INTELLIGENT PUBLIC TRANSPORTATION SYSTEM PLATFORM IN A
UNIVERSITY SETTING
A Dissertation
Presented to
The Graduate Faculty of the University of Akron
In Partial Fulfillment
of the Requirements for the Degree
Doctoral of Philosophy
Alaa Alghwiri
August, 2017 NTELLIGENT PUBLIC TRANSPORTATION SYSTEM PLATFORM IN A UNIVERSITY SETTING
Alaa Alghwiri
Dissertation
Approved: Accepted: ______Advisor Department Chair Dr. Shengyong Wang Dr. Sergio Felicelli
______Committee Member Dean of the College Dr. Sergio Felicelli Dr. Donald Visco
______Committee Member Dean of the Graduate School Dr. Chen Ling Dr. Chand Midha
______Committee Member Date Dr. Ping Yi
______Committee Member Dr. Richard Einsporn ABSTRACT
Modeling complex systems plays a significant role in decision supports for stochastic processes such as the transportation system. Furthermore, the transportation system in an urban university setting poses additional challenges due to the dynamic nature of the user groups (mainly students) as many factors including the living arrangements and class schedules of the students influence the demands for on-campus transportation. In this research initiative, a university shuttle system was simulated using discrete event simulation modeling to understand the systems dynamics, identify system bottlenecks, and recommend improvement strategies. Specifically, the passengers waiting times as well as the number of reneged passengers were analyzed at each bus stop. After modeling and validating the current transportation system scenario, several hypothetical scenarios were investigated and quantitative comparison was made to optimize the transportation system key performance indicators. Furthermore, forecasting methodology was used to estimate the transportation system future demands at each bus stop to be able to do demand planning ahead of time and avoid any expected service interruptions.
ii TABLE OF CONTENTS
Page LIST OF FIGURES ...... vi
LIST OF TABLES ...... viii
CHAPTER
I. INTRODUCTION ...... 1
Research Motivation…………………………………………………………… 1
Research Objectives……………………………………………………………. 2
II. LITERATURE REVIEW AND RESEARCH METHODOLOGY ...... 3
Public Transport System Design……………………………………………….4
Public Transport System Improvement and Optimization……………………...5
Public Transport System Quality ……………………………………………… 6
Public Transport Passenger Prediction……………………………………. 7
Public Transport Traffic Prediction……………………………………….. 8
Related Work…………………………………………………………………... 9
Research Methodology……………………………………………………….. 14
III. PUBLIC TRANSPORTATION AT THE UNIVERSITY OF AKRON ...... 20
Campus Overview……………………………………………………………. 20
iii Campus Shuttle (Roo Express) Service………………………………………. 20
Roo Express Main Routes………………………………………………...21
IV. MODEL DEVELOPMENT AND CONSTRUCTION ...... 27
Description of Bus Route Operation and Assumptions………………………. 27
Modeling Process Overview………………………………………………….. 29
Modeling Objects………………………………………………………... 29
Data Driven Model……………………………………………………… 31
Simulation Process Controls…………………………………………….. 33
V. ANALYSIS OF THE IPTS DYNAMIC INPUTS ...... 42
Polsky Express Route Dynamic Variables…………………………………… 42
Polsky Express Arrival Rate Analysis………………………………………... 43
Dwell Time Analysis…………………………………………………………. 45
Travel Time between Stops Analysis………………………………………… 46
Passengers Origin- Destination (OD) Analysis………………………………. 49
VI. DEMAND FORECASTING ...... 51
Polsky Express Route Demand Forecasting………………………………….. 51
Polsky Stop Demand Prediction………………………………………... 52
VII. MODEL VALIDATION, EVALUATION, RESULTS ...... 61
Model Validation……………………………………………………………... 61
iv Model, And Forecasting Evaluation………………………………………….. 63
Current IPTS Model Evaluation………………………………………... 63
Forecasted IPTS Model Evaluation…………………………………….. 64
Model Alternative Scenarios…………………………………………………. 66
VIII. CONCLUSION AND FUTURE WORK ...... 68
Conclusion……………………………………………………………………. 68
Future Work…………………………………………………………………... 69
BIBLIOGRAPHY ...... 71
APPENDIX ...... 75
v LIST OF FIGURES
Figure Page
2.1 Influential factors for IPTS ...... 15
2.2 Research Methodology ...... 19
3.1 Polsky Express Route ...... 22
3.2 Metro Dash Route ...... 23
3.3 South Route ...... 24
3.4 East Route ...... 26
4.1 Bus movement between stops in a regular bus headway ...... 27
4.2 Dynamic interaction between bus and passengers ...... 28
4.3 Simulation model main objects ...... 30
4.4 Passengers’ destination process control ...... 34
4.5 Simulation Process ...... 35
4.6 Time of arrival to a stop process in the simulation model ...... 36
4.7 Passengers’ waiting time process...... 37
4.8 Number of unserved passengers’ process ...... 38
4.9 Number of reneged passengers’ process ...... 39
5.1 Polsky route arrival rate ...... 44
5.2 Traffic between Different Stops ...... 47
5.3 Cycle Time Box Plot ...... 47
5.4 Comparing Cycle Time between Different Day ...... 48 vi 5.5 OD Percentages ...... 50
6.1 Polsky Demands Box Plot ...... 52
6.2 Polsky Demand Run Chart...... 53
6.3 acf and pacf for the Polsky Stop demands ...... 53
6.4 Polsky Demand Run Chart with Non-Zero Values...... 54
6.5 acf and pacf graphs for the Polsky Building Stop...... 54
6.6 Residulas analysis for the Polsky Building stop ...... 58
6.7 Forecasting for the Polsky Building stop ...... 59
6.8 Forecasted vs. Actual for the Polsky Building Stop ...... 60
7.1 Forecasted vs. Current Arrival Rates ...... 65
vii LIST OF TABLES
Table Page
1. Simulation applications in the field of Engineering ...... 17
2. Passenger arrival rate table in the simulation model ...... 32
3. Passengers destination table in the simulation model ...... 32
4. Route sequence table in the simulation model ...... 33
5. Dwell times in the Polsky Express route ...... 45
6. OD Percentages ...... 49
7. Stationarity Test ...... 56
8. Forecasting table for the Polsky Building stop ...... 59
9. Passengers average waiting time in Minutes ...... 62
10. Number of Unserved Passengers at the Polsky Express Route ...... 63
11. Maximum forecasted values for the Polsky Express route ...... 64
12. Current Model vs. Forecasted Model KPIs...... 65
13. Alternative Scenarios Analysis with Respect to IPTS KPIs ...... 66
viii CHAPTER 1
INTRODUCTION
1.1. Research Motivation
University campuses are now considered small cities because of the growing number of academic buildings and the increasing number of commuters. This growth causes thousands of students, faculty, and staff to arrive to and depart from university campuses on a daily basis which leads to traffic congestion [1]. Campus growth will also cause a higher demand of parking spaces and create the need to build additional parking garages.
However, space availability and cost feasibility restricts the construction of parking decks, therefore, mass-transit shuttles will be required to make up for the loss in parking spaces
[2]. In an attempt to reduce the demand for parking and the environmental impact of commuting, many strategies encouraged staff and students to use active transportation modes such as walking, cycling, and public transport [3] . However, these alternatives are not feasible for areas that experience cold weather often. In addition, other influential factors play a role in commuter decision in such a diverse and complex process. These factors will be covered as a part of this research scope and motivation. For a public transport system, the uncertainty of the future demand and the factors that influence this demand makes the study complex enough to be an interesting research field [4]. The urban
Public Transport System (UPTS) is continuously expanding particularly after incorporating many new technological methodologies such as Automatic Vehicle Location
(AVL) and Automatic Passenger Count (APC).
1 The purpose of these technological advances is to generate a more reliable and sustainable urban public transport system or Intelligent Public Transport System (IPTS).
1.2. Research Objectives
Our research objectives in this proposal are described as follow:
1. Develop a robust and scalable Intelligent Public Transportation System (IPTS) in one
platform
2. Understand the overall system influential factors and incorporate them in a modeled
IPTS to ultimately have full control over the process settings to ensure reliability and
sustainability
3. Utilize the IPTS to allocate resources proactively to follow the overall benefits:
3.1. Fleet Size (Number of Buses Per Route)
3.2. Fleet characteristics (Total Number of Passengers / Bus)
4. Conduct resources optimization analysis to fit with the peak time needs
5. Evaluate the current IPTS performance using the following performance measures:
5.1. Passenger Waiting Time or Bus Headway
5.2. Daily number of served passengers
5.3. Number of unused spaces per bus
5.4. Reneging (will be discussed in details later)
5.5. Bus bunching (will be discussed in details later)
6. Predict the demand of the IPTS if any influential factors on campus change ,i.e. rise or
fall in enrollment
7. Analyze the impact of changing the first stop for each bus route and determine the best
setting for the overall benefit
2 CHAPTER 2
LITERATURE REVIEW AND RESEARCH METHODOLOGY
University campuses represent a major trip attractor for thousands of students, staff, and faculty arriving and departing on a daily basis [1]. The increasing number of commuters causes serious issues such as high traffic congestion which affects cities near university campuses and contributes to the rise in fuel consumption. As a result, many strategies are being discussed that intend to help relieve traffic congestion near campuses, find superior ways to construct new parking decks, reduce fuel consumption and consequently reduce the environmental impact. For example, the environmental impact of pollution and degradation caused by the universities in the form of energy and material consumption via different activities can’t be ignored.
Importantly, a framework to achieve campus sustainability was discussed [5].
Furthermore, a study was conducted in 2003 which discussed the strategies to meet the federal requirements concerning air quality, increasing congestion, lack of land for parking, and the high cost of parking structures. The study concluded that to effectively deal with campus traffic, seven measures are proposed such as Transportation demand management, organization, planning etc. [6]. Another research proposed reducing private vehicle usage on campuses to avoid traffic congestion and reduce fuel consumption by encouraging the usage of public transit [7]. Moreover, a thorough survey analysis has been conducted to clearly understand and investigate all the barriers and motivators affecting travel decisions, and what universities could do to encourage a shift from private vehicle usage 3to active modes [3]. Public transport literature will be categorized into three sections: Public Transport
System (PTS) design, improvement and optimization, and demand quality. The demand quality will also be divided into passengers and traffic prediction.
2.1 Public Transport System Design
In the field of transportation system design, researchers concentrated on the Vehicle
Routing Problem (VRP) or the bus route generation which are very similar. Both issues were extensively studied using the applications of operations research [8]. The VRP is faced on a daily basis by thousands of companies and organizations that deliver or collect goods or people [9]. In an attempt to get the near optimal solutions for the VRP, time windows were discussed using various heuristic methods. Tan and Lee in 2001 mentioned that the objective of the heuristic solution for the VRP is to serve a number of customers in a predefined time frame at minimum cost based on some constraints such as the vehicle capacity and the total trip time [10]. On the other hand, another approach discussed the
Multi-Depot routing problem by utilizing two-stage and one-stage algorithms to solve the problem. Generally, the one-stage solution framework allowed users to smoothly add constraints and variants to the algorithm [11]. Another study published in 2013 from the
University of Virginia which covered the design strategy for campus parking and transportation systems. The main goal of the study was to find a strategy to meet the continuous loss in parking spaces as a consequence of the campus growth. They concluded that, it is not feasible to build new parking decks because there is already a shortage of spaces on campus and the cost would be exorbitant. Therefore, it is most beneficial to increase public transit ridership by covering new areas. A decision support tool was
4 developed to help estimate the ridership for any new proposed area to finally balance the expected ridership with the parking deficit [2].
2.2. Public Transport System Improvement and Optimization:
For improvement and optimization purposes, researchers benefit from modeling complex systems to provide a relatively low-cost way of gathering information for decision making in many fields. For example: engineering, management, telecommunications, and transportation are all utilizing the modeling approach to run hypothetical scenarios in the purpose of optimizing certain performance measures [12]. Seanego participated in improving the bus service system in the University of Pretoria by utilizing discrete-event simulation modeling. In the model, the bus service was simulated to assist in delivering decision support for a future bus system expansion into new areas taking into account optimizing certain performance measures such as cost efficiency as well as current and future bus system reliability [13]. Another simulation optimization approach to campus bus routing with diversion was used by Wang in 2009. Wang used the simulation model to minimize the cost and reduce the passengers’ inconvenience [14]. Moreover, a discrete- event simulation model was developed to analyze the waiting time of passengers at each stop in addition to the utilization of buses by running different hypothetical scenarios to finally help in decision support [15]. Utilizing the Automatic Vehicle Location System
(AVL), a proposed study published in 2012 discussed a feasible solution to improve public transport services performance measures, specifically travel time and reliability factors for cost efficient public transport system. In the study, AVL historical data was analyzed to demonstrate public transport system inefficiencies, and bottlenecks [16]. Another paper discussed in detail the Automatic Vehicle Location (AVL) system implementation,
5 application, and benefits in the United Kingdom. AVL, which is considered part of the
Intelligent Transportation System (ITS), showed its usefulness in targeting the high occupancy and high headway buses which could provide twice as many economic benefits for buses and passengers alike. Moreover, AVL can provide a great support for real time management, control of bus fleets, the passenger information system, and bus priority [17].
To estimate the optimal cycle length of demand, Chandra and Quadrifoglio (2013) used an analytical queuing model that could help estimate the best cycle length from terminal to terminal using continuous approximation and inputs from demand data and geometrical parameters of the service area. The optimized cycle length helped increase transit ridership, reduce congestion and pollution and finally increase the livability of residential areas [18]. Another optimization research for a common issue in public transportation system discussed the bus bunching. Bus bunching minimization was studied by examining the factors affecting it which are: design head way, route length, and traffic congestion
[19].
2.3. Public Transport System Quality:
After explaining the increased negative impact of private vehicle usage on campuses, it was concluded that to tackle this issue it is essential to encourage modal change into PTS usages. This modal change will create a need for a real-time PTS in order to meet the need to arrive timely on campus by students and faculty. Many papers studies analyzed the main attributes for PTS quality in details. Ultimately, using a qualitative systematic review, it is concluded that PTS reliability and frequency are considered generally the most important attributes [20].
6 2.3.1. Public Transport Passenger Prediction
Predicting passenger volume accurately is really important for transportation demand planning. By knowing the current and predicting future demand, public transit agencies could then make decisions with respect to fleet size and frequency. A vast amount of research in the literature has focused on models to predict public transit demand based on historical data and /or real-time data. In this section, I will focus on the models that predict passenger demand using historical data. Some papers have been utilizing machine learning techniques for accurate passenger demand estimation. For example, a passenger demand prediction model was developed using a support vector machine in 2007. The result showed that the prediction model is effective and it does have a stronger fitting when compared to a model developed using BP Neural Network [21]. Moreover, a comprehensive study mentioned the challenges in facing the accurate prediction for passenger demand which include Inhomogeneous, seasonal bursty periods, and periodicities. To successfully overcome all these challenges, three predictive models were developed: Time Varying Poisson Model, Weighted Time Varying Poisson Model, and
Autoregressive Integrated Moving Average Model (ARIMA). The method predicted accurately more than 78% of passenger demand on bus service in Yantai, China [7].
Another approach benefits form Raspberry Pie technology to generate real-time information about not only the vehicle location but also its passenger occupancy. This research used a GPS module to provide the location with an active antenna to ensure high accuracy in addition to passenger estimation which was accomplished by monitoring Wi-
Fi probe request from near-by smart phones [22]. Another approach used a novel information system for the improvement of productivity in public transport bus services
7 called the BusGrid. In this research the BusGrid receives real-time data from both the
Automatic Vehicle Location (AVL) and the Automatic Passenger Count (APC) and processes it using machine learning tools to finally predict the demand for the current and future planned stops [23].
2.3.2. Public Transport Traffic Prediction:
Traffic between stops on PTS is a very important aspect for reliability because it is directly affecting bus headway or frequency and ultimately real-time arrivals to bus stops.
Because of this importance, many prediction models have been developed to give a semi- accurate approximation for traffic between stops and prepare the PTS time-table accordingly. The literature is rich of studies that cover different methodologies which are being used for the prediction purposes. For example, Support Vector Regression (SVR) feasibility study was conducted in 2004 to check its accuracy in predicting travel time. As a result, the generated set of parameters predicted the travel time very well [24]. In the same year, an interesting comparison for short term traffic speed prediction accuracy was made between Artificial Neural Network (ANN) and the Support Vector Machine (SVM).
The result showed that the support vector regression performed better especially when the training data is less in quality and quantity [25]. The Autoregressive Integrated Moving
Average (ARIMA) approach was also discussed in modeling and forecasting vehicular traffic flow [26]. Another machine learning technique called Boosting was combined with
Artificial Neural Network to increase the prediction accuracy of the travel time [27]. One approach that doesn’t require a big training data to estimate the parameter necessary for classification called Naïve Bayesian classifier was tested and ultimately showed a good prediction accuracy [28]. Furthermore, a prediction model for an urban network was
8 developed by Chen, Y., H.J. Van Zuylen, and Y. Qipeng [29]. The predictive model combined both rough set and Support Vector Machine together to help improve traffic time prediction accuracy. Rough set was utilized to pre-process the traffic data with respect to noise, missing data, and inconsistent, this approach ultimately led to more accurate prediction accuracy and velocity. Discrete event simulation model was developed to reflect the new traffic regimes in Istanbul STRAIT by incorporating probabilistic behavior of vessel arrivals and other characteristics based on historical data [30].
2.4. Related Work
One of the recent research papers published in 2012 called “The Demand Determinants for Urban Public Transport Services” discussed in detail the two big challenges that face decision makers in the field of Public Transport Services which are:
How to clearly identify the factors that influence the demand
How to quantify the future demand uncertainty
This study reviewed the literature with respect to all the studies that discussed the factors that affect the demand for a Public Transport Services and summarizes the finding into two main categories: structural and external factors.
Structural Factors include
1. Cost of travel
2. Service quality
3. Travel time
4. Travel distance
5. Alternative transport mode
6. Purpose of travel
9 7. Level of transport supply
External Factors include
1. Public transport dependencies 5. City built environment
2. Demographics 6. Government policy
3. Economics 7. Behavioral factors
4. Social factors
The study stated that probably “it would be too difficult for decision makers and researchers to measure and/or quantify all the factors discussed above and include them in the models to be developed in an already complex Public Transport modeling environment”. This quote specifically proves the challenges of this field especially after including the AVL and APC Systems that give a real-time information about the PTS [4].
Besides knowing the influential factors for PTS, we still need to investigate the motivators and barriers that affect university populations’ decision on choosing to commute to campus using the PTS. For this purpose, a random sample of 2300 staff and 2200 students were generated using a random selection function in the Statistical Package for the Social
Sciences (SPSS). The survey analysis showed that the PTS travel time was the most important barrier for staff and students which will also be considered later on as a performance measure for PTS quality in the simulation model.
10 Survey analysis findings are summarized in the following strategies that would increase level of active commuting [3]:
Appropriate strategies for increasing levels of active commuting:
Reduce the travel time barrier
Increase public transport services through university subsidization of services and
by lobbying for improved services
Improve the pedestrian and bicycle network leading to campus
Provide additional student housing on or near campus
Encourage the local government body to increase the amount of and/or density of
housing in the local area
Improving the cost effectiveness of active modes
Introduce a subsidized U-pass
Increase the cost for parking relative to the cost for public transport
Address perception of travel time
Provide access to timetabling information
Evidence to support effectiveness
Reduce the barriers to the use of active modes. The most significant barrier is travel
time; therefore its reduction is of particular importance in increasing the use of
active modes
The potential to save money was rated to be the most effective motivating factor
for using active modes
Perth residents overestimate public transport journey times
11 A high proportion of staff and students in zones less than 1 Km far from campus
used active modes
With respect to the Reneging behavior, a queueing analysis with Balking and Reneging behaviors of impatient passengers was discussed in 2014 using a large-scale Monte-Carlo simulation. The paper addressed a special class of queueing problems with an orientation to PTS which have the following features and definitions:
Batch arrivals: arrives in teams rather than individually
Bulk services: customers are served by the server in teams rather than individually
Balking: customers choose not to join a queue upon their arrivals because there is
too long of a queue ahead
Reneging: the departure of a queueing customer before obtaining aimed service
This study formulated queues using compound Poisson processes and contributed in providing analytical exploration of the bulk queueing problem and the determination of the mean and the variance for the length of queue that is subject to the Balking and Reneging actions of impatient passengers [31]. This approach will be part of this paper which would be explained using Discrete Event Simulation in the research methodology. Specifically, just Reneging will be measured in this research when a bus reaches a certain stop and the capacity would not be enough to let all passengers’ board. Then Reneging is expected to happen after a certain time under this condition. Balking is hard to be measured especially in a place that runs the AVL system where passengers stay inside buildings until the bus gets close. Another stochastic modeling approach using Markov Chain Technique was formulated in 2015 for bus transit system with multiple stops. The research shed the light on a basic problem that faces PTS decision makers regarding the fleet size and the bus
12 capacity and how both directly affect the bus headway from operators and passengers perspectives. There are limitations in using this approach such as the fact that buses arrivals at stops according to fixed timetables, prohibiting early or late arrivals. Moreover, if there are no passengers boarding at stop then the bus will skip the stop which is not the case in this approach. Finally, the bus head way is assumed not to exceed 12 minutes while this constraint violates the variability in such a process, especially for long routes. The study showed a nice relationship for number of abandoned passengers as an output factor using both input factors: the fleet size and the bus capacity [32]. Another model discussed a new approach in providing real-time data from multiple sources to be then plugged into a predictive model to estimate crowding level at bus stops. This approach proposed a Wi-Fi sensor that detects passengers who enable their Wi-Fi smart phones. Information from these different sources will then be processed using a predictive model to specify the estimated time for passenger crowding so that the transit agency can dispatch a new bus to the expected crowded route before crowding occurs. However, this approach has many drawbacks such as the estimation might not be accurate since it is assuming that all passengers would have a smart phone and are utilizing their Wi-Fi service. Also, the approach works more accurately when just one route is activate at each stop. However, there may be several route passing through a particular stop. Finally, some people will not feel comfortable if a system is tracking their digital presence, infringing upon their privacy
[33]. A similar approach used the Genetic Algorithm to optimize the performance of a real- time public transport system with uncertain demands at bus stops. In the model, a hybrid predictive controller corresponds to the bus dispatcher who dynamically provides the optimal control actions to the bus system to minimize users total travel time. Uncertain
13 passenger demand was included at the model as a disturbance and the prediction is then made using on-line and off-line information of passengers’ behavior. The network was modeled as a one-way loop route with equidistant stops which is not representative for all the real case scenarios. Furthermore, passenger arrival to each station at a certain rate by following a negative exponential distribution with destinations randomly chosen among the stations ahead of the station where the passenger is boarding. Then, every passenger is characterized by origin, destination stops and by the arrival time [34]. For the purpose of providing an accurate real-time estimation of bus arrival information, an advanced Public
Transport Systems application was proposed. In the model, dwell times analysis at stops was processed and added to the model in order to improve service reliability of the Public transit at bus stops for heterogeneous traffic conditions [35].
2.5. Research Methodology
For this Intelligent Public Transport System (IPTS), we will cover two types of system inputs: static and dynamic. Static system inputs will include the inputs that are kept constant during the bus operation while the dynamic system inputs change. Static system inputs include fleet size for each route, bus capacity, and distance between stops. Dynamic system inputs include: passenger arrival rate, passengers preferences (destination) rates, available number of seats at a certain stop, and traffic between stops. To face the challenge of including all the influential factors in one model, we will need to reorder all the factors to better understand how they interact to each other’s and what performance measures are most important to evaluate the IPTS. Figure 1 shows in details the influential factors relationship:
14 Figure 1: Influential factors for IPTS
Based on the graph 1, we can clearly mention that the Renege and bus bunching situations are considered measures for IPTS system failure if they happen since both situations reflect an issue in the factors behind them. Furthermore, if both situations can be avoided, then a reliable and sustainable IPTS could be generated, which is our goal in this research. Note that for such a stochastic queuing process, the arrival pattern of passengers, the service pattern, and traffic between stops are all completely random variables and we need a semi-accurate pattern track for these variables to properly incorporate them all in one process which represent a challenge for this research. Because of the system randomness in different aspects as we mentioned earlier, no deterministic output could be obtained from modeling such a process. On the other hand, it is so essential for an
Intelligent Public Transit System to be reliable and sustainable, which requires accurate
15 system inputs and an environment that enables us to incorporate all these inputs together in one model. Such modeling would require testing and analyzing data, and might be costly if experimented on real life circumstances [36]. In such cases, simulation is uniquely capable of managing this complexity and system randomness to help us get a decision support. Simulation can be categorized into two categories: Continuous Simulation and
Discrete Event Simulation, depending on the application to be modeled. In this research
Discrete Event simulation fits the application of this research and SIMIO simulation software was chosen as a modeling environment for the IPTS for the following features:
o Powerful and relatively cheap for PCs
o Simulation-software user interfaces design, ease of use
o Object-oriented technology in simulation software improves model
flexibility, ability to model complex systems
o Publically available symbols promote 3D animation
o Growth, popularity facilitated by hardware/software advances
Moreover, SIMIO is using the OptQuest optimization tool. OptQuest is a simulation optimization engine, built on a truly unique set of powerful algorithms and sophisticated analysis techniques including metaheuristics optimization, evolutionary algorithms, tabu search and scatter search, to name a few. As a result, OptQuest enables users to shift from a “What if?” approach to a “What’s best?” approach [37].
Simulation proved its ability to mitigate many problems in different engineering fields and the following tables summarizes some previous related studies and categorizes them into three main categories as follows [36]:
16 Table 1: Simulation applications in the field of Engineering
17 Table 1: Simulation applications in the field of engineering cont.
Figure 2 below summarizes the main steps that will be done to finally have a full control of the IPTS for further analysis. Firstly, we need to generate accurate dynamic input to the model by using different kinds of distributions that properly explain the variation in the dynamic input data. Advanced Analytics will also be used to uncover the hidden insights from the dynamic input data for example, Advanced Analytics will help us track any common behavior for the arrival rate between week days which might have a direct relationship with classes scheduling. Since there will be a continuous change in these input data, a code will be written using R program which is a language and environment for statistical computing and graphics [38] to automatically do the analysis and then export the results into an Excel sheet. Secondly, SIMIO will import the Excel sheet generated from the R program to use it as an input for all kind of table required for the process.
Thirdly, after running SIMIO model, the results will be then processed again using R program to generate the final measures that will be then compared with real case measures
18 in the validation step. If the results are not valid yet then an iterative process will start between the validation step and the advanced analytics. Once the process is valid, we can then move to the final step where we can evaluate the current process based on the measures that we care about. Moreover, we can then replicate any future hypothetical scenarios and evaluate the results for any process improvement opportunities.
Figure 2: Research Methodology
19 CHAPTER 3
PUBLIC TRANSPORTATION AT THE UNIVERSITY OF AKRON
3.1. Campus Overview
The University of Akron is a public research university located in Akron, Ohio. As part of the university system of Ohio, It is regarded as a world leader in polymer research. The
University of Akron offers approximately 200 undergraduate and more than 100 graduate majors. The enrollment records are approximately 27,000 students from the United States and 71 foreign countries. The university’s best -known program is its College of Polymer
Science and Polymer Engineering. It has multiple branch campuses, such as Wayne
College in Orrville, Ohio, the Medina County University Center, located in Lafayette
Township, Ohio, and the University of Akron Lakewood, located in Lakewood, Ohio. In addition, the university hosts various nursing programs in affiliation with Lorain County
Community College under the University Partnership Program[39].
3.2. Campus Shuttle (Roo Express) Service
The Roo Express Shuttle Service has been in operation since 2009 and provides university students and faculty with an alternative transportation option. This service provides transportation to University buildings in the downtown area, neighborhoods near campus, and the parking lots on the North side of campus. During the fall and spring semesters the shuttle also services students on the weekends to and from the downtown area [40].
20 The Roo Express service at the University of Akron consists of eight total routes this year (2016-2017). However, our focus in this research will address the four main campus routes that run during the weekdays including the Polsky Express Route, the Metro Dash
Route, the South Route, and the East Route. Each route serves a unique set of stops on different sides of campus, but, the Polsky Express Route and the Dash Route have a certain shared stops in Akron downtown area. About 321,996 students were served by the Roo
Express shuttle in the fall 2013. The Roo Express shuttle service is a valuable resource that participates in reducing the number of students who live close to campus from using their cars to commute.
3.2.1. Roo Express Main Routes
3.2.1.1. Polsky Express Route
This route is considered the busiest route in the University of Akron shuttle service as it connects the main campus buildings with the west side of campus (Polsky
Building and the College of Business Administration). This route consists of only six stops beginning at the Student Union and concluding there. The expected waiting time for this route is less than 10 minutes with a fleet of two buses.
Polsky Express Stops:
1) Student Union 4) High St, Exchange
2) Near Law School 5) Starbucks
3) Polsky Building 6) Crystal Room
21 Figure 3: Polsky Express Route 3.2.1.2 Metro Dash Route
This route is considered the longest route since it contains fourteen stops and connects the University campus with the RKP metro (Public Transit) through downtown
Akron. This route’s service starts from the North side of the campus (College of Arts and
Sciences) and moves to the downtown Akron area up to the RKP metro and back. This route connects the North campus buildings with the downtown buildings as well as other student housing such as The Depot and 22 Exchange. Four buses are serving in this route to provide a good bus frequency. And these buses have a higher passenger capacity compare to Roo Express buses. This route came to operation in Fall-2016 as an agreement between the Parking and Transportation Services and the RKP metro Public Transit
Agency.
Metro Dash Route Stops:
1) College of Art and Science 3) High St., Before University Ave
2) Quaker Square at Mill Forward 4) Polsky Building
22 5) 22 Exchange 10) Depot Apartment
6) Between South Main and South 11) College of Business
High Administration
7) RKP Metro Public Transit 12) Near Morley Health Center
8) GoJo 13) Quaker At Broadway
9) Four O One Apartments 14) Quaker at Mill Backward
Figure 4: Metro Dash Route
23 3.2.1.3. South Route
This route consists of eleven stops with a wait time less than 20 minutes and fleet size of just one bus. It serves students who live on the south side of campus.
South Side Route Stops:
1) JAR Circle 7) At University Common Apts.
2) Spicer Hall on Spicer St. 8) At The Corner of Sherman St.
3) Crouse and Spicer and Wheeler St.
4) Power St. Before Brown St. 9) At South Hall
5) On Allyn St. Near Boss Park 10) University Edge Apts.
6) On Allyn Before Thorton 11) Spicer Hall on Spicer St.
Figure 5: South Route
24 3.2.1.4 East Route
This route consists of eleven stops with a wait time less than 20 minutes and fleet size of just one bus. It serves students who live on the east side of the campus to get to the campus center from the North side.
East Route Stops:
1) At the circle near Bierce Library
2) At the Fir Hill Towers on Fir Hill St.
3) At the corner of S. Adolph St. and E. Buchtel Avenue.
4) On Buchtel Avenue, west of Annadale Ave. (South side at Velvel Arms Apts.)
5) On Buchtel Avenue, near South Adams Street (South side, at the Buchtel Arms
Apts.)
6) On Buchtel Avenue, near East Market Street (South side, at the Buchtel Place
apartments, north entrance)
7) On Carroll Street, near East Market Street (north side, at the Buchtel Place
apartments, south entrance)
8) On Carroll Street, near Beaver St. (north side, at the University Rentals
apartments)
9) On Carroll Street (Carroll House apartments and University Rentals apartments)
10) At the corner of Carroll and Spicer Streets (north side, at the East Campus
Parking Deck)
11) At the James A. Rhodes Arena circle
25 Figure 6: East Route
26 CHAPTER 4
MODEL DEVELOPMENT AND CONSTRUCTION
4.1. Description of Bus Route Operation and Assumptions
At the beginning of operation each day, each empty bus is being dispatched from the designed starting point of operation which is usually a certain bus stop on a certain route.
Each bus travels along the route allowing passengers to board and alight at stops. The same service continues stop after stop from the starting time of operation until the end time of operation each day. Figure 6 shows the bus movement between stops in a route with a regular bus headway and a constant dwell time for simplicity. The vertical axis shows the distance traveled along the route while the horizontal axis shows the time spent for two purposes: the solid inclined line shows the travel time between stops while the dotted horizontal line represent the dwell time which includes both time for boarding and alighting passengers at bus stops [32].
Figure 7: Bus movement between stops in a regular bus headway
27 To better understand the dynamic interaction between bus and passengers at stops, we will discuss the following graph:
Figure 8: Dynamic interaction between bus and passengers
As indicated in the graph above, the vertical axis shows the number of passengers waiting at a certain stop which is dynamically changing along a regular day. The horizontal axis shows the discrete arrival and departures of buses at stops which includes the dwell and the headway times. The dynamic process comprises a random passenger’s arrival to each stop where they keep waiting for the bus, and during this period a passengers’ wait time is indicated in the diagram above as point C. Note that the number of passengers waiting at a stop keep increasing with time as indicated from point A to point B. When the bus arrives at a certain stop passengers are allowed to board on the bus until the bus departs
28 from the stop. At times, not all passengers can find a space on the bus and then they either stay in line waiting for the next bus or they might renege as indicated in point D. The same steps will happen at the next stops in a certain route for the full day of operation.
Model Assumptions:
1. The operation time is from 7:00 AM- 11:00 PM as it operates in the real case
scenario.
2. The passengers either board the bus or stay in line waiting for the next bus, but
might renege.
3. Buses are not allowed to pass each other such that the bus order is kept the same.
4. Bus stop capacity (Queue Length) is assumed to be infinite because of the
availability of the Automatic Vehicle Location (AVL) System.
5. Balking passengers’ analysis will not be included in the scope of this research.
6. Bus capacity variations will be considered in this model and standing passengers in
the bus is enabled for the Metro route buses and will be considered in the model
settings as well.
7. AVL provides current bus locations for all passengers who use smart phones.
8. Waiting time before Reneging for impatient passengers will depend on the bus
headways for each route.
4.3.Modeling Process Overview
4.3.1. Modeling Objects
In the case of the University of Akron Public Transportation System, bus stops were modeled as a sources objects for generating passengers to the bus queue. Bus stops are all linked together in a sequence using Time Path which is a link between nodes (bus stops)
29 and by using it, we could specify the time spent by each moving vehicle on it. Vehicle
Object was used as passenger transporter between stops; this vehicle has properties that specify vehicle capacity, speed, starting node (bus depot), loading and unloading times, and the route where the bus operates. The vehicles are also controlled by other logical properties such as routing and travel logic options to ultimately provide a better control on the vehicle behavior. Sinks Objects are being used as the passenger’s final destination where the passengers alight. Passengers were represented as model entities and mimicked the shape of human beings. The following graph shows all the model objects as they appear in SIMIO model:
Passengers Bus Queue Boarding Passengers
Alighting Passenger s
Bus
Figure 9: Simulation model main objects
30 4.3.2 Data Driven Model
Data in the typical simulation model is used to drive and control the model behavior during the model run. Discrete event simulation is flexible to either enter the data directly into the modeling object or in data tables and then have the model reference the data in those tables. The reason behind having the data table option is that we have a large amount of data and a desire to frequently change it. Moreover, it would be easier for a multiple user model to use the data table compared to directly entering the data to the modeling object. To have a robust and scalable model in our case, it’s more convenient to use the data tables as a frame work. A SIMIO data table is a data container that has a collection of user-defined columns with corresponding rows of data. Each column can hold a specific type of data such as a string, real number, Boolean, date/time, expression value, entity type, etc. Within SIMIO we can define as many tables as we like and each table can have any number of columns of different types. Moreover, we can create relations between tables such that an entry in one table can reference the data held by another table. According to all these features, we will use data tables for both the passengers’ arrival rate and the passengers’ preferences as I will explain as follows:
Passenger Arrival Rates:
A unique table was created to each bus stop to represent the arrival rate to it in an hourly basis such that the average number of passengers per hour are randomly generated from each bus stop to the stop queue using the Poisson distribution. This information was collected by the bus drivers who count the number of boarding passengers to the bus at each stop. The following table shows the rate table for the student union in SIMIO model as an example:
31 Table 2: Passenger arrival rate table in the simulation model
The first two columns in table 2 show the time period which is one hour in this research and the third column represents the average number of passenger that will be randomly generated. The starting offset represents the starting run time which is 7:00 AM every day and the ending time represents the end of the first time period.
Passengers Preferences Table:
Using this kind of table, we can determine the mix of passengers (passengers with different destinations) that will be generated to each bus stop as well as the percentages that will be generated to each mentioned stops. A unique table was built to each bus stop as shown in the table below:
Table 3: Passengers destination table in the simulation model
32 The first column represents the mix of passengers based on their destination while the second column represents the percentage that goes to each of these targeted stops. Note that the arrival rate table specifies the number of passengers that arrive to the stop each hour of operation and this preference table distributes them to their expected destinations based in the percentages proposed.
Route Sequence Table:
Each bus in operation in IPTS should have a certain route to operate through it and this is the purpose of this table. It is determining the sequence that the bus will follow as it is happening in the real case scenario. Each route has a unique sequence table and the following sequence table represents an example of it:
Table 4: Route sequence table in the simulation model
4.3.2. Simulation Process Controls
An Add-on process feature in object-based tools such as SIMIO makes it easy to rapidly build complex models. The Add-on process is a small piece of logic that can be easily inserted into the object at a selected point to perform some custom logic. This custom logic can be added to the object at a certain instance of operation to add more controls on
33 objects or to record performance measures as an output from the simulation process.
Unlike a programming insert-processes can span simulated time. For example, a process can wait for a tank to fill, a resource to become idle, or a queue to reduce to a specific size.
Hence processes are not only easier to learn, create, and understand, but they are also significantly more powerful than programming inserts.
Two kinds of Add on processes will be discussed in this model: control and output add-on processes.
Control Add-On Process:
This kind of process was used at each output node for each bus stop (Source of
Entities). We discussed earlier that each bus stop generates different kinds of entities based on their destination. To successfully send these passengers to the right destination, a process was created at this instance of modeling to check for each passenger destination when they are boarding the bus. This process consists of two basic logical objects as shown on the block diagram below:
Figure 10: Passengers’ destination process control
34 Each process starts as the begin branch and starts executing each row of logic blocks step by step. The “decide” step checks the passengers’ destinations while the
“assign” step is inserting them to the proper node for the destination. Note that the number of rows show the number of expected destinations from this bus stop. The “execute” step will be discussed later when talking about the output add-on processes.
The following diagram shows the simulation iterative process which usually starts with the conceptual design. In this step, a full understanding for the whole process concepts, so fundamentals and steps should be clear and ready for the next step. After that, we incorporate the input analysis into the simulation model where this step includes both model verification and validation. This step is considered the most important and critical one for getting accurate outputs from the analyzed inputs which makes it so iterative until getting the model validated. For each process, there should be certain baselines that will be used in the validation step to compare with. Finally, we can move to the output and experimentation which is also considered an iterative step with both the conceptual design and the input analysis. In SIMIO, it is feasible to visually watch the process run in 3-
Dimensional view which adds another verification and validation step.
Figure 11: Simulation Process [41]
35 For the verification and validation purposes, we need to measure some output variables inside the simulation model to compare them with other real measurements to finally check the model validity. The most critical and important point in simulation is to be able to prove that the model is valid and representative to the real case scenario. Note that without validating the model, no decisions could be made using the model. For this purpose, I will now talk about the output processes.
Output Processes:
We will start by mentioning that the output processes usually measures the outputs that are really important to us and we care about by modeling the whole process. In this simulation model, we care about the following:
1. Passenger waiting time at stops
2. Number of unserved passengers: Which means the number of passengers
who cannot be picked when the first bus arrives.
3. Renege situation
4. Bus bunching
We will start discussing the passengers waiting time at each bus stop. In this case, we have been recording the time when each passenger reaches the stop and send to the bus queue to wait for the bus to arrive and then board if there is space. To record the time, the following process was created:
Figure 12: Time of arrival to a stop process in the simulation model
36 The assign step shown in this add-on process records the time of creation for each passenger to finally be able to record each passengers waiting time at each bus stop.
To successfully record the waiting time, we will need to wait until the passenger find a space and board to the bus using this add-on process:
Figure 13: Passengers’ waiting time process
The assign step in this process is used to subtract the time when the passenger boarded to the bus from the time when the passenger reaches the stop which concluded the waiting time. The tally is used to record this measure for each passenger while the write step will create an excel file to finally analyze the outputs.
To be able to record the number of unserved passengers at each bus stop, the following process was created that gives the number of passengers as well as the time when it happens.
37 Figure 14: Number of unserved passengers’ process
In this process, after each vehicle alights passengers to a certain destination, we then check the remaining number of spaces in the vehicle and compare it with the number of passengers waiting at the next stop. Note that in this research we usually have multiple vehicles serve on the same route; that is why there is three rows of logic steps in this process. Each row checks and records for each separate vehicle so that we can finally have details about which bus missed passengers and at what time. Each step in each row of the process include a certain logic and math to finally get the required model outputs which are in this process the number of unserved passengers. The first “decide” step checks for the ID of the vehicle since the vehicles are numbered to distinguish between them. The second “decide” step checks if the difference between the available seats at a vehicle and
38 the number of waiting passengers at the stop. If the difference is negative, this means that a certain number of passengers cannot be served using this vehicle and should either wait for the next bus or renege. The Assign step will record the negative number of passengers who will not be served using this vehicle for further analysis. The “tally” is used to record the number of unserved passengers in the outputs if it might happen. Finally, the “write” step is used to summarize the measures in an excel sheet that includes the number of unserved passengers and the time when this occasion happens.
Passengers in the real case scenario wait at the stop for a vehicle to board to their destination. If a passenger does not find a space in the first coming bus, then it might renege either immediately or after a certain period of time if not served depending on human behavior. Renege occasion is one of the measures that we care about to evaluate IPTS. To record such a situation when it is happening, the following process was created.
Figure 15: Number of reneged passengers’ process
Each passenger that reaches the queue in the model will start waiting for a vehicle after specifying its destination. This process in Figure 15 is immediately executed to keep 39 track of each passenger behavior in line before getting served. The passenger will wait at the bus stop for a certain period before reneging. The reneging period is directly related to the bus headway and considered as a passengers perceived decision [42]. After the delay takes place to the maximum estimated time for reneging, the model will then search using the “search” step for the passenger if it’s still in the stop queue. If it is there, then the destroy step will remove it from the stop queue. Finally, the write step will report the time when this occasion happens.
After creating all the input tables and building all the add-on processes to control all model objects, we still need to make sure that the model run is properly working based on the input tables and processes logic. SIMIO has a lot of tools to go through the model run step by step. Firstly, it is possible to watch the model run in two and three dimensional view which gives us the ability to check the process flow. During the model run if I have doubt regarding a certain run time, it is applicable to use the step button that enables us to forward the run just one step each time I push the step button. Secondly, the error button which shows if there is any error in the model run but this does not cover the logical errors.
Another efficient way to check the model run is called the breakpoints. Breakpoints prohibit entities from going in a certain branch to check how the model will behave without it. Finally, the most detailed model check step is the trace button. Trace gives a detailed report of everything changed in the model such that when entities are created and their movement in the model step by step until they leave the model to their final destination.
After making sure the model run is properly working, it is feasible then to check the final results. To do so, we can fast forward the run to immediately go to the results and check everything recorded from the model. In the results, there will be a summary with a
40 pivot grid table that includes all the objects in the model with some summary statistics for some measures. A report that can also show what happen in the model in details headed by each object type.
It is now concluded that SIMIO includes many interactive tools for helping in the verification phase of a simulation project. Whether there is an error that has occurred in the model or simply or simply are stepping through the model to be sure that the logic implemented is working correctly, using a combination of the Trace window, Watch window and/or animation within the Facility window can help provide great insight into the processes that are being run and decisions that are being made within your simulation model.
41 CHAPTER 5
ANALYSIS OF THE IPTS DYNAMIC INPUTS
In this part of the research, all the IPTS dynamic variables (DV) will be discussed in details by mentioning the sources for collecting these variables which include on- site data collection process as well as the AVL system. As mentioned in the research methodology and in the data driven model parts, each route has different DV generated from different sources. The following are the DV for each route: Arrival rate to each stop, passenger’s destinations, time between stops or travel time, and dwell time for each stop. Note that the on-site data collection will help us address all the mentioned DV except for the dwell time which will be evaluated using the AVL system. These DV vary in a daily basis and therefore it needs continuous processing using a program code to track these changes and keep it up to date. 5.1. Polsky Express Route Dynamic Variables:
In this research initiatives, I will go through all the stages of quantifying the dynamic demands for the Polsky Express route. The exact same procedure will be also applied on the other routes in the bus service at the University of Akron. Note that all the other IPTS static variables (SV) will be held the same as it is depending on the current system resources. Moreover, SV will be used in the valid simulation model, later in this research, to providing a new system recommendations based on the experimented optimized conditions of these SV.
42 The on-site data collection process was conducted for a two weeks period and helped us evaluating the variations of arrivals for both the Polsky building and the Student
Union stops, the variations in traffic between stops, and passengers’ origin-destination analysis. The complement of this chapter will discuss these analyses by each DV.
5.2. Polsky Express Arrival Rate Analysis
As mentioned earlier, the bus drivers count how many passengers board the bus from each stop during the bus operation. But this number doesn’t reflect the total number of passengers that were waiting at the bus stops. For this reason, the on-site data collection process was conducted to help us evaluate the total number of passengers including the unserved as well as the reneged passengers at the Student Union and the Polsky building stops that experience the highest arrival rate in this route. Another data collection process for the rest of stops in this route will be suggested in the future work part. The following analysis shows in detail the Polsky Express route arrival rate inputs for each bus stop in the route. Note that the arrival rate for the stops at which on-site data that were not collected will be evaluated using the passenger count data. Each bus in this route makes 14 complete cycles per day. The number of arrived passengers from both buses in the same cycles was added and then the average values were calculated to finally get the arrival rate for the route stops other than the Polsky and the Student Union stops as shown in the following
Figure 16.
43 POLSKY EXPRESS MEAN ARRIVAL RATE
40 37 37 35 Polsky 35 32 Buchtel 30 21 30 30 25 29 29 StarBucks 19 20 16 17 20 Galluccii 15 12 12 11 10 9 8 9 8 10 7 11 7 6 6 5 56 5 5 6 5 5 StudentUn 3 4 4 4 34 3 34 4 34 4 3 3 5 2 2 2 2 2 2 2 ion 0 1 1 1 1 1 1 01 1 1 1 01 01 1 Buckingha 0 m 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Figure 16: Polsky route arrival rate
Figure 16 clearly shows the average number of arrived passengers at each cycle of the day to each bus stop in the Polsky Express route. These data will be incorporated in the simulation model as a rate table. The simulation model will then randomly generate passengers’ arrivals to the model stops using Poisson distribution based on these records.
Poisson distribution was verified using (stat fit) program which tested the on-site arrival data at the bus stops to check whether the arrival rate follows a Poisson distribution or not.
As we get more information about this route arrival rate, it is feasible to update these records and automatically incorporate the new results to the simulation model.
From Figure 16 above, it’s clear that the Polsky and Student Union stops arrivals are significantly high when compared to the other stops in the same route. It is also clear that both the Polsky and the Student Union stops have similar trends throughout the day except that the Polsky stop’s arrival exceeds the Student Union around the middle of the day. On the other hand, the Student Union exceeds the arrivals at Polsky in the afternoon time up to the day end.
44 5.3. Dwell Time Analysis
In order to have an estimate for the route cycle time, it is necessary to quantify both the Travel and Dwell times. The summation of them will represent the time each bus spends from the starting to the ending points of each round trip. The average Dwell Time analysis for the Polsky Express Route will be discussed in this section while the Travel Time analysis will be discussed in the next one. Dwell Time analysis was explored from the following website in November-1-2016 at 12:16 PM:
Website: https://akron.doublemap.com/admin/reports/route_dwell_time
The Double map website represents the Automatic Vehicle location (AVL) that helps students see the exact bus location in a real time on the route. This website feature enables students to stay inside the university buildings until the bus gets close to the stop.
This is so helpful for students especially in the winter season when it is cold outside.
Moreover, this website also has some reporting features that records the Travel Time between stops and Dwell Time at each bus stop. This website is now limited to providing just the average records. Hence, Table 5 shows the website results with respect to the average Dwell Times.
Table 5: Dwell times in the Polsky Express route Stop Stop Name Dwell Time (Minutes) Number
1 Student Union 0.4
2 Law School 0.6
3 Polsky Building 1.5
4 Exchange 0.5 5 Starbucks 1.1
6 Crystal Room 0.7
45 Table 5 shows the average Dwell Time analysis in a stop based. The results above were averaged to cover the period from September-1-2016 to November-1-2016. It’s clear that the Polsky stop has the maximum dwell time which reflects the high ridership at this stop when compared to any other stops in the same route. The Dwell Time records will be incorporated into the simulation model to mimic what is happening in the real case scenario.
5.4. Travel Time between Stops Analysis
Travel time variations between stops is a very important DV since the passenger waiting time at the route stops is directly proportional to the traffic between stops as well as the dwell time. For such an important factor, it is important to accurately quantify it as well as estimating how it is varying through the day. Hence, an on-site data collection process was conducted which helped us estimate the daily traffic between stops in addition to how the traffic is varying between different days of the week. Figure 17 next page shows the box plots for the traffic between all stops in the Polsky Express route. For example,
Law Student box plot represents the travel time between the Student Union and the Law school. The box plot is an informative visual representation for the traffic in this case as it clearly shows the summary statistics for the traffic variable. For the same example, the
Minimum value is 1 minute while the maximum is 5 minutes with a median of 3 minutes.
Moreover, it gives information about the data distribution, the interquartile range (the likely range of variation), and the suspected outliers.
46 Figure 17: Traffic between Different Stops
The Traffic between stops will be incorporated in the simulation model between each consecutive stops. A triangular distribution will be used to mimic the traffic variation between different stops in the Polsky Express route. Triangular distribution parameters are the Minimum, Median and the Maximum value of the variable.
As the information about both the Travel and Dwell Times is now ready, we can estimate the maximum and cycle time for the Polsky Express route using Figure 18 below.
Figure 18: Cycle Time Box Plot
The cycle time box plot indicates that the distribution is symmetric with an equal value for both the mean and the median which equals 18 minutes. The maximum waiting time in this route would be 22 minutes and the likely range for it is between 17 and 19 minutes. As it was mentioned earlier that there are two buses serving on this route for the fall/ spring semesters. Therefore, with assuming equal distance between the buses during
47 the operation, it is feasible to divide the cycle time by 2 based on the number of buses serving in the route. Ultimately, the maximum waiting time will be 11 minutes and the average will be 9 minutes. Note that these records will be used as a performance measure to validate the simulation model in the model validation and verification section.
For the simulation purposes, it is necessary to check if the cycle time varies between different weekdays. If it the statistical analysis test shows daily variation, then different week days need to be modeled separately to reflect the traffic changes during the day and the impact of it on the other model objects. Non-parametric test was chosen after finding that the population data does not follow a normal distribution. Moreover, the box plot for the cycle time in a daily basis was plotted as in Figure 19 to provide a visual cycle time comparison for the means between the weekdays.
Figure 19: Comparing Cycle Time between Different Day
ANOVA Hypothesis Test:
H0: M(Monday) = M(Tuesday) = M(Wednesday) = M(Thursday) = M(Friday) Ha: The medians are not all equal
48 The following represents Kruskal –Wallis rank test results using R program:
Based on the test results, the P-value equals 0.1479 which is higher than 0.05 level of significance. In conclusion, we can say that there is not enough evidence to reject the null hypothesis and say that the means are not equal for different weekdays.
5.5. Passengers Origin- Destination (OD) Analysis
Origin- Destination analysis is very important in determining the passengers’ destination based on the boarding stop. This information will help us evaluate the expected number of available seats at each bus stop when compared to the ridership. The optimum case that we are looking for is when there are enough seats for all passengers at each bus stop especially at the time of peak demands. The on-site data collection helped us evaluate this DV and understand the passengers’ behavior when moving between bus stops throughout the day as shown in Table 6 below:
Table 6: OD Percentages
Origin Student Law Polsky Exchange Star Crystal Union School Building apt Bucks Room Destination
Student Union 0 0 75 % 80 % 70 % 50 % Law School 0 0 0 0 10 % 10 % Polsky Building 60 % 80 % 0 0 20 % 40 % Exchange apt 20 % 10 % 5 % 0 0 0 Star Bucks 10 % 10 % 15 % 10 % 0 0 Crystal Room 10 % 0 5 % 10 % 0 0
49 Table 6 shows the detailed percentages between stops as the horizontal row represents the origin and the vertical row represents the destination. As shown in the table, the highest percentages values are usually distributed between the Student Union and the
Polsky Building stops while light destination percentages appear for other stops in the route. Figure 20 below shows a visual representation for the OD matrix with the stops names on both the x-y axis and the percentages on the z axis.
Figure 20: OD Percentages
All IPTS input DV including the Origin- Destination analysis will be incorporated into the simulation model. The model will now reflect the real case scenario after checking the performance measures for the IPTS. Once the model is valid, it is then feasible to evaluate the IPTS performance as well as running any hypothetical scenarios and evaluate their impact on the key performance indicators.
50 CHAPTER 6
DEMAND FORECASTING
Descriptive analysis usually provides information that describes the current system conditions. Knowing the current situation conditions is very important to evaluate the operation with respect to certain Key Performance Indicators (KPIs). In this research, descriptive analysis was analyzed using R program to provide accurate DV inputs that can be updated using the automated codes since the DV are continuously changing. The
Discrete Event Simulation (DES) represents an efficient tool to model stochastic processes as in the case of IPTS. Moreover, the DES is representing all IPTS objects in one platform and shows how these objects interact with each other. As in this research, the DES with all
SV and DV incorporated in it shows the interaction between passengers at each stop with the buses through the daily operation. By using the DES we are now able to run any hypothetical changes and assumed scenarios to ultimately evaluate their impact using a low cost software. On the other hand, it would make a big impact if we predict the future system
DV using historical data to move forward from descriptive analysis to prescriptive analysis.
In this research, I will utilize Time Series Analysis (TSA) to forecast the future IPTS demands at each stop in details.
6.1. Polsky Express Route Demand Forecasting:
Time Series analysis will be used to estimate the future demand for each bus stop in the Polsky Express route. Data was gathered for this route’s stops from September-1-
2016 until April-30-2017.
51 Historical data will be set up to forecast this route’s demand as I will show in details in the following section. Note that I will explain the prediction procedure in details for the Polsky Express stop while just the prediction results will be shown for the remaining stops in the appendices section.
6.1.1. Polsky Stop Demand Prediction:
At the first step, I will show the summary statistics for the historical data for the
Polsky stop demand using the box plot as shown in Figure 21:
Figure 21:Polsky Demands Box Plot
Next, the run chart, autocorrelation (acf), and the partial autocorrelation (pacf) charts for the Polsky express demand will be shown to check the data stationarity and if there is any suspected seasonality in it as shown in the following figures:
52 Figure 22: Polsky Demand Run Chart
Figure 23: acf and pacf for the Polsky Stop demands
Based on the run chart, it is clear that there are zero demand values that represent the yearly holidays and breaks in the United States. To keep the data consistent, I will replace these zero values by the mean and show both graphs according to that as follow:
53 Figure 24: Polsky Demand Run Chart with Non-Zero Values
Figure 25: acf and pacf graphs for the Polsky Building Stop
54 To check for seasonality in this case, the acf figure will be investigated to check if there are clear correlation every certain number of lags. In this case, it is clear from the acf figure that there are seasonality every five lags such that at lag 5 the autocorrelation value exceeds the blue dotted line and the same is happening at lag 10 and 15. This means that for this time series data set it is required to consider the impact of seasonality in the analysis. Note that the Polsky stop is a common stop that boards students to their classes every week and we know that the classes schedule is fixed during the semester. Moreover, while preparing the data set for TSA, I excluded the weekend days from the data set since there is no shuttle service in this route in the weekends. Consequently, the seasonality now every 5 days instead of every 7 days which makes sense. Later on, we will see the seasonality terms in the final time series function as it plays a role in the forecasting process.
Next, the weak Stationarity conditions will be discussed for the same data set. Weak stationarity has certain conditions that need to be satisfied before going forward with forecasting process. Stationarity conditions can be stated as follows:
1. E (Xt): Mean is constant over time.
2. V (Xt): Variance is constant over time.
3. Γ= cov(Xt;Xt-|h|): Covariance does not depend on time.
In R program, a handy function (Personally prepared) using available packages was utilized to check the stationarity for each time series analysis data using three common stationarity tests: Kwiatkowski–Phillips–Schmidt–Shin (KPSS) test, Augmented Dickey-
Fuller (ADF) test, and Phillipps-Perron (PP) test. Stationarity hypothesis test for both the
55 Null and the Alternative assumptions are as follows for the KPSS test and the opposite for the other two tests:
Null hypothesis: (Trend) Stationarity
Alternative hypothesis: There is a unit root.
The best scenario in this stationarity test is to have P-value more than the level of significance (which is 0.05 in this case) for KPSS test and lower than the P-value for the other tests. To check that, table 7 below shows the results of the test for both cases with and without differencing:
Table 7: Stationarity Test
Test P-value
Differencing KPSS ADF PP
Without 0.01 0.316 0.01
With 0.1 0.01 0.01
Based on Table 7 and without any differencing, PP test is satisfied while KPSS and
ADF didn’t. On the other hand, with one differencing applied on the data, all tests are satisfied. This means that we can proceed with the forecasting analysis but I will check the models in both cases just to make sure that without differencing is the ideal case for this data set. Moreover, it is feasible to check for over differencing just to have another clue that the model without differencing is the best for this data set.
Now, time series modeling starts to pick the model that will be ideal to get the forecasted data from. To get a starting point for the Seasonal ARIMA (SARIMA) model, auto.arima () command in R program was used as follows:
56 Based on this model results, note that all parameters are significant for this model.
But, it is still required to check different models to finally pick the best one. After checking all possible models, it was found that the following is the best with respect to minimizing
AIC, BIC, and AICc values:
Based on the final model results, all the parameters are significant and this represents the final model that will used for prediction. After the time series model is fit, it is required to check the residuals for correlation, heteroscedasticity, and normality. A common tests are used to check the residuals for each of these cases and specifically,
Ljung-Box test is used to check for uncorrelated residuals, McLeod-Li test is used to check for the square of uncorrelated residuals, and, for the normality, both q-q normal plot and
Jarque-Bera test are utilized. In R program, there is a command that can check for all these conditions as shown in figure 26 below:
57 Figure 26: Residulas analysis for the Polsky Building stop
The hypothesis for testing the residuals randomness conditions are the same for both tests Box-Ljung and MCleod-Li:
H0: The data are random.
Ha: The data are not random.
The results in figure 26 shows that the P-values for both tests are more than 0.05 level of significance. This means that we don’t have enough evidence to reject the null hypothesis and say that the residuals are not random. Based on this result, it is feasible that the model is ready for the prediction step. The final model that was picked for the forecasting step is Seasonal ARIMA (1, 1, 2) (1, 0, 2) [5]. A command in R called Forecast was used to predict thirty days ahead as follow:
58 Figure 27: Forecasting for the Polsky Building stop
Figure 27 shows the predicted values in details for the Polsky Express stop including two confidence intervals for both confidence levels 80% and 95%. It’s clear that the predicted data trend is consistent with the historical data. To specifically determine the predicted values, the following table is attached to show these details for both the predicted values as well as the confidence intervals:
Table 8: Forecasting table for the Polsky Building stop
59 To validate the forecasting model for this stop, both the forecasted data for April and the actual collected data were compared to each others for a twenty days a head.
Moreover, the root mean squared error(RMSE) was used as a measure to pick the model that minimizes the error and provide a better forecasting results. The follwing graph shows the trend for both the frecasted and the actual data for a tewenty days:
FORECASTED VS. ACTUAL
Actual Forecasted
500
400
300
200
100
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Figure 28: Forecasted vs. Actual for the Polsky Building Stop
Based on the graph shown above, note the both graph are following the same trend. Moreover, the forecasting data are very close to the actual up to day 8 and after that the gap between them increased a little bit. The root mean square error for the difference between both graphs equals: 265 and this model was picked after comparing the RMSE with Seasonal ARIMA ((1,1,1)(2,0,1) which have a RMSE value equals to
277.50.
60 CHAPTER 7
MODEL VALIDATION, EVALUATION, RESULTS
In this chapter the IPTS simulation model will be validated in order to be representative to the real case scenario. After that, the valid model will be evaluated with respect to the proposed performance measures as will be discussed later. Moreover, the forecasted data for each bus stop will be tested to check the prescriptive analysis for the future demand. Both models results, the current and forecasted, will be used to check how the operation is performing with respect to the model (KPIs) at the University of Akron.
After that, hypothetical scenarios for the IPTS will be suggested to check their impact on the IPTS main KPIs so that we can finally pick the scenario that fits the University of Akron demands the best. Finally, a conclusion for all the discussed analysis will be discussed in addition to the suggested future work.
7.1. Model Validation
After all the SV, DV, and the model logic were evaluated and incorporated in the simulation model, it is now ready for the validation step. To make sure that the model operates like the real case scenario, performance measures from the simulation model should match the real case operation measures.
61 In this case I will use three measures from the real operation that should be satisfied in the simulation model which are:
1. Each bus should complete 14 round trips in a daily basis
2. The average daily served passengers for the route
3. The average waiting time in the route which was evaluated by the on-site data
collection process.
The simulation model for the IPTS was used to run 20 replications which were enough number to represents the model randomness. Firstly, in the simulation model, each bus operates an average number of round trips equals to 16 cycles. Note that the extra 2 cycles at the end of the day will not affect the operation because the ridership is very low.
Secondly, the average number of daily served passengers is 613 passengers. After checking the simulation operation, it was found that the average number of served passengers for both buses is equal to 570 passengers. Finally, the average passengers waiting time matches the range in the on-site data collection and in details shown in Table 9:
Table 9: Passengers average waiting time in Minutes Stop Min Max Mean Median Stand_Dev Crystal Room 2.7 23.38 14.94 13.5 7.68 Exchange 0.06 23.86 12.36 11.04 8.46 Law School 2.88 23.74 15.66 14.76 6.78 Polsky 0.72 23.56 11.7 11.64 8.16 Starbucks 0.84 23.02 9.9 8.04 7.2 Student Union 0 23.74 10.86 10.56 7.8
Based on the validation check measures, the model represents a good approximation in representing the real case scenario. Though, this enables us to move forward to the next step to evaluate the current system service with respect to the number of unserved passengers and reneged passengers that cause service interruption and negative passengers’ feedback. Next section will show all these analyses in details.
62 7.2. Model, And Forecasting Evaluation
The most important part of this research is be able to have an accurate evaluation for the IPTS operation. Moreover, to be able to evaluate the results of any model changes that might improve the operation and satisfy the passengers’ needs. The simulation model now represents a platform that enables us to run any hypothetical scenario that might help in mitigating the passengers’ complaints. Currently, the number of unserved and reneged passengers will be evaluated for the current system operation. Also, the forecasted values will be utilized in the model to check for any service interruptions that might affect the
IPTS operation in the future in order to plan ahead of time.
7.2.1. Current IPTS Model Evaluation
The current IPTS operation will be evaluated according to the following KPIs: the number of unserved passengers and the number of reneged passengers. The difference between both measures is that not all unserved passengers immediately renege, it usually takes the passengers’ time to decide to leave the stop queue. The assumed time before reneging equals 23 minutes as it equals the max route cycle time plus one minute. In this case it means that for a complete cycle time both buses should pass a certain stop and if a passenger doesn’t find a spot in both consecutive buses will then renege.
After running the model 20 replications, the following results were generated for each KPI as shown in Table 10 below:
Table 10: Number of Unserved Passengers at the Polsky Express Route Crystal Exchange Law School Polsky Star Bucks Student Room Apt Building Union
13 18 6 94 11 85
63 Table 10 shows the number of unserved passengers from the first arrived bus at the stop by the stop name and the total number for the route equals a total of 227. On the other hand, the number of reneged passengers equals just 10.
7.2.2. Forecasted IPTS Model Evaluation
Based on the demand forecasted results for each bus stop, I will use the maximum demand value primarily for both the Student Union and the Polsky building stops which usually experience the most arrivals in the Polsky Express route. Note that the forecasting model was used to predict thirty days ahead and day 28 represented the higher arrival rate for all the stops. The maximum arrival rate for all the bus stops was chosen since it represents the worst case scenario in forecasting the demands and check this case’s impact on the model KPIs. The following table represents the values for the maximum forecasted demands for each bus stop in the Polsky Express route:
Table 11: Maximum forecasted values for the Polsky Express route
Crystal Exchange Law Polsky Student Star Bucks Room Apt School Building Union
46 97 13 273 54 282
The total number of forecasted arrivals for the Polsky Express route equals 765 arrivals in the 28th forecasted day. These arrivals will be incorporated in the valid simulation model to check their impact on the model KPIs. Figure 26 next page shows a comparison between the current model average demands with the maximum forecasted demands for the IPTS. It is clear from the figure that the maximum forecasted data shows a slight increase in the arrivals to each bus stop.
64 Current vs. Forecasted Demands
Average Demands for the Current Model Maximum Demands for the forecasted Model 273 300 282 250 200 226 225 150 97 46 100 54 13 50 75 41 0 37 10 Polsky 22exchange StarBucks Crystal Room StudentUnion Law School TOTAL DAILY ARRIVALS TOTAL DAILY POLSKY EXPRESS STOPS
Figure 29: Forecasted vs. Current Arrival Rates
After incorporating the forecasted values for each bus stop in the simulation model, their impact on the model KPIs was estimated and compared to the current model demands as shown in Table 12 below:
Table 12: Current Model vs. Forecasted Model KPIs
Number of Unserved Number of Reneged Passengers Passengers Average Current Model Settings 227 10 Maximum Forecasted Model Settings 263 37
From table 12, it is clear that both the number of unserved and reneged passengers increased for the forecasted model and this matches the increase in the demands as shown in figure 26. Based on these results, a few hypothetical scenarios will be suggested to tackle the increased number in the unserved and reneged passengers. The following section will deal with some possible alternative scenarios and their impact on the model KPIs.
65 7.3. Model Alternative Scenarios
To deal with the IPTS worst case scenario with respect to the demands, it is possible to suggest alternative scenarios to check how the IPTS operation performs according to them. The alternative scenarios will include the same system settings except some of the static variables could be changed. For example, it is possible to change the fleet size i.e. the number of buses serving in a certain route. Moreover, it is possible to change the bus capacity i.e. the total number of available seats and check the impact on the IPTS KPIs. Or it is possible to make different combinations between fleet size as well as bus capacity in each separate scenario as I will perform in this section. One option is to divert a third bus just at the times of peak demands from other routes and let it serve in the Polsky Express route[43]. The simulation model represents the platform that we can apply all these changes as it was the scope of this research. Automatic programmable R codes are also helpful to quickly analyze the simulation model results and immediately get the impact on the IPTS
KPIs as I will apply in this section. Table 13 below shows all tested scenario for the service operation and their impact on the model KPIs.
Table 13: Alternative Scenarios Analysis with Respect to IPTS KPIs
Number of Number of ID Alternative Scenario Unserved Reneged Passengers Passengers
1 3 Buses (20 Passengers/Bus) 338 27
2 3 Buses (25 Passengers/Bus) 281 14
3 2 Buses (20 Passengers/Bus) 236 37
2 Buses (25 Passengers/Bus) and 4 230 9 one on demand
66 To optimize the IPTS performance with respect to both KPIs, it is required to minimize both the number of unserved and reneged passengers together. Based on that, scenario number 4 represents the minimum number among all suggested scenarios which have the least number of both KPIs. Forecasting analysis can be done at the beginning of each month to predict the days of peak demands and use the alternative scenario to mitigate the problem and gain passengers satisfaction as long as the IPTS settings is the same.
67 CHAPTER 8
CONCLUSION AND FUTURE WORK
In this chapter, the research conclusion will be mentioned in details for this research as well as the future work discussion and suggestions.
8.1. Conclusion
The University public transportation system represents a complex stochastic
process that includes different dynamic variables such as: stops demands, traffic
between stops, and the dwell time at each bus stop.
On-site data collection process helped us understands the behavior of the system
dynamic variables and quantify them to finally get an accurate simulation model
that is representative to the real case scenario.
Discrete Event Simulation proved to be an efficient tool for representing the
University’s public transportation system. It is able to represent the interaction
between buses and passengers for each route besides its 3-dimensional visualization
that is easy to follow.
Time Series Analysis proved to be a helpful tool in forecasting future demands for
each bus stop.
Time series analysis as well as DES represented a smart methodology in the IPTS
that helped us in applying prescriptive analysis for the bus operation and plan ahead
of time to solve the operation interruptions.
68 8.2. Future Work
Collecting information about the demands and dwell times at each bus stop,
passengers’ origin- destination analysis, and the traffic between bus stops will help
us have enough historical data to understand this complex stochastic system’s
behavior as well as in forecasting future changes. Having the historical data will
help us plan a head of time for any expected service interruptions and suggest how
it might be mitigated.
Adding the agent based modeling features to the discrete event simulation will help
this research to having more accurate model with respect to the bus bunching during
the IPTS operation.
Linking the simulation model to a dynamic external excel sheets that are subjected
to continuous changes based on the continuous analysis for the system dynamic
variables. This will make it easier and quicker to simulate any new model changes
and get the results according to it.
Applying the exact same procedure to all university routes so that we can have a
complete idea about the whole campus routes operation. This will balance the
demands and supplies between campus routes accurately.
Expanding the university public transportation system to cover new areas based on
the students’ housings outside the campus will help in increasing the ridership to
the university bus service. Moreover, it will help students to move from using their
personal cars on campus into using the public transportation system to ultimately
mitigate the parking congestion on campus as well [44].
69 To be able to cover all the aspects that affect the IPTS, it will be important to discuss
the weather impact on the ridership. As this aspect depends on the human behavior,
agent based modeling will be a good tool to estimate its impact. Weather
circumstances directly affect the time the passengers can wait before reneging. For
example, some passengers might decide to walk if the weather is warm and the
distance is within the acceptable walking range. On the other hand, when the
weather is cold, passengers’ waiting time before reneging might increase because
the option of walking is not applicable.
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74 APPENDIX
Exchange Apt Stop Demands Prediction:
75 Test P-value
Differencing KPSS ADF PP
Without 0.10 0.605 0.011
With 0.1 0.01 0.01
76 77 Starbucks Stop Demand Prediction:
78 Test P-value
Differencing KPSS ADF PP
Without 0.015 0.033 0.01
With 0.1 0.01 0.01
79
80
Crystal Room Stop Demand Prediction:
81
Test P-value
Differencing KPSS ADF PP
Without 0.066 0.194 0.044
With 0.1 0.01 0.01
82
83
Student Union Stop Demand Prediction:
84 Test P-value
Differencing KPSS ADF PP
Without 0.01 0.366 0.01
With 0.1 0.01 0.01
85
86
Law School Stop Demand Prediction:
87
Test P-value
Differencing KPSS ADF PP
Without 0.10 0.06 0.01
With 0.1 0.01 0.01
88
89