined for each value, as described by the elbow method. An elbow curve was plotted (Figure 5-2) to determine the number of clusters for which the marginal gain in explained variance is low (<1%). This curve shows that 16 is the desired number of clusters for the data frame.

Figure 5-2: Elbow curve with ideal K-value highlighted Following the methodology described in Section 5.3.1, the 200 data points were clustered into 16 clusters. Table 5-3 and Table 5-4 display the average feature values of each cluster as well as the average values for the entire data set. Figure 5-3 displays box-and-whisker plots for each cluster of the six target variables, with the centre box line representing the median value, the box edges representing the 25th and 75th percentiles, and the whiskers extending to the minimum and maximum data points. Table 5-5 summarizes the average target variable value for each cluster with values better than the average of all intersection approaches highlighted (greater speeds, lower delays). Cluster IDs of clusters whose average performance exceeds the average values for

64 all target variables are highlighted. Individual maps of the clusters and a summary table listing each cluster’s intersection approaches can be found in Section 8.6 of the Appendix.

Figure 5-3: Target variable distributions for each cluster

Table 5-3: Geometric feature averages for each cluster

Cluster Cluster Thru Left QJ QJ Length Right Chan Receiving Stop Stop Stop Stop ID Size Turn Right (if QJ from Right Bay Near Far Both None Right = 1) Thru 0 9 2.67 0.67 0.56 75.6 0 0.22 0.11 0.11 0.22 0 0.67 1 11 3 1 0 N/A 0 0 0 1 0 0 0 2 16 2.75 1 0.06 39.0 0 0 0.19 0 1 0 0 3 11 3 1 0.18 41.0 0 0 0 1 0 0 0 4 15 2.47 1 0.2 41.7 0 0 0.13 0 0 1 0 5 29 2 1 1 24.2 0 0 0 1 0 0 0 6 31 2 1 1 36.2 0 0 0 0.97 0.03 0 0 7 7 2 1 0 N/A 0 0 0 1 0 0 0 8 13 2 1 0 N/A 0 0 0.08 0.92 0 0 0.08 9 8 2.13 1 0.5 39.8 0 1 0.25 0.38 0.63 0 0 10 7 2.14 1 0 N/A 1 0 0.14 0.57 0.14 0.14 0.14 11 8 2.38 1 0.25 21.5 0 0 0 1 0 0 0 12 6 2.17 1 1 65.2 0 0 1 0.83 0.17 0 0 13 7 3 1 0 N/A 0 0 0.29 1 0 0 0 14 12 2.08 1 1 27.6 0 0 0 1 0 0 0 15 10 2.5 0 0.1 22.0 0 0 0.1 0.8 0.2 0 0 All 200 2.33 0.94 0.48 35.4 0.04 0.05 0.10 0.74 0.14 0.08 0.04

Table 5-4: Dynamic feature averages for each cluster

ID Cluster TMC TMC TMC TMC TMC TMC Sig Split Cycle Sig Split Cycle Size Left Right Thru Left Right Thru Time AM Length Time PM Length AM AM AM PM PM PM AM AM PM PM 0 9 221 659 2250 227 493 2369 53.7 0.46 119.9 56.4 0.46 124.0 1 11 238 175 1741 302 202 2769 60.1 0.52 115.6 61.0 0.52 118.5 2 16 293 281 1975 288 254 1786 53.4 0.46 116.3 52.8 0.44 118.8 3 11 387 263 2903 322 429 2019 50.6 0.41 122.6 45.5 0.36 124.7 4 15 242 211 1549 309 231 1708 43.2 0.39 112.4 43.5 0.37 117.2 5 29 170 178 1592 210 230 1713 60.0 0.53 113.6 63.1 0.55 115.7 6 31 223 238 1269 286 297 1479 42.5 0.39 110.8 43.5 0.39 111.4 7 7 146 223 1519 175 236 1981 56.0 0.52 108.6 55.9 0.52 107.1 8 13 300 136 1409 298 161 1462 45.2 0.41 112.5 43.5 0.41 108.8 9 8 242 210 1537 351 318 1827 41.3 0.37 112.5 45.1 0.39 113.8 10 7 270 442 1504 317 602 2094 46.1 0.37 124.1 44.7 0.36 124.3 11 8 373 48 2131 395 44 2329 75.0 0.64 117.4 74.8 0.63 119.3 12 6 389 195 1614 413 267 1708 42.7 0.35 121.7 41.7 0.35 120.3 13 7 248 204 1303 366 266 2202 44.6 0.38 117.1 45.3 0.38 120.0 14 12 175 197 1648 203 312 1418 50.8 0.46 110.8 47.9 0.44 109.1 15 10 28 262 1930 30 247 1947 56.7 0.51 112.7 58.0 0.51 114.2 All 200 236 235 1692 271 277 1833 51.2 0.45 114.6 51.5 0.45 115.9

Table 5-5 : Target variable averages for each cluster

Average Speed (km/h) Signal Delay (sec) Delay (min/km) Cluster AM PM Count AM PM Count AM PM Count ID Avg Avg Avg Avg Avg Avg 0 23.5 18.1 3 27.0 24.8 8 0.7 0.5 1 1 26.4 21.9 11 22.8 23.6 11 1.4 2.0 10 2 18.5 16.4 16 27.5 38.1 16 1.9 2.7 16 3 15.9 13.5 11 36.7 46.0 11 2.9 4.5 9 4 17.3 13.8 15 30.3 38.8 15 2.4 3.7 15 5 23.1 21.6 27 18.5 18.7 29 1.6 1.8 29 6 17.7 14.4 30 25.5 27.8 31 2.4 2.9 31 7 19.8 18.7 7 21.5 24.9 7 2.2 2.7 7 8 15.5 13.8 12 34.2 38.2 13 3.1 3.5 12 9 19.0 15.4 8 33.4 35.9 8 1.9 2.6 8 10 23.5 18.5 6 30.4 33.9 7 2.3 3.0 6 11 28.9 27.6 8 16.5 20.5 8 0.9 1.2 8 12 19.5 18.5 6 34.1 40.6 6 2.7 3.7 6 13 17.0 12.1 7 32.0 52.3 7 2.8 4.4 7 14 21.3 18.6 12 26.6 24.4 12 2.4 2.3 12 15 22.7 19.6 10 24.9 24.8 10 1.7 2.2 10 All 20.2 17.4 189 26.6 30.5 199 2.1 2.7 187

It should be noted that Cluster 0 has very little segment-level data associated with it. As shown in Table 5-3, many of the intersection approaches in Cluster 0 do not have a bus stop located at the intersection. The four clusters exceeding the average performance for every target variable, Clusters 1, 5, 11, and 15, will each be discussed.

5.4.1.2 Cluster Performance Analysis and Comparisons

Cluster 11 boasts among the best performance for every target variable between the clusters. As shown in Table 5-4, right turning volumes for Cluster 11 intersection approaches are much lower than other clusters. Conversely, Table 5-4 also shows that Cluster 15, whose performance is better than average though not to the same extent as Cluster 11, has low left turning volumes. These two clusters contain most of the three-way approaches in the data set, with 9 out of 18 of the approaches between the two clusters located at three-way intersections. Many of Cluster 11’s approaches are configured without right turns while many of those in Cluster 15 lack left turns.

As a result, Clusters 11 and 15 consist of opposing approach directions at many of the same intersections. The locations of approaches of these two clusters are shown in Figure 5-4 and a comparison of the average features of these two clusters is found in Table 5-6 with greater values highlighted.

Figure 5-4: Locations of approaches in Clusters 11 and 15 Table 5-6: Comparison of features between Clusters 11 and 15

Feature Cluster 11 Cluster 15 All Approaches Average Average Average TMC Left AM (veh/2 hrs) 373 28 236 TMC Right AM (veh/2 hrs) 48 262 235 TMC Thru AM (veh/2 hrs) 2131 1930 1692 TMC Left PM (veh/2 hrs) 395 30 271 TMC Right PM (veh/2 hrs) 44 247 277 TMC Thru PM (veh/2 hrs) 2329 1947 1833 Sig Time AM (sec) 75.0 56.7 51.2 Split AM (green/amber frac) 0.64 0.51 0.45 Cycle Length AM (sec) 117.4 112.7 114.6 Sig Time PM (sec) 74.8 58.0 51.5 Split PM (green/amber frac) 0.63 0.51 0.45 Cycle Length PM (sec) 119.3 114.2 115.9

As discussed, the approaches of Cluster 11 display low right turning volumes, and a similar observation is found for left turning volumes in Cluster 15. Cluster 11 also shows better transit performance in all target variables than Cluster 15, which in turn still shows better performance than the average over all approaches. Consequently, low right and left turning volumes appear to play a role in improving transit performance, with right turning volumes being more critical than left. As buses along the study routes mainly operate in the rightmost lane, they may experience queues of right turning vehicles more frequently than queues of left turning vehicles. Additionally, as most stops at the intersections studied are near-side, bus access of the stops may be impeded by queues of right turning vehicles. Cluster 11 avoids this issue at its locations with no right turn available. This suggests that implementing turning restrictions, either full-time or part-time throughout the day, could be an effective strategy to improve speeds and reduce delays, with right turn restrictions showing more efficacy than left turn restrictions. However, traffic volumes in all directions would need to be studied if restrictions were to be implemented, and restrictions cannot be placed at all intersections of a corridor.

Also noteworthy is the difference of signal split values between Clusters 11 and 15, and in turn the differences between Cluster 15 and the average of all clusters. The trend in signal split is similar to that of speed and the opposite of delay for these clusters, suggesting that signal split may directly or indirectly be related to these performance metrics. Passive or active signal timing adjustments in favour of public transit could be considered an effective treatment option. However, signal coordination and the effects of signal timing adjustments on other road users must be considered if these interventions were to be implemented.

Clusters 11 and 15 represent an ideal case for transit operations. However, bus service at busy four-way intersections is inevitable. Clusters containing several such intersections were examined. In particular, clusters comprising of intersection approaches with two common intersection configurations throughout the study corridors were analyzed. Firstly, a configuration of the following features was considered:

• Two through lanes • Left turn lane • Right turn exclusive/QJ lane • Near-side stop

Clusters 5 exclusively contains approaches with this configuration, as does Cluster 6 with the single exception of one approach containing a far-side stop instead of a near-side stop. From Table 5-5, Cluster 5 displays better transit performance than the data set average for all target variables, while Cluster 6 ranks lower than average for operating speed and comparable to average for delay. The more poorly performing approaches within generally well-performing Cluster 5 were first studied for further investigation into their low performance. Afterwards, Cluster 5 was compared against the worse-performing Cluster 6 of the same configuration for potential insights into reasons for this difference in performance. Figure 5-5 displays the locations of approaches of these two clusters with the better-performing Cluster 5 in blue and the worse-performing Cluster 6 in red.

Figure 5-5: Locations of approaches in Clusters 5 and 6 *Except Steeles Ave E and Markham Road WB approach (in Cluster 6) which has far-side stop

Cluster 5 is one of the largest clusters, with 29 intersection approaches. Of these 29 approaches, 6 rank more poorly than the cluster average for all target variables. These are listed in Table 5-7.

Table 5-7: Consistently poorly performing approaches in Cluster 5

Approach AM PM AM Signal PM Signal AM PM Speed Speed Delay (sec) Delay (sec) Delay Delay (km/h) (km/h) (min/km) (min/km) Finch Ave E & 19.4 14.0 29.1 24.7 2.0 2.9 Warden Ave (EB) Lawrence Ave W & 8.7 4.1 33.3 37.7 6.6 7.6 Marlee Ave (EB) Lawrence Ave E & 21.0 17.1 35.8 35.0 2.4 2.0 The Donway W (WB) Steeles Ave W & 13.9 15.0 20.3 37.6 2.3 2.6 Kipling Ave (EB) Steeles Ave W & 12.1 13.9 46.3 26.3 3.3 2.5 Weston Rd (EB) Steeles Ave W & 14.7 16.8 27.4 26.8 1.9 2.7 Weston Rd (WB) Cluster Average 23.1 21.6 18.5 18.7 1.6 1.8

It was noted that many of these intersections are close to (within 300 metres of) other major or minor arterials.

• Eastbound Finch Avenue at Warden Avenue is 300 metres downstream of Bridletowne Circle West and 290 metres upstream of Bridletowne Circle East • Eastbound Lawrence Avenue at Marlee Avenue is 120 metres upstream of Allen Road • Westbound Lawrence Avenue at The Donway West is 265 metres downstream of Don Mills Road • Eastbound Steeles Avenue at Weston Road is 270 metres upstream of Signet Drive • Westbound Steeles Avenue at Weston Road is 255 metres downstream of Signet Drive Transit performance at the above locations are hampered by such proximity to other traffic signals. Segments downstream of another close signalized intersection do not allow for transit to accelerate to high speeds, and segments closely upstream of another intersection may experience spillover effects from queues at that intersection. While transit agencies cannot change existing road layouts such as these, municipalities in the process of planning futures road layouts could

72 consider spacing major intersections apart if public transit performance is a priority. Additionally, this observation may justify the need for greater spacing between bus stops, with stops too close together reducing transit speed and increasing delay.

Cluster 5 as a whole was then compared to Cluster 6 to determine if factors could be identified to explain the better performance seen by Cluster 5 approaches. Table 5-8 shows the average of the data frame features for the two clusters compared against each other and the average for all intersection approaches with the greater values of the two clusters highlighted.

Table 5-8: Comparison of features between Clusters 5 and 6

Feature Cluster 5 Cluster 6 All Approaches Average Average Average QJ Length (m) 24.2 36.2 35.4 TMC Left AM (veh/2 hrs) 170 223 236 TMC Right AM (veh/2 hrs) 178 238 235 TMC Thru AM (veh/2 hrs) 1592 1269 1692 TMC Left PM (veh/2 hrs) 210 286 271 TMC Right PM (veh/2 hrs) 230 297 277 TMC Thru PM (veh/2 hrs) 1713 1479 1833 Sig Time AM (sec) 60.0 42.5 51.2 Split AM (green/amber frac) 0.53 0.39 0.45 Cycle Length AM (sec) 113.6 110.8 114.6 Sig Time PM (sec) 63.1 43.5 51.5 Split PM (green/amber frac) 0.55 0.39 0.45 Cycle Length PM (sec) 115.7 111.4 115.9

Compared to Cluster 5 approaches, approaches in Cluster 6 have greater turning traffic volumes and lower through volumes, as well as shorter green and amber signal times and lower signal splits. Since all but one approach between these two clusters have near-side stops, it is possible that those approaches with lower right turning volumes have better transit performance due to shorter right turning vehicle queues blocking the stop. Similarly, larger left turning volumes may occupy greater road capacity, increasing congestion in the through lanes. Since these two clusters only have intersections with two through lanes, congestion from turning vehicles could have a substantial effect on transit performance. Similar to the discussion of Clusters 11 and 15, turning restrictions are an intervention frequently employed to reduce traffic and transit congestion at

73 major intersections. While full turning restrictions cannot be in place at every intersection along a corridor, cities implementing transit-priority measures may consider some form of turning restriction, such as time-of-day-based or location-based, where turning can be primarily allowed at intersections with 3 through lanes or far-side stops as to allow buses to more easily bypass queues of turning vehicles.

Consistent with the analysis of Clusters 11 and 15, cycle splits could be a factor in determining performance. Greater signal cycle splits would logically reduce signal delays, and thus increase speed and reduce overall delays on the segment level. However, at major-major intersections with high-frequency transit in all directions, all transit riders must be considered. Agencies wishing to implement transit priority measures may be able to coordinate traffic signals of such major-major intersections proportionally to transit ridership in each direction, with the idea being to minimize the total person-delay of transit riders. TSP green extension algorithms consider similar reasoning by extending the green signal if a transit vehicle is present, with the goal of moving passengers in the vehicle through the intersection before its signal turns red. As none of the intersection approaches in the data frame have activated TSP, it cannot be said if TSP significantly helps transit performance on these corridors. However, due to the difference in signal timings and splits of these otherwise geometrically similar intersection approaches, it is likely that TSP could improve transit performance at such intersections.

The second common configuration of approaches in the data frame consists of:

• Three through lanes • Left turn lane • No right turn/QJ lane • Near-side stop

. Clusters 1, 3, and 13 primarily include this configuration, with the exception of two of Cluster 3’s approaches containing a QJ/right turn lane. Table 5-5 shows that Clusters 3 and 13 perform worse than average for all six target variables, while Cluster 1 performs better than average. Figure 5-6 displays the locations of the more poorly performing Clusters 3 and 13 in red, and the better performing Cluster 1 in blue.

Figure 5-6: Locations of approaches in Clusters 1, 3, and 13

*Except Steeles Ave W and Bathurst St WB approach, and Sheppard Ave E and Bayview Ave WB approach (both Cluster 3) Firstly, Clusters 3 and 13 were analyzed for the better-performing approaches within these clusters. Two approaches in each cluster performed better than their respective cluster average in most or all target variables. Table 5-9 lists these locations.

Table 5-9: Best performing approaches in Clusters 3 and 13

Approach AM PM AM Signal PM Signal AM PM Speed Speed Delay (sec) Delay (sec) Delay Delay (km/h) (km/h) (min/km) (min/km) Cluster 3 Steeles Ave W & 19.0 15.7 5.5 23.6 1.1 2.0 Alness St (WB) Steeles Ave W & 20.1 18.0 29.6 22.3 1.4 3.6 Bathurst St (WB) Cluster Average 15.9 13.5 36.7 46.0 2.9 4.5 Cluster 13 Finch Ave E & 17.4 14.1 15.8 36.1 2.2 3.9 Don Mills Rd (EB) Lawrence Ave E & 23.0 20.9 35.9 52.2 1.3 1.8 McCowan Rd (EB) Cluster Average 17.0 12.1 32.0 52.3 2.8 4.4

The above locations are similar in traffic volumes and signal timings and splits to their cluster averages. However, it is noted that many of the other approaches in these clusters are immediately downstream of driveways for vehicles accessing and egressing parking lots from the primary corridor, whereas these approaches are generally not. These types of driveways give rise to many vehicles turning onto or off the corridor, resulting in increased volumes of turning traffic, cars changing lanes, and stopped vehicles as they wait for an opportunity to turn. It is quite likely that these factors can contribute to delays as buses must maneuver around such vehicles. To improve transit performance, turning restrictions can be imposed for such driveways, allowing potential access from only one direction. This may reduce the number of left turning or right turning vehicles accessing and egressing the transit corridor.

Next, features of Cluster 1 were compared to those of Clusters 3 and 13. A summary can be found in Table 5-10, with greater values between clusters highlighted.

Table 5-10: Comparison of features between Clusters 1, 3, and 13

Cluster 1 Cluster 3 Cluster 13 All Average Average Average Approaches Average TMC Left AM (veh/2 hrs) 238 387 248 236 TMC Right AM (veh/2 hrs) 175 263 204 235 TMC Thru AM (veh/2 hrs) 1741 2903 1303 1692 TMC Left PM (veh/2 hrs) 302 322 366 271 TMC Right PM (veh/2 hrs) 202 429 266 277 TMC Thru PM (veh/2 hrs) 2769 2019 2202 1833 Sig Time AM (sec) 60.1 50.6 44.6 51.2 Split AM (green/amber frac) 0.52 0.41 0.38 0.45 Cycle Length AM (sec) 115.6 122.6 117.1 114.6 Sig Time PM (sec) 61.0 45.5 45.3 51.5 Split PM (green/amber frac) 0.52 0.36 0.38 0.45 Cycle Length PM (sec) 118.5 124.7 120.0 115.9

Once again, the cluster with better performance shows greater signal cycle splits. These observations further justify the case that designing signal timing proportional to transit ridership may benefit transit performance for such an intersection configuration. Additionally, TSP may also be a benefit for these types of intersections. As well, turning volumes are lower for Cluster 1, which further justifies turning restrictions as a transit priority measure. The through-traffic volumes do not appear to play a role in transit performance in this analysis.

5.4.2 Ordinary Least Squares Regressions OLS was conducted for the six target variables using the data frame as the feature matrix. An R2 value was found for each of the six regressions (Table 5-11).

Table 5-11: R-Squared values of OLS regressions for each target variable

Metric Speed Signal Delay Delay Peak Pd. AM PM AM PM AM PM R2 0.273 0.378 0.271 0.360 0.242 0.381

For each of the three metrics, evening peak regressions show greater accuracy than morning peak regressions, meaning the features of the data frame better describe the evening peak conditions

through OLS. Peak conditions are likely better reflected throughout the duration of evening peak (3:00pm to 7:00pm) than morning peak (6:30am to 10:30am) as fringe hours of the morning peak period more likely represent early morning and midday trips. This suggests evening peak period performance is more consistent than morning peak period performance. Because of this, only the three evening peak target variables were further examined for significant features. Table 5-12 summarizes the significance of each feature when describing the performance of intersection approaches in the data frame. Statistically significant features at the 95% confidence level for each variable are bolded. Features not statistically significant for any of the three metrics are listed in the bottom section of the table.

Table 5-12: Summary of OLS regression results for PM target variables

PM Speed PM Speed PM Sig PM Sig PM Delay PM Delay Variable Coef t-stat Delay Coef Delay t-stat Coef t-stat Constant -7.372 -2.300 62.722 7.574 6.051 7.258 Thru 4.544 3.834 -2.400 -0.825 -0.546 -1.766 Right from Thru 8.947 3.442 -10.466 -1.672 -1.325 -1.965 QJ Right 4.229 2.920 -10.956 -3.019 -1.097 -2.835 Sig Time -0.190 -2.350 0.503 2.504 0.059 2.813 Split 61.662 5.931 -153.466 -5.926 -16.231 -5.985 Stop Both -2.722 -1.909 14.913 4.000 2.168 5.835 Stop Far -2.610 -1.705 16.709 4.410 1.606 4.037 Stop Near -2.040 -1.617 17.398 5.741 2.276 6.877 Stop None N/A N/A 13.703 2.840 N/A N/A Left Turn -1.517 -0.751 3.826 0.802 0.560 1.068 Chan Right 2.198 1.086 -0.500 -0.098 -0.844 -1.600 Receiving Bay 0.415 0.282 2.540 0.684 0.473 1.207 QJ Length -0.005 -0.155 0.000 0.005 0.001 0.132 TMC Left 0.003 1.068 -0.003 -0.413 -0.001 -1.035 TMC Right -0.004 -1.632 -0.005 -0.897 0.000 0.041 TMC Thru -0.001 -1.550 0.002 0.965 0.000 0.785 R2 0.378 0.360 0.381 n 189 199 187

The most noteworthy observation of this table is the significance of the intersection’s traffic cycle split in dictating the performance of the approach. The positive coefficient for average speed indicates that greater splits yield greater average speeds, showing an average increase of 6.2km/h per split increase of 0.1. Likewise, the negative coefficients for signal delay and segment-level delay indicate that delays are reduced with greater cycle splits, respectively showing an average reduction of 15.3sec and 1.6min/km per split increase of 0.1. Longer splits do not allow for long queues to form and allow queues to dissipate more effectively. As such, buses are not subject to as high levels of traffic congestion at intersections with longer splits as those with lower splits, and thus mobility is easier. The regressions further support the possible application of TSP green extension algorithms for signals with lower splits.

On the note of traffic signal plans, green/amber signal time shows an opposite relationship to the target variables to that of cycle split. The table indicates that lower green signal times lead to greater operating speeds and lower delays. However, this could instead be an indicator of cycle lengths. Chapter 2 demonstrates that longer cycle lengths typically lead to greater delays and lower operating speeds. To test this claim, the regression models were re-run with cycle length in place of green/amber signal time. Table 5-13 shows that the same relationship exists for cycle length and green/amber signal time suggesting that longer cycle lengths cause lower operating speeds (approximately 0.7km/h per 10 second increase in length) and longer delays (approximately 2.0sec at the signal level and 0.2min/km at the segment level per 10 second increase in length). The coefficients for the cycle lengths range between one third and one half of those for green/amber signal times, consistent with the fact that green signal times are typically one third to one half of the total cycle. Combined with the signal split results, if a corridor is to be redesigned with a transit-priority focus, signal timing plans should be adjusted such that cycles are shorter with greater split for directions with greater transit ridership. This would need to be considered, however, in conjunction with existing signal coordination plans of signals along the corridor and intersecting corridors. Consideration would also need to be given to changes in other vehicle delays.

Table 5-13: Comparison of green signal time and cycle length features

Metric PM Speed PM Signal Delay PM Delay Feature Sig Time Cycle Length Sig Time Cycle Length Sig Time Cycle Length Coefficient -0.190 -0.077 0.503 0.204 0.059 0.024 t-statistic -2.350 -2.038 2.504 2.195 2.813 2.399

The existence of a QJ/exclusive right turn lane positively impacts operating speeds (4.2km/h increase) and reduces delays (11.0sec at the signal level and 1.1min/km at the segment level) for the studied intersection approaches. By allowing buses to bypass queues of traffic, Chapter 2 shows how these lanes can effectively reduce delays. Interestingly, the length of the QJ does not seem to significantly impact speeds or delays at the intersection. This suggests that if the space is available to add a queue jump lane, it would improve transit performance regardless of length. Common barriers to QJ lanes include topographic constraints and the need for property acquisition.

For average speeds, the number of through lanes seems to significantly improve conditions at intersection approaches. As most of the approaches consist of 2 or 3 through lanes, those with 3 appear to have higher operating speeds than those with 2. This is most likely as traffic volumes are better accommodated with 3 lanes than with 2. However, the same is not said for signal delay and segment-level delay. Additionally, none of the three metrics are significantly impacted by traffic volumes, through or turning in this analysis. This could be due to data limitations of the model or the need for an analysis of traffic volume-to-capacity ratios.

The existence of a right turn lane re-designated from a through lane also significantly raises operating speeds and reduces segment-level delays. These lanes may experience the same conditions as queue jump lanes which branch off through lanes upstream from the intersection. However, very few intersection approaches have these types of lanes and not enough data may be present to be able to conclude that these types of right turn lanes positively impact transit conditions.

While all stop configurations lower operating speeds and yield high delays, including Stop None for measuring signal delays, the relative values of the coefficients of the dummy stop location features note the impacts of stop location. The signal delay regression shows that approaches

80 with no stop have the lowest impact on signal delay duration, and near-side stops have the greatest impact. Interestingly, having both a near-side and far-side stop seems to result in lower signal delay than having a single stop, either near-side or far-side. However, this is likely due to insufficient data of intersections with two stops, as operating speeds drop with the addition of stops along a route. Segment-level delay analysis also shows that near-side stops lead to greater delays than far-side stops, consistent with literature findings that show in most cases a far-side stop is preferable to a near-side stop. Stop relocation from near-side to far-side could be considered to reduce delays.

5.4.3 Regression Tree Analysis For the grid search process, several parameter values were tested for both maximum depth of tree and minimum number of samples in the leaf. Maximum tree depth ranged as all integers from 2 through 7, and minimum number of samples in a leaf ranged as multiples of 5 from 5 through 50. Following the methodology outlined in Section 5.3.2.2, the optimal maximum tree depth and minimum number of samples in the leaves were determined for each of the six target variables. These are presented in Table 5-14. The trees themselves can be found in Section 8.7 of the Appendix, but their results will be summarized in tabular form in this section.

Table 5-14: Optimal tree parameters for each target variable

Parameter Optimal Max Tree Optimal Min Depth Samples in Leaf AM Speed 3 10 PM Speed 2 35 AM Signal Delay 2 5 PM Signal Delay 2 35 AM Delay 4 20 PM Delay 3 35

5.4.3.1 Average Speed

Firstly, regression trees were developed for morning and evening average speed, and the results are shown in Table 5-15.

Table 5-15: Summary of operating speed tree

AM Speed PM Speed Conditions Average # of Conditions Average # of Speed Samples Speed Samples (km/h) (km/h) Split ≥ 0.58 29.43 17 Split ≥ 0.488 22.234 63 Split ≤ 0.58 20.718 60 Split ≤ 0.488 16.816 50 TMC Left ≥ 18.5 Split ≥ 0.392 Sig Time ≥ 51.5 Split ≤ 0.58 17.716 101 Split ≤ 0.392 13.855 76 TMC Left ≥ 18.5 Sig Time ≤ 51.5 Split ≤ 0.58 25.246 11 TMC Left ≤ 18.5

In both cases, the split is the primary decision factor. Approaches with greater splits are found to have greater operating speeds. On average, these trees show that intersections with splits over 0.58 have an average morning speed 10.2km/h greater than those with lower splits. Evening speeds increase by an average of 7.2km/h for intersections with splits greater than 0.488. While evening data shows that split was the only factor in classifying approaches based on average speed in this analysis, it is not to say other factors do not affect speed. Given the relatively small data set, the variance of other features is relatively high and thus more nodes were not created given the optimal tree parameters. The morning operating speed tree shows that left turning volumes and green signal times also play a role into morning bus speed. If an approach has a split less than 0.58, morning speed is greatest when left turning volumes are minimized (by an average of 6.4km/h). This is likely because long red signal times cause long queues of left turning vehicles to form, introducing congestion onto the roadway and hindering bus access to the stop or intersection. Additionally, given low splits and high left turning volumes, if the signal time is shorter, then speed will be lower.

These results support the clustering and OLS results that greater traffic cycle splits should be awarded to corridors with higher transit ridership if municipalities are aiming to design traffic signal plans with a transit priority focus. In fact, this regression shows it is the most important

factor when considering operating speed, particularly because evening peak period speeds are generally lower than morning peak period operating speeds.

5.4.3.2 Average Signal Delay

Next, regression trees were developed for morning and evening peak signal delays, with the results summarized in Table 5-16.

Table 5-16: Summary of signal delay tree

AM Signal Delay PM Signal Delay Conditions Average # of Conditions Average # of Signal Samples Signal Samples Delay Delay (sec) (sec) Split ≥ 0.57 13.902 22 Split ≥ 0.497 19.089 66 Split ≥ 0.511 20.136 26 Split ≥ 0.417 27.486 35 Split ≤ 0.57 Split ≤ 0.497 Split ≤ 0.511 30.036 34 Split ≤ 0.417 33.822 55 Split ≥ 0.404 QJ Right = 1 TMC Left ≥ 201 Split ≤ 0.511 22.402 39 Split ≤ 0.417 43.705 54 Split ≥ 0.404 QJ Right = 0 TMC Left ≤ 201 Split ≤ 0.404 31.085 20 TMC Right ≥ 196.5 TMC Left ≥ 221.5 Split ≤ 0.404 23.785 21 TMC Right ≥ 196.5 TMC Left ≤ 221.5 Split ≤ 0.404 39.176 37 TMC Right ≤ 196.5

The trees developed for signal delay are more detailed and complex than those developed for average operating speed. However, it is still evident that signal cycle split is a feature of key importance in determining transit performance for signal delay. In both morning and evening peak periods, signal delays are the lowest when split is the highest. Morning peak results show a

83 signal delay at intersections with split greater than 0.57 (13.9sec) less than half that of locations with split less than 0.57 (28.2sec). Evening peak results are similar, showing a 17.1sec difference between signals with splits less than 0.497 and greater than 0.497. This makes sense, as signal delays are greater the longer the bus is idling at a red signal, and red signals are longer for the bus if the split is lower. Given lower split values, morning signal delays are found to be greatest for intersections with low right turn volumes. However, this observation may be due to the methodology for determining signal delay. As most stops are near-side, buses have trouble accessing the stop with large right turning volumes. When long queues are present, buses may serve passengers short of the stop, which could fall outside the signal buffer. In this case, the queue delay would not be captured as signal delay.

In the evening peak period, given low signal splits, signal delays are greater when no queue jump lane exists by a difference of 9.9sec given a split of less than 0.41. When the split is low, long traffic queues may form. As buses must wait behind these queues to access near-side stops, the presence of a queue jump lane would allow buses to bypass idling through traffic to access the stop, thus reducing signal delay. As shown in Chapter 2, queue jump lanes improve transit performance. If a city wishes to improve transit at an intersection where the split must be kept low, a queue jump lane could possibly be a feasible method to do so.

5.4.3.3 Average Delay

Finally, the trees were developed for morning and evening delay time per kilometre. These results are shown in Table 5-17.

Table 5-17: Summary of segment-level delay tree

AM Delay PM Delay Conditions Average # of Conditions Average # of Delay Samples Delay Samples (min/km) (min/km) Split ≥ 0.57 0.795 19 Split ≥ 0.488 1.677 65 Split ≥ 0.465 1.8 58 Split ≥ 0.41 2.538 40 Split ≤ 0.57 Split ≤ 0.488 Split ≤ 0.465 1.927 25 Split ≤ 0.41 3.17 40 QJ Length ≥ 33.5 QJ Length ≥ 7.5 Split ≤ 0.465 2.718 85 Split ≤ 0.41 4.155 42 QJ Length ≤ 33.5 QJ Length ≤ 7.5

As with the previous two performance metrics, split is the variable with the greatest decision power for transit performance. Morning peak segment delays reduce by an average of 1.5min/km if a split is greater than 0.57, and by an average of 0.7min/km if a split is greater than 0.465. Evening delays drop by an average of 1.6min/km for splits greater than 0.488 and by an average of 1.1min/km for splits greater than 0.41. The morning and evening peak periods show similar factors affecting delay time. Given a low split, the presence and length of a queue jump lane is the greatest factor in reducing delay. Approaches without queue jump lanes, or with short queue jump lanes accumulate greater delay time than those with long queue jump lanes (by 0.79min/km in the morning and 0.99min/km in the evening), as buses must wait behind queues of through traffic given a low split at the intersection.

Evidently, traffic signal cycle split is a key factor in determining transit performance. If a transit agency or municipal governement wishes to improve transit performance, signal timing plans and TSP should be considered first. Other factors including the presence and length of queue jump lanes and turning traffic volumes also play a role in transit performance. Some factors including stop location, the presence of channelized right turns or receiving bus bays, the number of through lanes, and green signal timings do not affect these transit performance metrics to the same extent. However, constraints and considerations highlighted in this chapter must be addressed prior to implementation.

6 Summary and Conclusions

6.1 Summary of Thesis The two objectives of this thesis were to develop a descriptive analysis of surface transit performance along four Toronto corridors and to apply data science techniques to the results to determine factors that affect transit performance. The descriptive analysis was conducted by developing a methodology to find five performance metrics between the route, segment, stop, and intersection levels from AVL, GTFS, and contractor-collected data. The five performance metrics were found for eight transit routes along the four corridors in both eastbound and westbound directions during both the morning and evening weekday peak periods. The results of these metrics were discussed in detail for the westbound direction during the evening peak period, and results for all four direction-peak combinations were presented between Chapter 4 and the Appendix, Chapter 8.

Morning and evening peak values of three of the performance metrics, average operating speed, signal delay, and segment-level delay, were utilized as the basis for describing the performance of unidirectional intersection approaches. A set of 200 approaches were clustered independent of performance based on physical and dynamic attributes. Intra-cluster and inter-cluster analyses were conducted to examine why some approaches of similar characteristics perform better than others, and what treatments transit agencies can apply to improve transit performance at the poorly performing locations. The approaches were further analyzed through OLS regression and regression trees to determine which intersection characteristics have greater influence on the transit performance metrics.

Combined, these two parts of the thesis form the basis of automated transit performance monitoring and treatment which can be applied to the budding practice of spot improvement programs.

6.2 Conclusions 6.2.1 Descriptive Analysis The descriptive analysis of westbound evening routes found overall that of the eight transit routes studied, Route 52 Lawrence West continuously displays the poorest performance at the

86 route, segment, stop, and intersections levels, with Routes 60 Steeles West and 54 Lawrence East also displaying poor performance at certain locations. Route 52 intersects with Allen Road, a busy north-south expressway with access to Highway 401, and Black Creek Drive, a major thoroughfare which becomes Highway 400 north of Lawrence Avenue. Lawrence Avenue is also the closest parallel corridor north of Eglinton Avenue, which was undergoing heavy construction for the Crosstown LRT at the time of data collection, likely resulting in traffic spillover. Steeles Avenue West is highly industrialized with physically large traffic travelling along it while also the boundary between Toronto and Vaughan.

Average speed analysis showed that most segments of the section of Lawrence Avenue West between Bathurst and Jane Streets experience speeds below 20km/h. The two segments of Steeles Avenue West leading to Keele Street report the lowest operating speeds among all segments studied. Segment-level delay analysis displayed similar results with most of the segments of greatest delays found between Routes 52, 54, and 60. Many of the lowest speeds and greatest delays are reported at segments leading to major intersections. Some north-south corridors, including Jane Street, Bathurst Street, Don Mills Road, Warden Avenue, and Kennedy Road, repeatedly yield poor westbound evening segment-level performance on the corridors studied.

Stop-level analysis found that less than half of westbound timing points across all eight routes studied meet the TTC’s service standard of on-time performance in the evening peak period, with none of the timing points along Routes 52 Lawrence West, 54 Lawrence East, and 36 Finch West meeting the standard over all trips studied. The number of on-time trips is generally higher for stops with headways less than 5 minutes compared to those with headways between 5 and 10 minutes, as the definition of on-time is more relaxed. With the exceptions of Routes 85 Sheppard East and 52 Lawrence West, OTP is found to decrease at timing points downstream on a route.

Signal delay analysis found that the majority of the greatest evening signal delays in the westbound direction along occur along Lawrence Avenue, between Routes 52 Lawrence West and 54 Lawrence East. Similar to segment-level analysis, high signal delays are found commonly between corridors at the same north-south intersections, including Jane Street, Keele Street, Warden Avenue, and McCowan Road. This is expected, as all of these roads are major arterials with high-frequency transit and access to the Regional Municipality of York. Lowest signal

87 delays are found along Route 36 Finch West, which is also the only route of the studied eight with activated TSP control in favour of the study corridor at several traffic signals.

Running time variation at the route level found that the running times of more poorly performing routes, Routes 52, 54, and 60, are in fact more similar to their schedules than other routes studied, which are frequently faster than scheduled. Shorter routes, including Routes 36 Finch West and 85J Sheppard East between Yonge Street and Don Mills Road, show the greatest variation in running time, likely due to their length. Delay analysis at the route level shows Route 52 has the greatest average route-level delay for eastbound runs among the studied routes, and has similarly high delay westbound, second to Route 54 in the morning and Route 60 in the evening. Average delay and variability in delay are greater in the evening peak period than the morning peak period, with Routes 52 Lawrence West, 54 Lawrence East, 60 Steeles West, and 39 Finch East showing high variabilities in route-level delay.

6.2.2 Clustering and Regression Analyses This section applied data science techniques to locations from the descriptive analysis seeking to provide information for municipal governments or transit agencies wishing to impose transit- priority spot improvements at locations of poor performance. Suggested spot improvements include signal timing adjustments, TSP, turning restrictions, and implementation and extension of queue jump lanes.

The clustering algorithm placed the 200 intersection approaches with available TMC and STP data into 16 clusters. Clusters varied in performance based off values of the six target variables, with some performing better than others in all. One cluster, Cluster 11, consistently ranked among the best for performance. The approaches found in this cluster had very low right turning volumes, due to in part some of the approaches being found at three-way intersections. Similarly, Cluster 15, whose performance was good but not to the same extent as Cluster 11, showed low left turning volumes as it also contains many approaches of three-way intersections. This comparison suggested that turning restrictions could be effective transit-priority measures, with right turning restrictions showing better results for buses than left.

Two common road configurations found along the four corridors were studied. The first configuration consists of two through lanes, a left turn lane, and a right turn/queue jump lane

88 with a near-side stop. Clusters 5 and 6 consisted primarily of this configuration, with Cluster 5 performing much better than Cluster 6. The more poorly performing locations within Cluster 5 were found to be all within 300 metres of another signalized intersection with an arterial road, indicating limitations of bus acceleration from upstream approaches or possible spillover effects from downstream approaches. As a whole, Cluster 5 approaches were found to have greater splits than Cluster 6 approaches, indicating that more time spent with a green signal along a corridor factors into better transit performance. This can be used as an argument for signal timing plans proportional to transit ridership, or possibly TSP implementation to improve performance at intersections with this configuration. Additionally, Cluster 6 had greater right and left turning volumes than cluster 5, showing that turning restrictions would also improve transit conditions at similar locations.

The second configuration of three through lanes, a left turn lane, no right turn/queue jump lane, and a near-side stop, was found in Clusters 1, 3, and 13. Cluster 1 performed better than Clusters 3 and 13 in all performance metrics. Much like the previous configuration, Cluster 1 was found to have longer green signal times and greater splits as well as lower turning volumes than Clusters 3 and 13. This shows that signal timing adjustments in favour of transit ridership and/or TSP along with turning restrictions would benefit intersections of this configuration.

Regression analyses showed similar results, with speed, signal delay, and segment-level delay all being primarily influenced by traffic cycle split. Higher splits in favour of the transit direction lead to higher speeds and lower delays for both morning and evening figures. OLS found that longer green/amber signal times, and by extension cycle lengths, lead to lower operating speeds and greater delays at the intersection and segment levels. QJ lanes were also shown to improve transit conditions at intersections regardless of their length.

Tree analysis showed details for cases of better transit performance given lower signal splits. It showed that speed in the morning was also found to decrease with greater left turning volumes. Signal delay in the evening decreased when a queue jump lane was present. Segment-level delay decreased with the presence of a longer queue jump lane. This analysis showed that if changes cannot be made to a traffic signal plan and TSP cannot be implemented, turning restrictions and queue jump lanes also improve transit performance.

Caveats exist for implementing the suggested transit-priority measures. In all cases, the effects of these measures must be studied against general traffic and other road users. For signal timing adjustments, full corridors must be considered and optimized, most likely using simulation software, to properly recommend signal timing and coordination plans. QJ lanes must consider potential land constraints, such as property acquisition or other infrastructure changes such as bridge widening. Additionally, factors which were not found to be significant in these analyses, such as stop location, QJ length, and other geometric features, have been shown in previous studied to have effects on transit speeds and delays and should not be discounted following this research, especially due to some of the limitations listed in Section 6.3.

6.3 Limitations and Future Work Much of the data processing, particularly based on the contractor-collected data, was limited due to data availability. A route had a maximum of 30 runs for each direction-peak combination, with fewer of those containing sufficient event data. Event data was necessary in the determination of signal delay, and thus that part of the analysis yielded many results with little or no data. The signal delay methodology depends on capturing data points within a buffer. Buffer sizes were determined based on visual inspection of queue lengths; however, it is difficult to know if they over-estimated or under-estimated the size of these queues. They do not capture exceedingly long queues due to signal delay, nor do they capture congestion blocking the passage of a bus through an intersection. Additionally, as signal delay and delay used the same data points, those whose speed was calculated to be less than 5km/h, these two variables were not fully independent of one another, and problematic locations could have been overrepresented.

Even the usable event data was limited, as sources of congestion were infrequently noted, as were events of holding or driver changes. These events would have been useful in describing the conditions and reason for low speeds and high delays of specific locations, as some locations may not be performing as poorly as the data suggests.

The number of intersection approaches used for the clustering and regression analyses was lower than it could have been due to TMC and STP data availability limitations. Additionally, the

90 number of features in the data frame was limited, and potentially descriptive features including ridership of the routes along the primary and intersecting corridors, traffic volume to capacity (V/C) ratios, and space availability for expansion were not included.

As this work explores the early steps of automated surface transit performance evaluation and improvement recommendation based on characteristics, a larger sample size of locations could be looked at with a more in-depth set of features. Additionally, beyond just the intersection level, this type of analysis could be conducted at the segment level and route level. Analyses could be conducted for low-frequency routes, downtown routes, and streetcar routes to determine if different factors affect their performance.

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Seattle Department of Transportation. (2016, November 16). Spot Improvements. Retrieved from seattle.gov: https://www.seattle.gov/transportation/projects-and- programs/programs/transit-program/spot-improvements

SFMTA. (2015). Muni Forward Implementation Workbook. San Francisco. Retrieved from sfmta.com: https://www.sfmta.com/projects/muni-forward

Shalaby, A., Abdulhai, B., & Lee, J. (2003). Assessment of streetcar transit priority options using microsimulation modelling. Canadian Journal of Civil Engineering, 1000-1009.

Skabardonis, A. (2000). Control Strategies for Transit Priority. Transportation Research Record, 20-26.

Taylor, B. D., & Fink, C. N. (2003). The Factors Influencing Transit Ridership: A Review and Analysis of the Ridership Literature. University of California, Los Angeles, Department of Urban Planning, Los Angeles.

TCRP. (1997). The Role of Transit in Creating Livable Metropolitan Communities. New York: National Academy Press.

TCRP. (2003). A Guidebook for Developing a Transit Performance-Measurement System. Washington DC: Transportation Research Board.

TCRP. (2003). Transit Capacity and Quality of Service Manual - 2nd Edition. Washington DC: Transportation Research Board.

TCRP. (2016). A Guidebook on Transit-Supportive Roadway Strategies. Washington DC: Transportation Research Board.

Toronto Transit Commission. (2017). Service Standards and Decision Rules for Planning Transit Service. Toronto.

TPB. (2018). FY 2019-2024 Transportation Improvement Program. Washington DC: Metropolitan Washington Council of Governments.

Truong, L. T., Sarvi, M., & Currie, G. (2016). An investigation of multiplier effects generated by implementing queue jump lanes at multiple intersections. Advanced Transportation, 1699-1715.

8 Appendix

8.1 STOIS Phase 3 Evaluation Framework Technical Memorandum To: David Kuperman, Renata Moraes, Matthew Davis, Date: 18 September 2018 Hussain Tamimi - City of Toronto; Trevor Pitman, Project:476635 Jose Rubio - TTC From: Rita Hu, Glareh AmirJamshidi, Yannis Stogios – Parsons Amer Shalaby, Ehab Diab, Mahmood Mahmoodi Nesheli, Graham Devitt – UTTRI Re: City of Toronto Surface Transit Operational Improvement Studies (Phase 3): Development of Evaluation Framework - Draft 8.1.1 Introduction The evaluation framework is developed to adequately define and evaluate improvement strategies for routes, stops, intersections and segments along each of the study corridors. The development is based on a thorough review of the state of the art and state of the practice but customized appropriately for this project with due consideration given to the available resources and time constraints.

8.1.2 Main Objectives The following are the main objectives of the evaluation framework development.

• The evaluation framework will reflect and be consistent with the local best practices of the City of Toronto and TTC, with assistance from the international industry literature. • The evaluation process under this framework will consider relevant strategies included in the “planning, urban design, transportation, and parking policies contained in the Official Plans, Secondary Plans, and Site and Area Specific Policies”, as specified in the RFP; and consult parallel studies. • Following this evaluation framework, we will be able to recommend performance improvement strategies at the stop, intersection and route levels, as appropriate.

• The Key Performance Indicators (KPIs)/Measures of Effectiveness (MOEs) used in the evaluation process will reflect the goal of improving speed and reliability while minimizing negative impacts on the general traffic and other road users.

8.1.3 Evaluation Framework The proposed evaluation framework, shown in Figure 1, consists of three stages: analysis and diagnosis, treatment options, evaluation and recommendation. The assessment of existing conditions, i.e. diagnosis, will be conducted based on data collected in the field and data provided by the City and TTC. The treatment options are broadly grouped into four categories: planning/scheduling, traffic/parking, physical/geometric, and ITS. For each category of improvements, a set of suitable criteria is developed to enable a comparative assessment for selecting complementary measures and avoiding competing ones.

8.1.3.1 Diagnosis

In this stage, the types of operational problems along the study routes will be detected and classified. For each route, direction and time period, we will identify operational problems by type (speed, reliability), level of impact (stop, intersection, segment), scale (localized, extended) and severity (low, moderate, severe). The identification and assessment of operational problems will be informed by and based on a set of performance indicators with appropriate thresholds, as defined in Table 1. Graphical presentation of the operational performance (possibly in the form of a heat map) for each route, direction and time period can help quickly identify problems by location and time and correlations across space and time. In addition, we will assess the sources of operational problems, which can be broadly classified as internal factors (i.e. related to the transit service) or external factors such as traffic disturbance and disruptions. The output of this stage is a list of operational problems for each route, classified according to various criteria (objective, severity, scale and level) and distinguished by location and time period. The output will also include an assessment of the possible sources of each identified operational problem. Finally, visual inspection of satellite images and possibly field observation of selected segments/spots with severe performance issues will be performed to help make appropriate suggestions of treatment options. Table 2 shows examples of sources of problems.

Evaluation and Diagnosis Treatment Options Recommendation

Type of the problem: Pass/Fail • Internal Transit Route Feasibility, Boundaries • External Planning & Scheduling • Physical (space) • Supply (Fleet size) Problem Assessment Traffic & Parking

Level Objective • Stop • Intersection ITS Implementation • Speed • Segment Pros/Cons • Reliability • Route

Severity Physical & Geometry Scale

• Low Companion strategy • Localized • Moderate • Extended None Possible • Severe

Test the selected strategies • Rank the problem based on with the preferred scenarios * problem assessment Quality Check • Check available/parallel cases Compatibility of: and studies • Direction • Visual assessment based on • Time period satellite images and field • Parallel studies and cases Recommend the final strategy observation

*Traffic Simulation will be conducted as per RFP requirement.

Figure 8-1. Proposed Evaluation Framework

Table 1. Performance Measures for Surface Transit Service

Performance Objectives Performance Measures and Targets Area

Reliability • Increase “on- • Percent of trips departing within an acceptable time time” window from terminal. The TTC Standard: to be performance considered on-time, a vehicle must leave its origin timepoint between 1 minute early and 5 minutes late. • Increase The TTC’s goal is to have 90% of all trips depart on schedule time. Any vehicle leaving more than 20 minutes late adherence from one end is considered a ‘missed trip’. The TTC’s • Decrease long goal is to minimize the number of missed trips on delays each route. • Percent of trips arriving within an acceptable time window at a timing point. The TTC Standard: (1) for routes with service headway > 10 min, a vehicle is considered to be on time if it is no more than one minute early and no more than five minutes late. The TTC’s goal is to have 60% of all trips meet the on- time performance standard; (2) for service headway ≥ 5 min and ≤ 10min, a vehicle is considered on time if the headway deviation is less than 50% of the scheduled headway. The TTC’s goal is to have 60% of all trips within ±50% of the scheduled headway over the entire service day; (3) for headway < 5min, a vehicle is considered on time when the headway deviation is less than 75% of the scheduled headway. TTC’s goal is to have 60% of all trips operated within ±75% of the scheduled headway over the entire service day.

• Percent of trips arriving within an acceptable time window at terminal (i.e., finishing the trip). The TTC Standard: to be considered on-time, a vehicle must arrive at its terminal timepoint between 1 minute early and 5 minutes late. The TTC’s goal is to have 60% of all trips arrive on-time. • Average delay at stop Speed • Decrease travel • Average speed times • Average running speed • Number of low-speed trips by time of day • Reduce signal • Average passenger-serving time at stop delaysa • Average signal delay • Reduce dwell • Average non-serving (idling) time (e.g. holding at times timing point) a Traffic Signal Operations Policies and Strategies report is used.

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Table 2. Example Sources of Problems

Diagnosis Internal Factors External Factors Planning Operation • Long bus route • Poor on-time dispatching • Poor traffic signal • Short spacing between • Considerably late/early • On-street parking stops trips • Poor street design for bus • Problematic stop location • Long idling at stops operation and maneuver • Type of vehicle • Uneven load • Vehicles blocking • Overloaded vehicles intersection • High variance of schedule headway and travel times • High variance of passenger demand 8.1.3.2 Treatment Options

In Stage 2 of the framework, we will identify alternative treatments (each consisting of one or more strategies) to address each operational problem detected in stage 1. In the next sub-section, we will first discuss the various potential strategies considered for application in this study. Next, we will discuss in more detail the likely impact of each strategy and its applicability to specific operational problems.

Candidate Strategies for Improved Performance

The following table shows four main categories: i) Planning & Scheduling modification; ii) Physical & Geometric Modification; iii) Traffic & Parking Regulation, and iv) Intelligent Transportation System. Individual strategies as well as combinations of strategies may be considered as appropriate interventions.

Table 3. List of Strategies

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Planning and scheduling Traffic and parking Physical and Intelligent modifications regulation geometric transportation modification system

• Stop location • Turn • Queue • Passive Route Interacti Lane Traffi and spacing restrictio jump signal structure on with s c ns • Bus timing traffic signal • Parking lanes adjustme s restrictio nt ns • Phase re- service • Traffic signal shadowi ng • Local/Expr • Moveme • Length • Transit Operation Bus Stop Trans ess service nt en bus signal manageme moveme s it restrictio stop priority nt nt signal n • Curb • Bus-only s exempti extensi phase on on • Pre- • Boardi signals ng • Bus islands traffic signal

The details of the strategies considered for different levels are provided below. The category of each strategy is shown in parentheses. Some strategies that have more than one main impact level is described only once.

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STOP-LEVEL STRATEGIES

• Lengthen bus stop (physical measure): Under this strategy, a bus stop’s length (i.e. bus bay) is increased to allow for serving more (or longer) buses simultaneously. • Curb extensions (physical measure): Curb extensions (bus bulbs, bus nubs) extend the curb and sidewalk out to the edge of the parking lane. • Boarding islands (physical measure): Bus stops on raised concrete islands within the roadway.

INTERSECTION-LEVEL STRATEGIES

• Relocate stops (physical measure): Under this strategy, an existing bus stop is moved from its current location at an intersection (e.g., near side) to a different location (e.g., far side). • Movement restriction exemption (traffic and parking): Buses are allowed to make movements (e.g., left turns, right turns, proceed straight ahead) that are prohibited for other vehicles. • Turn restrictions (traffic and parking): One or more existing general traffic turning movements at an intersection are prohibited. Selectively prohibiting turning movements can free up time or roadway space for use by buses and traffic in general. • Queue jump lanes (physical measure): Buses (or in some applications, buses and right turning vehicles) are given an opportunity to move past queued through vehicles at a signalized intersection and, in many cases, to proceed into the intersection in advance of the through traffic. • Passive-signal timing adjustments (ITS): Existing signal timing plans are optimized to reduce delay for traffic in general on intersection approaches used by buses or for buses specifically. Since the signal timing plan is applied whether or not a bus is present, the adjustments are considered to be passive. • Phase re-service (ITS): A traffic signal phase is served twice during a traffic signal cycle— for example, a left turn phase that is served both at the start and the end of the green phase for through traffic. Serving a phase twice per cycle minimizes the chance a bus has to wait for the next cycle to be served and thereby reduces bus travel time delay and variability.

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This strategy accommodates varying bus arrival times at a traffic signal (e.g., caused by varying dwell times at an upstream stop) better than serving one phase only per cycle. • Signal priority (ITS): Traffic signal timing is altered in response to a request from a bus so that the bus experiences no or reduced delay passing through the intersection. • Bus-only phase (ITS): A traffic signal phase included in the traffic signal cycle to serve bus movements that cannot be served, or are not desired to be served, concurrently with other traffic. • Pre-signals (ITS): A traffic signal for one direction of a street, coordinated with a traffic signal at a downstream intersection that is used to control the times when particular vehicles may approach the intersection. In a transit context, pre-signals are used at the end of a bus lane to give buses priority access to the intersection when constraints make it infeasible to continue the bus lane all the way to the intersection. They can also be used to manage queues on the intersection approach—for example, when a side street or driveway is regularly blocked by queues extending back from the traffic signal. • Bus traffic signal (ITS): An intersection that is signalized primarily to serve bus movements rather than general traffic.

SEGMENT-LEVEL STRATEGIES

• Curb-side bus lane (physical measure): A bus lane located in the rightmost lane of the roadway and adjacent to the right curb. • Shared bus/bike lane (physical measure): A curbside lane shared part- or full-time by buses and bicycles; other users may also be allowed into the lane at specific times or locations. • Queue bypass (physical measure): A relatively short bus lane that allows buses to move to the front of the line at a bottleneck, where they merge again into the adjacent general traffic lane. To avoid delays caused by waiting in the general traffic queue to pass the bottleneck. • Median bus lane (physical measure): Lanes reserved for the exclusive use of buses. These lanes are located in the middle of a roadway and are often separated from other traffic by curbs or landscaped islands.

ROUTE-LEVEL STRATEGIES

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• Consolidate stops (planning and scheduling): Bus stop spacing is optimized—typically by increasing the spacing—so that buses make fewer stops along the route while minimally affecting the area served by transit. This strategy saves time due to acceleration and deceleration delay, door opening and closing time, traffic signal delay and re-entry delay. • Local/Express service during peak time: During peak periods, run some trips as express trips. These will serve only major stops with high peak demand and will theoretically result in a shorter run time.

Detailed Impacts of Individual Strategies

The likely impact of each strategy for the appropriate impacted level is presented in Table 4. This table is mainly based on “A Guidebook on Transit-Supportive Roadway Strategies” (Paul et al., 2016), which can be used as a quick reference for identifying and comparing potential strategies in Stage 3.

As shown in Table 4, each class of strategy covers several single strategies to address the issues that are detected at Stage 1. The effect and constraint of each strategy are summarized at their main impact level(s). For example, the impact of TSP is provided only at the intersection level, although the use of TSP at multiple intersections would benefit the transit line performance at the segment or route levels. More detailed description about the effect, constraint and companion strategy is presented in Appendix B.

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8.1.3.3 Evaluation and Recommendation

Stage 3 provides recommendations based on the feasibility and boundaries that each strategy might have with pro/con and companion strategy. The proposed solution could be further assessed using statistical/simulation methods. The selection process generally follows three steps:

1. Identify potential strategies that might have benefit to each corridor/route and check the feasibility of each identified strategy (Table 5a and Figure 2), identify scenarios based on the feasible strategies (single or combination) and assess the pros/cons for each scenario, then rank the scenarios (Table 5b); Figure 8-2. Feasibility

2. Conduct further assessment using micro-simulation models for Check specific scenarios/strategies in accordance to the RFP requirements; and 3. Recommend the suitable scenario(s) (package of interventions across the entire route); provide summary of benefits and disbenefits associated with each scenario.

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Table 4 Typical Treatment/Solution Options for Each Level (TCRP, 2016) (continue next page)

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Table 4 Typical Treatment/Solution Options for Each Level (TCRP, 2016) (continued from previous page)

Notes: 0 = none, L = low, M = moderate, H = high, VH = very high. PS = planning and scheduling, PM = physical measures, TP = traffic and parking. * = duplicate strategy. Grey cells = no related information based on our literature review.

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Table 5a) Check the Feasibility for the Identified Strategies for Each Corridor/Route

Pass/Fail Strategy Boundaries1 Complement strategy2 Strategy 1 Strategy 2 … 1Boundaries describes the limitations for implementing the proposed strategy; check mark with some criteria from Stage 2 such as: Traffic volumes, bus volumes, space, public/policy restrictions. 2 The complimentary strategy to make the system better.

Table 5b) Rank the Potential Scenarios

Assessment Scenarios Ranking Pros Cons No Effect Scenario 1 Scenario 2 …

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8.2 Operating Speed Data 8.2.1 Tables EBAM Speed

Table 8-1: Eastbound segments with the lowest operating speeds for each route in the morning peak period

Route Segment Speed Route Segment Speed (km/h) (km/h) 60 Steeles Jane St 10.22 53 Steeles Don Mills Rd 10.79 West Bathurst St 10.73 East Brimley Rd 12.31 Weston Rd 12.13 Middlefield Rd 12.35 Islington Ave 12.81 Redlea Ave 16.74 Kipling Ave 13.95 Dumont St 16.80 36 Finch Virgilwood Dr 15.27 39 Finch East Kennedy Rd 16.41 West Wilmington Ave 16.96 Don Mills Rd 17.41 Bathurst St 18.81 McCowan Rd 17.74 Champagne Dr 19.16 Leslie St 19.07 Goldfinch Ct 20.65 Victoria Park Ave 19.14 84 Sheppard Keele St 10.69 85 Sheppard Birchmount Rd 16.63 West Jane St 15.51 East Wilfred Ave 16.94 Northover St 16.28 Victoria Park Ave 17.36 Wilson Heights Blvd 19.60 Bayview Ave 17.37 Norman Wesley Way 19.97 Consumers Rd 17.73 52 Lawrence Keele St 7.51 54 Lawrence Morning Dew Rd 9.69 West Marlee Ave 8.68 East Birchmount Rd 10.43 Bathurst St 11.80 Bennett Rd 10.75 Caledonia Rd 11.93 Don Mills Rd 11.26 Avenue Rd 12.39 Pharmacy Ave 13.74

Table 8-2: Eastbound segments with lowest operating speeds over all study routes in the morning peak period

Rank Segment Route Average Speed (km/h) 1 Keele St 52 Lawrence West 7.51 2 Marlee Ave 52 Lawrence West 8.68 3 Morning Dew Rd 54 Lawrence East 9.69

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4 Jane St 60 Steeles West 10.22 5 Birchmount Rd 54 Lawrence East 10.43 6 Keele St 84 Sheppard West 10.69 7 Bathurst St 60 Steeles West 10.73 8 Bennett Rd 54 Lawrence East 10.75 9 Don Mills Rd 53 Steeles East 10.79 10 Don Mills Rd 54 Lawrence East 11.26 11 Bathurst St 52 Lawrence West 11.80 12 Caledonia Rd 52 Lawrence West 11.93 13 Weston Rd 60 Steeles West 12.13 14 Brimley Rd 53 Steeles East 12.31 15 Middlefield Rd 53 Steeles East 12.35 16 Avenue Rd 52 Lawrence West 12.39 17 Islington Ave 60 Steeles West 12.81 18 Olympia Dr 52 Lawrence West 12.83 19 Corona St 52 Lawrence West 12.92 20 Weston Rd 52 Lawrence West 12.98 21 Martin Grove Rd 52 Lawrence West 13.24 22 Dufferin St 52 Lawrence West 13.68 23 Pharmacy Ave 54 Lawrence East 13.74 24 Glen Rush Blvd 52 Lawrence West 13.85 25 Kipling Ave 60 Steeles West 13.95

Table 8-3: Eastbound segments with the lowest operating speeds for each route in the evening peak period

Route Segment Speed Route Segment Speed (km/h) (km/h) 60 Steeles Dufferin St 7.94 53 Steeles Don Mills Rd 9.80 West Bathurst St 8.48 East Middlefield Rd 10.54 2700 Steeles Ave W 8.75 Brimley Rd 12.58 Jane St 10.82 Warden Ave 12.94 Alness St 11.66 Redlea Ave 13.76 36 Finch Bathurst St 9.85 39 Finch East Kennedy Rd 8.26 West Champagne Dr 11.88 Victoria Park Ave 10.76 Virgilwood Dr 12.31 Leslie St 11.96 Dufferin St 12.37 Warden Ave 13.96 Yonge St 13.38 Don Mills Rd 14.10

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84 Sheppard Jane St 9.84 85 Sheppard Birchmount Rd 6.11 West Keele St 10.87 East McCowan Rd 10.07 Bathurst St 11.40 Warden Ave 10.16 Wilson Heights Blvd 14.06 Victoria Park Ave 12.49 Northover St 15.78 Markham Rd 12.82 52 Lawrence Marlee Ave 4.10 54 Lawrence Warden Ave 8.11 West Weston Rd 8.15 East Don Mills Rd 8.76 Bolingbroke Rd 9.81 Bennett Rd 9.16 Martin Grove Rd 9.86 Pharmacy Ave 9.45 Keele St 10.30 Morning Dew Rd 10.18

Table 8-4: Eastbound segments with lowest operating speeds over all study routes in the evening peak period

Rank Segment Route Average Speed (km/h) 1 Marlee Ave 52 Lawrence West 4.10 2 Birchmount Rd 85 Sheppard East 6.11 3 Dufferin St 60 Steeles West 7.94 4 Warden Ave 54 Lawrence East 8.11 5 Weston Rd 52 Lawrence West 8.15 6 Kennedy Rd 39 Finch East 8.26 7 Bathurst St 60 Steeles West 8.48 8 2700 Steeles Ave W 60 Steeles West 8.75 9 Don Mills Rd 54 Lawrence East 8.76 10 Bennett Rd 54 Lawrence East 9.16 11 Pharmacy Ave 54 Lawrence East 9.45 12 Don Mills Rd 53 Steeles East 9.80 13 Bolingbroke Rd 52 Lawrence West 9.81 14 Jane St 84 Sheppard West 9.84 15 Bathurst St 36 Finch West 9.85 16 Martin Grove Rd 52 Lawrence West 9.86 17 McCowan Rd 85 Sheppard East 10.07 18 Warden Ave 85 Sheppard East 10.16 19 Morning Dew Rd 54 Lawrence East 10.18 20 Birchmount Rd 54 Lawrence East 10.25 21 Keele St 52 Lawrence West 10.30 22 Middlefield Rd 53 Steeles East 10.54

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23 Victoria Park Ave 39 Finch East 10.76 24 Jane St 60 Steeles West 10.82 25 Brimley Rd 54 Lawrence East 10.85

Table 8-5: Westbound segments with the lowest operating speeds for each route in the morning peak period

Route Segment Speed Route Segment Speed (km/h) (km/h) 60 Steeles Weston Rd 14.71 53 Steeles Old Kennedy Rd 15.30 West Carpenter 15.66 East Don Mills Rd 15.47 Keele St 15.98 Woodbine Ave 15.98 Old Weston Rd 16.88 Warden Ave 16.06 Dufferin St 17.12 Kennedy Rd 16.88 36 Finch Bathurst St 16.88 39 Finch East Victoria Park Ave 13.40 West Alness St 17.75 Skymark Dr 13.57 Greenview Ave 18.55 Don Mills Rd 13.76 Ancona St 19.26 Midland Ave 13.84 Torresdale Ave 20.98 Bayview Ave 15.14 84 Sheppard Allen Rd 12.72 85 Sheppard Midland Ave 11.19 West Jane St 14.63 East McCowan Rd 12.36 Bathurst St 16.11 Kennedy Rd 12.49 Weston Rd 19.30 Warden Ave 13.28 Buckland Rd 21.22 Birchmount Rd 14.53 52 Lawrence Bolingbroke Rd 12.02 54 Lawrence Don Mills Rd 7.60 West Brucewood Cres 13.06 East Victoria Park Ave 10.65 Kipling Ave 14.56 Markham Rd 11.11 Dufferin St 14.69 Warden Ave 11.47 Avenue Rd 14.80 Midland Ave 11.97

Table 8-6: Westbound segments with lowest operating speeds over all study routes in the morning peak period

Rank Segment Route Average Speed (km/h) 1 Don Mills Rd 54 Lawrence East 7.60 2 Victoria Park Ave 54 Lawrence East 10.65 3 Markham Rd 54 Lawrence East 11.11

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4 Midland Ave 85 Sheppard East 11.19 5 Warden Ave 54 Lawrence East 11.47 6 Midland Ave 54 Lawrence East 11.97 7 Bolingbroke Rd 52 Lawrence West 12.02 8 McCowan Rd 85 Sheppard East 12.36 9 Flerimac Rd 54 Lawrence East 12.42 10 Kennedy Rd 85 Sheppard East 12.49 11 William R Allen Rd 84 Sheppard West 12.72 12 Brucewood Cres 52 Lawrence West 13.06 13 McCowan Rd 54 Lawrence East 13.17 14 Warden Ave 85 Sheppard East 13.28 15 Victoria Park Ave 39 Finch East 13.40 16 Skymark Dr 39 Finch East 13.57 17 Goldberry Sq 54 Lawrence East 13.69 18 Don Mills Rd 39 Finch East 13.76 19 Midland Ave 39 Finch East 13.84 20 Kingston Rd 54 Lawrence East 13.88 21 Birchmount Rd 54 Lawrence East 14.00 22 Birchmount Rd 85 Sheppard East 14.53 23 Kipling Ave 52 Lawrence West 14.56 24 Jane St 84 Sheppard West 14.63 25 Dufferin St 52 Lawrence West 14.69

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8.2.2 Maps

Figure 8-3: Eastbound morning peak period segment speeds mapped

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Figure 8-4: Eastbound evening peak period segment speeds mapped

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Figure 8-5: Westbound morning peak period segment speeds mapped

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8.3 On-Time Performance Data 8.3.1 Tables Table 8-7: On-time performance of eastbound timing points in the morning peak period

Timing Timing Point Combined OTP Definition % Trips Sequence Headway On-Time (min) 60 Steeles West 1 Kipling Ave 7.33 Within 50% of headway 86.2 2 Islington Ave 7.33 Within 50% of headway 82.5 3 Weston Rd 7.33 Within 50% of headway 68.2 4 Jane St 7.33 Within 50% of headway 57.4 5 Pioneer Village Stn 3.67 Within 75% of headway 66.2 6 Keele St 2.68 Within 75% of headway 56.0 7 Dufferin St 2.68 Within 75% of headway 56.3 8 Yonge St 2.68 Within 75% of headway 53.4 53 Steeles East 1 Yonge St 5.25 Within 50% of headway 51.0 2 Bayview Ave 2.9 Within 75% of headway 67.2 3 Leslie St 5.25 Within 50% of headway 58.0 4 Don Mills Rd 2.9 Within 75% of headway 64.5 5 Victoria Park Ave 2.9 Within 75% of headway 69.6 6 Woodbine Ave 2.9 Within 75% of headway 63.9 7 Silver Star Blvd 5.25 Within 50% of headway 67.0 8 Brimley Rd 2.9 Within 75% of headway 71.2 9 McCowan Rd 2.9 Within 75% of headway 65.8 10 Middlefield Rd 2.9 Within 75% of headway 63.9 11 Markham Rd 2.9 Within 75% of headway 63.0 36 Finch West 1 Finch West Stn 6 Within 50% of headway 83.0 2 Dufferin St 6 Within 50% of headway 75.0 3 Bathurst St 6 Within 50% of headway 59.3 39 Finch East 1 Bayview Ave 3.33 Within 75% of headway 63.1 2 Don Mills Rd 3.33 Within 75% of headway 65.9 3 Seneca Hill Dr 3.33 Within 75% of headway 64.4 4 Victoria Park Ave 5 Within 50% of headway 58.9 5 Warden Ave 5 Within 50% of headway 58.7 6 Birchmount Rd 5 Within 50% of headway 55.9

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7 Midland Ave 5 Within 50% of headway 54.3 8 McCowan Rd 5 Within 50% of headway 54.6 9 Sandhurst Cir E 5 Within 50% of headway 54.2 10 Markham Rd 5 Within 50% of headway 55.9 11 Neilson Rd 5 Within 50% of headway 88.2 84 Sheppard West 1 Jane St 4.25 Within 75% of headway 65.5 2 Sheppard West Stn 2.88 Within 75% of headway 56.3 3 Bathurst St 2.88 Within 75% of headway 50.7 85 Sheppard East (Meadowvale to Don Mills) 1 Victoria Park Ave 6.5 Within 50% of headway 69.5 2 Midland Ave 6.5 Within 50% of headway 61.9 3 Brimley Rd 6.5 Within 50% of headway 59.9 4 McCowan Rd 6.5 Within 50% of headway 58.3 5 Scunthorpe Rd 6.5 Within 50% of headway 57.3 6 Markham Rd 6.5 Within 50% of headway 57.9 7 Morningside Ave 6.5 Within 50% of headway 59.0 85J Sheppard East (Don Mills to Yonge) 1 Bayview Ave 15 5 min late to 1 min early 70.4 52 Lawrence West 1 Kipling Ave 6 Within 50% of headway 43.1 2 Islington Ave 6 Within 50% of headway 44.1 3 Royal York Rd 6 Within 50% of headway 42.4 4 Weston Rd 3 Within 75% of headway 56.4 5 Jane St 3 Within 75% of headway 53.7 6 Culford Rd 3 Within 75% of headway 46.2 7 Keele St 3 Within 75% of headway 47.4 8 Dufferin St 3 Within 75% of headway 49.1 9 Bathurst St 4 Within 75% of headway 56.6 10 Lorindale Ave 4 Within 75% of headway 52.0 54 Lawrence East 1 Don Mills Rd 4.5 Within 75% of headway 78.3 2 Victoria Park Ave 4.5 Within 75% of headway 76.3 3 Birchmount Rd 4.5 Within 75% of headway 70.3 4 Kennedy Rd 4.5 Within 75% of headway 62.7 5 McCowan Rd 3 Within 75% of headway 61.5 6 Markham Rd 3 Within 75% of headway 60.7 7 Scarborough Golf Club Rd 3 Within 75% of headway 45.8 8 Kingston Rd 4.5 Within 75% of headway 59.5

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9 Morningside Ave 4.5 Within 75% of headway 57.8 10 Rouge Hill GO Stn 4.5 Within 75% of headway 66.3

Table 8-8: On-time performance of eastbound timing points in the evening peak period

Timing Timing Point Combined OTP Definition % Trips Sequence Headway On-Time (min) 60 Steeles West 1 Kipling Ave 7.5 Within 50% of headway 56.9 2 Islington Ave 7.5 Within 50% of headway 52.1 3 Weston Rd 7.5 Within 50% of headway 49.3 4 Jane St 7.5 Within 50% of headway 43.9 5 Pioneer Village Stn 3.75 Within 75% of headway 60.5 6 Keele St 3 Within 75% of headway 57.3 7 Dufferin St 3 Within 75% of headway 34.9 8 Yonge St 5.25 Within 50% of headway 38.4 53 Steeles East 1 Yonge St 5.25 Within 50% of headway 49.9 2 Bayview Ave 3.08 Within 75% of headway 70.2 3 Leslie St 5.25 Within 50% of headway 44.6 4 Don Mills Rd 3.08 Within 75% of headway 67.6 5 Victoria Park Ave 3.08 Within 75% of headway 64.7 6 Woodbine Ave 3.08 Within 75% of headway 58.6 7 Silver Star Blvd 5.25 Within 50% of headway 44.1 8 Brimley Rd 3.08 Within 75% of headway 63.5 9 McCowan Rd 3.08 Within 75% of headway 64.9 10 Middlefield Rd 3.08 Within 75% of headway 62.1 11 Markham Rd 3.08 Within 75% of headway 62.0 36 Finch West 1 Finch West Stn 6.75 Within 50% of headway 59.0 2 Dufferin St 6.75 Within 50% of headway 53.0 3 Bathurst St 6.75 Within 50% of headway 45.0 39 Finch East 1 Bayview Ave 4.67 Within 75% of headway 61.0 2 Don Mills Rd 4.67 Within 75% of headway 63.1 3 Seneca Hill Dr 4.67 Within 75% of headway 57.3 4 Victoria Park Ave 7 Within 50% of headway 56.3

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5 Warden Ave 7 Within 50% of headway 51.8 6 Birchmount Rd 7 Within 50% of headway 48.1 7 Midland Ave 7 Within 50% of headway 45.9 8 McCowan Rd 7 Within 50% of headway 42.5 9 Sandhurst Cir E 7 Within 50% of headway 42.1 10 Markham Rd 7 Within 50% of headway 41.1 11 Neilson Rd 7 Within 50% of headway 69.6 84 Sheppard West 1 Jane St 5.75 Within 50% of headway 49.0 2 Sheppard West Stn 3.65 Within 75% of headway 61.4 3 Bathurst St 3.65 Within 75% of headway 57.8 85 Sheppard East (Meadowvale to Don Mills) 1 Victoria Park Ave 6.25 Within 50% of headway 67.8 2 Midland Ave 6.25 Within 50% of headway 59.7 3 Brimley Rd 6.25 Within 50% of headway 55.9 4 McCowan Rd 6.25 Within 50% of headway 57.6 5 Scunthorpe Rd 6.25 Within 50% of headway 55.5 6 Markham Rd 6.25 Within 50% of headway 56.1 7 Morningside Ave 6.25 Within 50% of headway 58.5 85J Sheppard East (Don Mills to Yonge) 1 Bayview Ave 16 5 min late to 1 min early 61.8 52 Lawrence West 1 Kipling Ave 5.92 Within 50% of headway 38.3 2 Islington Ave 5.92 Within 50% of headway 34.6 3 Royal York Rd 5.92 Within 50% of headway 28.3 4 Weston Rd 2.92 Within 75% of headway 55.0 5 Jane St 2.92 Within 75% of headway 51.9 6 Culford Rd 2.92 Within 75% of headway 46.6 7 Keele St 2.92 Within 75% of headway 46.4 8 Dufferin St 2.92 Within 75% of headway 45.0 9 Bathurst St 3.9 Within 75% of headway 45.6 10 Lorindale Ave 3.9 Within 75% of headway 42.1 54 Lawrence East 1 Don Mills Rd 5.13 Within 50% of headway 36.3 2 Victoria Park Ave 5.13 Within 50% of headway 28.7 3 Birchmount Rd 5.13 Within 50% of headway 27.4 4 Kennedy Rd 5.13 Within 50% of headway 26.4 5 McCowan Rd 3.5 Within 75% of headway 49.8 6 Markham Rd 3.5 Within 75% of headway 52.2

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7 Scarborough Golf Club Rd 3.5 Within 75% of headway 51.3 8 Kingston Rd 5.3 Within 50% of headway 34.7 9 Morningside Ave 5.3 Within 50% of headway 34.8 10 Rouge Hill GO Stn 5.3 Within 50% of headway 36.0

Table 8-9: On-time performance of westbound timing points in the morning peak period

Timing Timing Point Combined OTP Definition % Trips Sequence Headway On-Time (min) 60 Steeles West 1 Yonge St 2.68 Within 75% headway 63.9 2 Bathurst St 2.68 Within 75% headway 59.7 3 Dufferin St 2.68 Within 75% headway 57.0 4 Keele St 2.68 Within 75% headway 60.2 5 Pioneer Village Stn 7.33 Within 50% headway 60.9 6 Jane St 7.33 Within 50% headway 56.7 7 Weston Rd 7.33 Within 50% headway 54.4 8 Islington Ave 7.33 Within 50% headway 52.1 9 Kipling Ave 7.33 Within 50% headway 49.0 53 Steeles East 1 Middlefield Rd 2.9 Within 75% headway 61.9 2 McCowan Rd 2.9 Within 75% headway 61.4 3 Old Kennedy Rd 5.25 Within 50% headway 54.5 4 Victoria Park Ave 2.9 Within 75% headway 56.3 5 Woodbine Ave 2.9 Within 75% headway 61.2 6 Don Mills Rd 2.9 Within 75% headway 59.1 7 Leslie St 5.25 Within 50% headway 54.6 8 Bayview Ave 2.9 Within 75% headway 61.8 9 Yonge St 5.25 Within 50% headway 41.0 36 Finch West 1 Finch Stn 6 Within 50% headway 86.3 2 Bathurst St 6 Within 50% headway 73.1 3 Dufferin St 6 Within 50% headway 65.6 39 Finch East 1 Neilson Rd 15 5 min late to 1 min early 89.6 2 Sandhurst Cir E 5 Within 50% headway 71.4 3 McCowan Rd 5 Within 50% headway 69.8

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4 Midland Ave 5 Within 50% headway 62.5 5 Birchmount Rd 5 Within 50% headway 58.3 6 Warden Ave 5 Within 50% headway 58.3 7 Victoria Park Ave 5 Within 50% headway 25.9 8 Don Mills Rd 3.33 Within 75% headway 77.1 9 Bayview Ave 3.33 Within 75% headway 64.5 84 Sheppard West 1 Bathurst St 2.88 Within 75% headway 53.6 2 Sheppard West Stn 2.88 Within 75% headway 54.8 3 Keele St 4.25 Within 75% headway 66.0 4 Jane St 4.25 Within 75% headway 61.7 85 Sheppard East (Meadowvale to Don Mills) 1 Morningside Ave 6.5 Within 50% headway 70.6 2 Malvern St 6.5 Within 50% headway 59.3 3 Markham Rd 6.5 Within 50% headway 60.3 4 Scunthorpe Rd 6.5 Within 50% headway 63.7 5 McCowan Rd 6.5 Within 50% headway 65.1 6 Brimley Rd 6.5 Within 50% headway 67.2 7 Midland Ave 6.5 Within 50% headway 69.4 8 Victoria Park Ave 6.5 Within 50% headway 68.5 85J Sheppard East (Don Mills to Yonge) 1 Don Mills Rd 15 5 min late to 1 min early 87.8 2 Bayview Ave 15 5 min late to 1 min early 59.3 52 Lawrence West 1 Dufferin St 3 Within 75% headway 55.2 2 Keele St 3 Within 75% headway 53.4 3 Culford Rd 3 Within 75% headway 49.8 4 Jane St 3 Within 75% headway 52.4 5 Weston Rd 3 Within 75% headway 50.9 6 Islington Ave 6 Within 50% headway 36.6 7 Kipling Ave 6 Within 50% headway 35.5 54 Lawrence East 1 Starspray Loop 4.5 Within 75% headway 88.2 2 Morningside Ave 4.5 Within 75% headway 75.2 3 Kingston Rd 4.5 Within 75% headway 72.3 4 Scarborough Golf Club Rd 3 Within 75% headway 53.5 5 Markham Rd 3 Within 75% headway 49.2 6 McCowan Rd 3 Within 75% headway 55.6 7 Kennedy Rd 4.5 Within 75% headway 70.4

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8 Birchmount Rd 4.5 Within 75% headway 69.6 9 Victoria Park Ave 4.5 Within 75% headway 69.7 10 Don Mills Rd 4.5 Within 75% headway 66.2

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8.3.2 Maps

Figure 8-6: Eastbound timing points displaying on-time performance in the morning peak period

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Figure 8-7: Eastbound timing points displaying on-time performance in the evening peak period

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Figure 8-8: Westbound timing points displaying on-time performance in the morning peak period

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8.4 Signal Delay Data 8.4.1 Tables Note that only signals with at least 5 signal delay measurements are listed.

Table 8-10: Eastbound signalized approaches with highest signal delays for each route in the morning peak period

Route Signal Delay Route Signal Delay (sec) (sec) 60 Steeles Hwy 27 68.2 53 Steeles Victoria Park Ave 45.6 West Islington Ave 51.8 East Midland Ave 37.3 Weston Rd 46.3 Don Mills Rd 35.9 Dufferin St 41.6 Brimley Rd 29.7 Jane St 41.2 Birchmount Rd 29.5 36 Finch 30m W of Endell St 14.0 39 Finch Kennedy Rd 39.9 West Virgilwood Dr 9.1 East McCowan Rd 37.8 Alness St 8.0 Bayview Ave 33.0 Tangiers Rd 7.2 Brahms Ave 30.8 Senlac Dr 6.8 Warden Ave 29.1 84 Sheppard Keele St 46.7 85 Sheppard Victoria Park Ave 32.5 West Rivalda Rd 35.9 East Midland Ave 29.0 Wilson Heights Blvd 32.8 Markham Rd 18.4 Allen Rd 29.9 Kennedy Rd 15.8 Jane St 28.5 McCowan Rd 15.0 52 Lawrence Black Creek Dr 62.4 54 Lawrence Warden Ave 60.3 West Allen Rd NB Ramp 57.1 East Don Mills Rd 53.1 Keele St 55.6 Birchmount Rd 41.6 Weston Rd 52.8 Greencrest Crct 41.4 Jane St 48.9 Kennedy Rd 37.8

Table 8-11: Eastbound signalized approaches with highest signal delays over all study routes in the morning peak period

Rank Signal Route Average Signal Delay (sec) 1 Hwy 27 60 Steeles West 68.2 2 Black Creek Dr 52 Lawrence West 62.4 3 Warden Ave 54 Lawrence East 60.3

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4 Allen Rd NB Ramp 52 Lawrence West 57.1 5 Keele St 52 Lawrence West 55.6 6 Don Mills Rd 54 Lawrence East 53.1 7 Weston Rd 52 Lawrence West 52.8 8 Islington Ave 60 Steeles West 51.8 9 Jane St 52 Lawrence West 48.9 10 Scarlett Rd 52 Lawrence West 48.1 11 Keele St 84 Sheppard West 46.7 12 Weston Rd 60 Steeles West 46.3 13 Victoria Park Ave 53 Steeles East 45.6 14 Dufferin St 52 Lawrence West 42.9 15 Birchmount Rd 54 Lawrence East 41.6 16 Dufferin St 60 Steeles West 41.6 17 Greencrest Crct 54 Lawrence East 41.4 18 Jane St 60 Steeles West 41.2 19 Kennedy Rd 39 Finch East 39.9 20 Kennedy Rd 54 Lawrence East 37.8 21 McCowan Rd 39 Finch East 37.8 22 Allen Rd SB Ramp 52 Lawrence West 37.3 23 Midland Ave 53 Steeles East 37.3 24 Bathurst St 52 Lawrence West 36.9 25 Rivalda Rd 84 Sheppard West 35.9

Table 8-12: Eastbound signalized approaches with highest signal delays for each route in the evening peak period

Route Signal Delay Route Signal Delay (sec) (sec) 60 Steeles Bathurst St 55.9 53 Steeles Middlefield Rd 43.7 West Dufferin St 48.8 East McCowan Rd 40.2 Hwy 27 47.9 Kenendy Rd 33.8 Islington Ave 46.1 Leslie St 27.4 Jane St 38.0 Don Mills Rd 26.5 36 Finch Tangiers Rd 22.4 39 Finch Kennedy Rd 47.5 West 30m W of Endell St 20.4 East Leslie St 44.5 Dufferin St 18.0 McCowan Rd 37.3 Wilmington Ave 16.8 Don Mills Rd 36.1 Goldfinch Ct 14.3 Bayview Ave 35.9

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84 Sheppard Wilson Heights Blvd 46.7 85 Sheppard McCowan Rd 48.5 West Allen Rd 45.3 East Victoria Park Ave 35.1 Rivalda Rd 44.7 Birchmount Rd 34.3 Jane St 44.3 Warden Ave 30.6 Keele St 34.2 Kennedy Rd 29.7 52 Lawrence Allen Rd NB Ramp 58.9 54 Lawrence Don Mills Rd 70.7 West Black Creek Dr 57.6 East Warden Ave 69.4 Kipling Ave 53.7 Markham Rd 68.8 Bolingbroke Rd 52.2 Pharmacy Ave 58.8 Keele St 48.9 Brimley Rd 58.3

Table 8-13: Eastbound signalized approaches with highest signal delays over all study routes in the evening peak period

Rank Signal Route Average Signal Delay (sec) 1 Don Mills Rd 54 Lawrence East 70.7 2 Warden Ave 54 Lawrence East 69.4 3 Markham Rd 54 Lawrence East 68.8 4 Allen Rd NB Ramp 52 Lawrence West 58.9 5 Pharmacy Ave 54 Lawrence East 58.8 6 Brimley Rd 54 Lawrence East 58.3 7 Black Creek Dr 52 Lawrence West 57.6 8 Bathurst St 60 Steeles West 55.9 9 Kipling Ave 52 Lawrence West 53.7 10 McCowan Rd 54 Lawrence East 52.2 11 Bolingbroke Rd 52 Lawrence West 52.2 12 Keele St 52 Lawrence West 48.9 13 Dufferin St 60 Steeles West 48.8 14 McCowan Rd 85 Sheppard East 48.5 15 Hwy 27 60 Steeles West 47.9 16 Kennedy Rd 39 Finch East 47.5 17 Caledonia Rd 52 Lawrence West 47.4 18 Wilson Heights Blvd 84 Sheppard West 46.7 19 Islington Ave 60 Steeles West 46.1 20 Allen Rd 84 Sheppard West 45.3 21 Rivalda Rd 84 Sheppard West 44.7 22 Weston Rd 52 Lawrence West 44.7

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23 Leslie St 39 Finch East 44.5 24 Kennedy Rd 54 Lawrence East 44.4 25 Jane St 84 Sheppard West 44.3

Table 8-14: Westbound signalized approaches with highest signal delays for each route in the morning peak period (limited data for Routes 36 and 85)

Route Signal Delay Route Signal Delay (sec) (sec) 60 Steeles Keele St 36.4 53 Steeles Middlefield Rd 54.8 West Jane St 35.3 East Victoria Park Ave 40.1 Gerry Fitzgerald Dr 33.8 Woodbine Ave 38.8 Dufferin St 30.3 Bayview Ave 37.1 Bathurst St 29.6 Hwy 404 SB Ramp 34.1 36 Finch 39 Finch Don Mills Rd 39.6 West East Midland Ave 37.3 No Data Bayview Ave 35.9 Victoria Park Ave 35.3 Hwy 404 NB Ramp 31.2 84 Sheppard Bathurst St 42.2 85 Sheppard McCowan Rd 56.2 West Allen Rd 41.1 East Warden Ave 22.0 Beecroft Rd 41.0 Birchmount Rd 15.0 Keele St 33.0 Shorting Rd 13.4 Jane St 31.9 Markham Rd 12.3 52 Lawrence Allen Rd SB Ramp 80.2 54 Lawrence Victoria Park Ave 60.2 West Islington Ave 50.4 East Kennedy Rd 49.1 Bathurst St 49.7 Kingston Rd 48.7 Avenue Rd 49.6 Warden Ave 48.1 Jane St 46.7 Midland Ave 46.8

Table 8-15: Westbound signalized approaches with highest signal delays over all study routes in the morning peak period

Rank Signal Route Average Signal Delay (sec) 1 Allen Rd SB Ramp 52 Lawrence West 80.2 2 Victoria Park Ave 54 Lawrence East 60.2 3 McCowan Rd 85 Sheppard East 56.2

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4 Middlefield Rd 53 Steeles East 54.8 5 Islington Ave 52 Lawrence West 50.4 6 Bathurst St 52 Lawrence West 49.7 7 Avenue Rd 52 Lawrence West 49.6 8 Kennedy Rd 54 Lawrence East 49.1 9 Kingston Rd 54 Lawrence East 48.7 10 Warden Ave 54 Lawrence East 48.1 11 Midland Ave 54 Lawrence East 46.8 12 Jane St 52 Lawrence West 46.7 13 Black Creek Dr 52 Lawrence West 45.2 14 Don Mills Rd 54 Lawrence East 43.8 15 Birchmount Rd 54 Lawrence East 43.0 16 Bathurst St 84 Sheppard West 42.2 17 McCowan Rd 54 Lawrence East 41.4 18 Allen Rd 84 Sheppard West 41.1 19 Beecroft Rd 84 Sheppard West 41.0 20 Victoria Park Ave 53 Steeles East 40.1 21 Don Mills Rd 39 Finch East 39.6 22 Woodbine Ave 53 Steeles East 38.8 23 Bellamy Rd 54 Lawrence East 37.7 24 Midland Ave 39 Finch East 37.3 25 Bayview Ave 53 Steeles East 37.1

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8.4.2 Maps

Figure 8-9: Eastbound morning peak period signal delays mapped

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Figure 8-10: Eastbound evening peak period signal delays mapped

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Figure 8-11: Westbound morning peak period signal delays mapped

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8.5 Segment-Level Delay Data 8.5.1 Tables Table 8-16: Eastbound segments with highest delays for each route in the morning peak period

Route Segment Delay Route Segment Delay (min/km) (min/km) 60 Steeles Jane St 3.55 53 Steeles Don Mills Rd 5.92 West Weston Rd 3.30 East Victoria Park Ave 4.76 Bathurst St 3.11 Brimley Rd 4.54 Signet Dr 2.36 Middlefield Rd 4.47 Kipling Ave 2.27 McCowan Rd 3.91 36 Finch Virgilwood Dr 1.32 39 Finch East Kennedy Rd 3.42 West 1111 Finch Ave W 1.24 McCowan Rd 2.87 Bathurst St 1.14 Willowdale Ave 2.39 625 Finch Ave W 1.11 Midland Ave 2.38 Goldfinch Ct 0.93 Don Mills Rd 2.20 84 Sheppard Northover St 3.70 85 Sheppard Birchmount Rd 2.64 West Keele St 3.68 East Midland Ave 2.41 Jane St 3.30 Warden Ave 2.30 Wilson Heights Blvd 2.56 Victoria Park Ave 2.29 Bathurst St 2.26 Neilson Rd 2.21 52 Lawrence Marlee Ave 6.63 54 Lawrence Warden Ave 5.45 West Bathurst St 4.46 East Don Mills Rd 4.72 Weston Rd 4.18 Pharmacy Ave 3.46 Keele St 3.85 Birchmount Rd 2.64 Jane St 3.59 Kennedy Rd 2.46

Table 8-17: Eastbound segments with highest delays over all study routes in the morning peak period

Rank Segment Route Average Delay (min/km) 1 Marlee Ave 52 Lawrence West 6.63 2 Don Mills Rd 53 Steeles East 5.92 3 Warden Ave 54 Lawrence East 5.45 4 Victoria Park Ave 53 Steeles East 4.76 5 Don Mills Rd 54 Lawrence East 4.72

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6 Brimley Rd 53 Steeles East 4.54 7 Middlefield Rd 53 Steeles East 4.47 8 Bathurst St 52 Lawrence West 4.46 9 Weston Rd 52 Lawrence West 4.18 10 McCowan Rd 53 Steeles East 3.91 11 Keele St 52 Lawrence West 3.85 12 Northover St 84 Sheppard West 3.70 13 Keele St 84 Sheppard West 3.68 14 Midland Ave 53 Steeles East 3.65 15 Jane St 52 Lawrence West 3.59 16 Jane St 60 Steeles West 3.55 17 Martin Grove Rd 52 Lawrence West 3.49 18 Pharmacy Ave 54 Lawrence East 3.46 19 Kennedy Rd 39 Finch East 3.42 20 Jane St 84 Sheppard West 3.30 21 Weston Rd 60 Steeles West 3.30 22 Avenue Rd 52 Lawrence West 3.28 23 Dufferin St 52 Lawrence West 3.27 24 Bathurst St 60 Steeles West 3.11 25 Bolingbroke Rd 52 Lawrence West 2.92

Table 8-18: Eastbound segments with highest delays for each route in the evening peak period

Route Segment Delay Route Segment Delay (min/km) (min/km) 60 Steeles Bathurst St 7.53 53 Steeles Don Mills Rd 5.42 West Dufferin St 5.63 East Brimley Rd 4.70 Jane St 3.65 McCowan Rd 4.39 Alness St 3.45 Middlefield Rd 4.36 Kipling Ave 2.65 Warden Ave 3.42 36 Finch Virgilwood Dr 3.18 39 Finch East Kennedy Rd 4.84 West Goldfinch Ct 1.98 Don Mills Rd 3.86 Bathurst St 1.81 Leslie St 3.74 Wilmington Ave 1.78 Victoria Park Ave 3.54 Champagne Dr 1.36 Warden Ave 2.90 84 Sheppard Jane St 5.01 85 Sheppard Birchmount Rd 7.12 West Bathurst St 4.96 East Midland Ave 4.30

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Wilson Heights Blvd 3.88 Warden Ave 4.13 Keele St 2.99 McCowan Rd 4.05 Yeomans Rd 2.16 Markham Rd 3.73 52 Lawrence Weston Rd 7.64 54 Lawrence Pharmacy Ave 6.30 West Marlee Ave 7.59 East Don Mills Rd 5.84 Bolingbroke Rd 5.85 Birchmount Rd 5.52 Corona St 4.90 Morningside Ave 5.03 Martin Grove Rd 4.55 Victoria Park Ave 4.77

Table 8-19: Eastbound segments with highest delays over all study routes in the evening peak period

Rank Segment Route Average Delay (min/km) 1 Weston Rd 52 Lawrence West 7.64 2 Marlee Ave 52 Lawrence West 7.59 3 Bathurst St 60 Steeles West 7.53 4 Birchmount Rd 85 Sheppard East 7.12 5 Pharmacy Ave 54 Lawrence East 6.30 6 Bolingbroke Rd 52 Lawrence West 5.85 7 Don Mills Rd 54 Lawrence East 5.84 8 Dufferin St 60 Steeles West 5.63 9 Birchmount Rd 54 Lawrence East 5.52 10 Don Mills Rd 53 Steeles East 5.42 11 Morningside Ave 54 Lawrence East 5.03 12 Jane St 84 Sheppard West 5.01 13 Bathurst St 84 Sheppard West 4.96 14 Corona St 52 Lawrence West 4.90 15 Kennedy Rd 39 Finch East 4.84 16 Victoria Park Ave 54 Lawrence East 4.77 17 Brimley Rd 53 Steeles East 4.70 18 Martin Grove Rd 52 Lawrence West 4.55 19 McCowan Rd 53 Steeles East 4.39 20 Middlefield Rd 53 Steeles East 4.36 21 Midland Ave 85 Sheppard East 4.30 22 Brimley Rd 54 Lawrence East 4.28 23 The Donway West 54 Lawrence East 4.27 24 Warden Ave 85 Sheppard East 4.13 25 Markham Rd 54 Lawrence East 4.09

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Table 8-20: Westbound segments with highest delays for each route in the morning peak period

Route Segment Delay Route Segment Delay (min/km) (min/km) 60 Steeles Old Weston Rd 2.78 53 Steeles Middlefield Rd 5.77 West Keele St 2.76 East Don Mills Rd 3.87 Carpenter Rd 2.13 Woodbine Ave 3.03 Weston Rd 1.89 Warden Ave 2.94 Pine Valley Dr 1.84 Bayview Ave 2.06 36 Finch Bathurst St 2.36 39 Finch East Victoria Park Ave 4.08 West Virgilwood Dr 0.99 Bayview Ave 3.68 Greenview Ave 0.85 Midland Ave 3.28 Torresdale Ave 0.76 Pharmacy Ave 3.21 Alness St 0.40 Don Mills Rd 3.14 84 Sheppard Bathurst St 4.94 85 Sheppard Brimley Rd 3.35 West Beecroft Rd 4.14 East McCowan Rd 2.86 Jane St 3.95 Pharmacy Ave 2.65 Goddard St 1.70 Kennedy Rd 2.27 Weston Rd 1.58 Warden Ave 2.16 52 Lawrence Avenue Rd 4.45 54 Lawrence Victoria Park Ave 5.51 West Jane St 3.86 East Don Mills Rd 5.07 Dufferin St 3.38 Markham Rd 4.04 Kipling Ave 3.01 Kingston Rd 3.72 Bathurst St 2.94 Pharmacy Ave 3.71

Table 8-21: Westbound segments with highest delays over all study routes in the morning peak period

Rank Segment Route Average Delay (min/km) 1 Middlefield Rd 53 Steeles East 5.77 2 Victoria Park Ave 54 Lawrence East 5.51 3 Don Mills Rd 54 Lawrence East 5.07 4 Bathurst St 84 Sheppard West 4.94 5 Avenue Rd 52 Lawrence West 4.45 6 Beecroft Rd 84 Sheppard West 4.14

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7 Victoria Park Ave 39 Finch East 4.08 8 Markham Rd 54 Lawrence East 4.04 9 Jane St 84 Sheppard West 3.95 10 Don Mills Rd 53 Steeles East 3.87 11 Jane St 52 Lawrence West 3.86 12 Kingston Rd 54 Lawrence East 3.72 13 Pharmacy Ave 54 Lawrence East 3.71 14 Bayview Ave 39 Finch East 3.68 15 Dufferin St 52 Lawrence West 3.38 16 McCowan Rd 54 Lawrence East 3.38 17 Brimley Rd 85 Sheppard East 3.35 18 Midland Ave 39 Finch East 3.28 19 Birchmount Rd 54 Lawrence East 3.25 20 Pharmacy Ave 39 Finch East 3.21 21 Don Mills Rd 39 Finch East 3.14 22 Midland Ave 54 Lawrence East 3.04 23 Woodbine Ave 53 Steeles East 3.03 24 Kipling Ave 52 Lawrence West 3.01 25 Warden Ave 54 Lawrence East 3.00

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8.5.2 Maps

Figure 8-12: Eastbound morning peak period segment-level delays mapped

142

Figure 8-13: Eastbound evening peak period segment-level delays mapped

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Figure 8-14: Westbound morning peak period segment-level delays mapped

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8.6 Clusters 8.6.1 Cluster Lists Table 8-22: Complete list of all intersection approaches sorted by cluster

Approach Direction Primary Route CLUSTER 0 Finch Ave E & Gordon Baker Rd/Finch E 404 N Ramp EB 39 Finch East Finch Ave E & Gordon Baker Rd/Finch E 404 N Ramp WB 39 Finch East Finch Ave E & 404 S Finch Ramp EB 39 Finch East Finch Ave E & 404 S Finch Ramp WB 39 Finch East Steeles Ave E & Leslie St EB 53 Steeles East Bayview Ave & Sheppard Ave E EB 85 Sheppard East Sheppard Ave E & Leslie St EB 85 Sheppard East Sheppard Ave E & 404 N Sheppard Ave Ramp/Yorkland Rd EB 85 Sheppard East Sheppard Ave E & 404 N Sheppard Ave Ramp/Yorkland Rd WB 85 Sheppard East CLUSTER 1 Steeles Ave E & Pharmacy Ave/Esna Park Dr EB 53 Steeles East Steeles Ave E & Pharmacy Ave/Esna Park Dr WB 53 Steeles East Steeles Ave E & Warden Ave EB 53 Steeles East Steeles Ave E & Birchmount Rd EB 53 Steeles East Steeles Ave E & Birchmount Rd WB 53 Steeles East Lawrence Ave E & Scarborough Golf Club Rd EB 54 Lawrence East Lawrence Ave E & Scarborough Golf Club Rd WB 54 Lawrence East Lawrence Ave E & Bellamy Rd N EB 54 Lawrence East Lawrence Ave E & Markham Rd EB 54 Lawrence East Steeles Ave W & Carpenter Rd/Private Access EB 60 Steeles West Sheppard Ave W & Oakdale Rd EB 84 Sheppard West CLUSTER 2 52 Lawrence Lawrence Ave W & Marlee Ave/Private Access WB West 52 Lawrence Lawrence Ave W & Caledonia Rd EB West 52 Lawrence Lawrence Ave W & Caledonia Rd WB West Steeles Ave E & Victoria Park Ave WB 53 Steeles East

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Lawrence Ave E & Port Union Rd WB 54 Lawrence East Lawrence Ave E & Birchmount Rd WB 54 Lawrence East Lawrence Ave E & Kennedy Rd WB 54 Lawrence East Lawrence Ave E & Midland Ave EB 54 Lawrence East Lawrence Ave E & Midland Ave WB 54 Lawrence East Lawrence Ave E & Brimley Rd WB 54 Lawrence East Lawrence Ave E & Bellamy Rd N WB 54 Lawrence East Steeles Ave W & Fenmar Dr/Pine Valley Dr EB 60 Steeles West Steeles Ave W & Alness St EB 60 Steeles West Steeles Ave W & Carpenter Rd/Private Access WB 60 Steeles West Steeles Ave W & Keele St EB 60 Steeles West Sheppard Ave E & Brimley Rd EB 85 Sheppard East CLUSTER 3 Steeles Ave E & Warden Ave WB 53 Steeles East Lawrence Ave E & Victoria Park Ave WB 54 Lawrence East Lawrence Ave E & Pharmacy Ave WB 54 Lawrence East Lawrence Ave E & Warden Ave WB 54 Lawrence East Lawrence Ave E & Markham Rd WB 54 Lawrence East Steeles Ave W & Alness St WB 60 Steeles West Steeles Ave W & Keele St WB 60 Steeles West Steeles Ave W & Dufferin St WB 60 Steeles West Steeles Ave W & Bathurst St WB 60 Steeles West Bayview Ave & Sheppard Ave E WB 85 Sheppard East Sheppard Ave E & Leslie St WB 85 Sheppard East CLUSTER 4 52 Lawrence Bathurst St & Lawrence Ave W WB West 52 Lawrence Weston Rd & Lawrence Ave W WB West Kennedy Rd & Steeles Ave E WB 53 Steeles East Lawrence Ave E & McCowan Rd WB 54 Lawrence East Lawrence Ave E & Victoria Park Ave EB 54 Lawrence East Lawrence Ave E & Birchmount Rd EB 54 Lawrence East Steeles Ave W & Islington Ave EB 60 Steeles West Steeles Ave W & Dufferin St EB 60 Steeles West Sheppard Ave W & Jane St EB 84 Sheppard West Sheppard Ave W & Wilson Heights Blvd EB 84 Sheppard West Bathurst St & Sheppard Ave W EB 84 Sheppard West Bathurst St & Sheppard Ave W WB 84 Sheppard West

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Sheppard Ave E & Morningside Ave WB 85 Sheppard East Markham Rd & Sheppard Ave E WB 85 Sheppard East Sheppard Ave E & Victoria Park Ave WB 85 Sheppard East CLUSTER 5 Finch Ave E & Warden Ave EB 39 Finch East Finch Ave E & Pharmacy Ave EB 39 Finch East Finch Ave E & Birchmount Rd EB 39 Finch East Brimley Rd & Finch Ave E EB 39 Finch East Brimley Rd & Finch Ave E WB 39 Finch East Finch Ave E & Midland Ave EB 39 Finch East Finch Ave E & Bridletowne Crcl E EB 39 Finch East Finch Ave E & Bridletowne Crcl E WB 39 Finch East Finch Ave E & Bridletowne Crcl W EB 39 Finch East Finch Ave E & Bridletowne Crcl W WB 39 Finch East Finch Ave E & Tapscott Rd EB 39 Finch East Finch Ave E & Tapscott Rd WB 39 Finch East Finch Ave E & Middlefield Rd EB 39 Finch East Finch Ave E & Middlefield Rd WB 39 Finch East Finch Ave E & Neilson Rd WB 39 Finch East 52 Lawrence Lawrence Ave W & Duplex Ave/Jedburgh Rd EB West 52 Lawrence Lawrence Ave W & Duplex Ave/Jedburgh Rd WB West 52 Lawrence Lawrence Ave W & Marlee Ave/Private Access EB West Steeles Ave E & Midland Ave WB 53 Steeles East Lawrence Ave E & The Donway W EB 54 Lawrence East Lawrence Ave E & The Donway W WB 54 Lawrence East Steeles Ave W & Signet Dr/Weston Rd EB 60 Steeles West Steeles Ave W & Kipling Ave EB 60 Steeles West Steeles Ave W & Weston Rd EB 60 Steeles West Steeles Ave W & Weston Rd WB 60 Steeles West Steeles Ave W & Martin Grove Rd EB 60 Steeles West Sheppard Ave W & Wilson Heights Blvd WB 84 Sheppard West Sheppard Ave W & Wilmington Ave/Faywood Blvd EB 84 Sheppard West Sheppard Ave W & Wilmington Ave/Faywood Blvd WB 84 Sheppard West CLUSTER 6 Victoria Park Ave & Finch Ave E EB 39 Finch East Victoria Park Ave & Finch Ave E WB 39 Finch East

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Kennedy Rd & Finch Ave E EB 39 Finch East Finch Ave E & Midland Ave WB 39 Finch East Finch Ave E & Neilson Rd EB 39 Finch East Bayview Ave & Finch Ave E EB 39 Finch East Bayview Ave & Finch Ave E WB 39 Finch East 52 Lawrence Avenue Rd & Lawrence Ave W EB West 52 Lawrence Bathurst St & Lawrence Ave W EB West 52 Lawrence Dufferin St & Lawrence Ave W EB West 52 Lawrence Dufferin St & Lawrence Ave W WB West Steeles Ave E & Don Mills Rd EB 53 Steeles East Steeles Ave E & McCowan Rd WB 53 Steeles East Steeles Ave E & Middlefield Rd EB 53 Steeles East Markham Rd & Steeles Ave E EB 53 Steeles East Markham Rd & Steeles Ave E WB 53 Steeles East Keele St & Sheppard Ave W EB 84 Sheppard West Keele St & Sheppard Ave W WB 84 Sheppard West Sheppard Ave E & Morningside Ave EB 85 Sheppard East Sheppard Ave E & Brimley Rd WB 85 Sheppard East Sheppard Ave E & Neilson Rd EB 85 Sheppard East Sheppard Ave E & Neilson Rd WB 85 Sheppard East Sheppard Ave E & Malvern St/Progress Ave EB 85 Sheppard East Markham Rd & Sheppard Ave E EB 85 Sheppard East Sheppard Ave E & Warden Ave EB 85 Sheppard East Sheppard Ave E & Warden Ave WB 85 Sheppard East Sheppard Ave E & Birchmount Rd EB 85 Sheppard East Sheppard Ave E & Birchmount Rd WB 85 Sheppard East Sheppard Ave E & Kennedy Rd EB 85 Sheppard East Sheppard Ave E & McCowan Rd EB 85 Sheppard East Sheppard Ave E & McCowan Rd WB 85 Sheppard East CLUSTER 7 Steeles Ave E & Brimley Rd EB 53 Steeles East Steeles Ave E & Brimley Rd WB 53 Steeles East Lawrence Ave E & The Donway E EB 54 Lawrence East Morningside Ave & Lawrence Ave E EB 54 Lawrence East Morningside Ave & Lawrence Ave E WB 54 Lawrence East

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Steeles Ave W & Signet Dr/Weston Rd WB 60 Steeles West Sheppard Ave W & Chesswood Dr WB 84 Sheppard West CLUSTER 8 Finch Ave E & Willowdale Ave EB 39 Finch East Finch Ave E & Willowdale Ave WB 39 Finch East Finch Ave E & Don Mills Rd WB 39 Finch East 52 Lawrence Avenue Rd & Lawrence Ave W WB West 52 Lawrence Black Creek Dr & Lawrence Ave W EB West 52 Lawrence Jane St & Lawrence Ave W EB West 52 Lawrence Jane St & Lawrence Ave W WB West Steeles Ave E & McCowan Rd EB 53 Steeles East Kingston Rd & Lawrence Ave E WB 54 Lawrence East Lawrence Ave E & Port Union Rd EB 54 Lawrence East Steeles Ave W & Jane St EB 60 Steeles West Sheppard Ave E & Kennedy Rd WB 85 Sheppard East Sheppard Ave E & Midland Ave WB 85 Sheppard East CLUSTER 9 Leslie St & Finch Ave E EB 39 Finch East Leslie St & Finch Ave E WB 39 Finch East 52 Lawrence Keele St & Lawrence Ave W EB West 52 Lawrence Keele St & Lawrence Ave W WB West Steeles Ave E & Bayview Ave EB 53 Steeles East Steeles Ave E & Bayview Ave WB 53 Steeles East Kingston Rd & Lawrence Ave E EB 54 Lawrence East Lawrence Ave E & Kennedy Rd EB 54 Lawrence East CLUSTER 10 Markham Rd & Finch Ave E EB 39 Finch East 52 Lawrence Black Creek Dr & Lawrence Ave W WB West Steeles Ave E & Don Mills Rd WB 53 Steeles East Steeles Ave E & Victoria Park Ave EB 53 Steeles East Kennedy Rd & Steeles Ave E EB 53 Steeles East Steeles Ave W & Islington Ave WB 60 Steeles West

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Steeles Ave W & Jane St WB 60 Steeles West CLUSTER 11 Steeles Ave E & Hwy 404 NB Off Ramp/Woodbine Ave EB 53 Steeles East Lawrence Ave E & The Donway E WB 54 Lawrence East Lawrence Ave E & Orton Park Rd EB 54 Lawrence East Steeles Ave W & Norfinch Dr/400 N Finch W Ramp WB 60 Steeles West Steeles Ave W & Founders Rd WB 60 Steeles West Steeles Ave W & Kipling Ave WB 60 Steeles West Sheppard Ave W & Chesswood Dr EB 84 Sheppard West Sheppard Ave W & Senlac Rd EB 84 Sheppard West CLUSTER 12 Steeles Ave E & Leslie St WB 53 Steeles East Lawrence Ave E & Don Mills Rd EB 54 Lawrence East Lawrence Ave E & Don Mills Rd WB 54 Lawrence East Highway 27 & Steeles Ave W EB 60 Steeles West Highway 27 & Steeles Ave W WB 60 Steeles West Sheppard Ave E & Midland Ave EB 85 Sheppard East CLUSTER 13 Finch Ave E & Don Mills Rd EB 39 Finch East Lawrence Ave E & McCowan Rd EB 54 Lawrence East Lawrence Ave E & Pharmacy Ave EB 54 Lawrence East Lawrence Ave E & Warden Ave EB 54 Lawrence East Lawrence Ave E & Brimley Rd EB 54 Lawrence East Steeles Ave W & Bathurst St EB 60 Steeles West Sheppard Ave E & Victoria Park Ave EB 85 Sheppard East CLUSTER 14 Kennedy Rd & Finch Ave E WB 39 Finch East Finch Ave E & Warden Ave WB 39 Finch East Finch Ave E & Pharmacy Ave WB 39 Finch East Finch Ave E & Birchmount Rd WB 39 Finch East Finch Ave E & McCowan Rd EB 39 Finch East Finch Ave E & McCowan Rd WB 39 Finch East Markham Rd & Finch Ave E WB 39 Finch East Steeles Ave E & Middlefield Rd WB 53 Steeles East Steeles Ave W & Fenmar Dr/Pine Valley Dr WB 60 Steeles West Steeles Ave W & Martin Grove Rd WB 60 Steeles West Sheppard Ave W & Jane St WB 84 Sheppard West Sheppard Ave E & Malvern St/Progress Ave WB 85 Sheppard East

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CLUSTER 15 52 Lawrence Weston Rd & Lawrence Ave W EB West 52 Lawrence Lawrence Ave W & Hickory Tree Rd/Little Ave EB West 52 Lawrence Lawrence Ave W & Hickory Tree Rd/Little Ave WB West Steeles Ave E & Midland Ave EB 53 Steeles East Steeles Ave E & Hwy 404 NB Off Ramp/Woodbine Ave WB 53 Steeles East Lawrence Ave E & Orton Park Rd WB 54 Lawrence East Steeles Ave W & Norfinch Dr/400 N Finch W Ramp EB 60 Steeles West Steeles Ave W & Founders Rd EB 60 Steeles West Sheppard Ave W & Oakdale Rd WB 84 Sheppard West Sheppard Ave W & Senlac Rd WB 84 Sheppard West

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8.6.2 Cluster Maps

Figure 8-15: Locations of approaches in Cluster 0

Figure 8-16: Locations of approaches in Cluster 1

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Figure 8-15: Locations of approaches in Cluster 2

Figure 8-16: Locations of approaches in Cluster 3

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Figure 8-17: Locations of approaches in Cluster 4

Figure 8-18: Locations of approaches in Cluster 5

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Figure 8-19: Locations of approaches in Cluster 6

Figure 8-20: Locations of approaches in Cluster 7

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Figure 8-21: Locations of approaches in Cluster 8

Figure 8-22: Locations of approaches in Cluster 9

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Figure 8-23: Locations of approaches in Cluster 10

Figure 8-24: Locations of approaches in Cluster 11

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Figure 8-25: Locations of approaches in Cluster 12

Figure 8-26: Locations of approaches in Cluster 13

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Figure 8-27: Locations of approaches in Cluster 14

Figure 8-28: Locations of approaches in Cluster 15

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8.7 Regression Trees

Figure 8-29: Regression tree for average morning operating speed

Figure 8-30: Regression tree for average evening operating speed

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Figure 8-31: Regression tree for average morning signal delay

Figure 8-32: Regression tree for average evening signal delay

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Figure 8-33: Regression tree for average morning segment delay

Figure 8-34: Regression tree for average evening segment delay