Analyzing the Impacts of Driver Familiarity/Unfamiliarity at Roundabouts

A thesis presented to

the faculty of

the Russ College of Engineering and Technology of Ohio University

In partial fulfillment

of the requirements for the degree

Master of Science

Erica A. Toussant

April 2016

This thesis titled

Analyzing the Impacts of Driver Familiarity/Unfamiliarity at Roundabouts

ERICA A. TOUSSANT

has been approved for

the Department of Civil Engineering

and the Russ College of Engineering and Technology by

Deborah S. McAvoy

Associate Professor of Civil Engineering

Dennis Irwin

Dean, Russ College of Engineering and Technology 3

ABSTRACT

TOUSSANT, ERICA A., M.S., April 2016, Civil Engineering Analyzing the Impact of Driver Familiarity/Unfamiliarity at Roundabouts Director of Thesis: Deborah S. McAvoy

The number of roundabouts has increased substantially throughout the United

States, but many people remain unfamiliar about their operations. This study was conducted to determine the effect unfamiliar drivers have on the operations of a roundabout. A questionnaire was conducted in two southeast Ohio cities aimed to define how driver characteristics, particularly driver familiarity, influenced knowledge pertaining to lane assignment and priority rules. Field data was also collected in the city of Athens, Ohio and used to further investigate the operations through a microsimulation approach implemented to model familiar and unfamiliar operations of this particular roundabout in VISSIM.

The results of the study show gender and exposure to educational information do not have an effect on knowledge of roundabouts, while age, familiarity, and residence do show significant differences in driver knowledge. Drivers in younger age groups, reporting higher frequencies of use, and/or living near a roundabout tended to answer more questions correctly. In terms of field data, there were no significant differences found between the speeds and gap acceptance exhibited by unfamiliar and familiar drivers. Applying speed distributions, gap acceptance, volumes, and driver errors observed to the models, familiar drivers experienced significantly small delay times and queue lengths on all approaches of the roundabout. 4

DEDICATION

To my parents, Dan and Carol, my grandma, Patricia,

my brother and sister, Chad and Lindsay,

and my boyfriend, Jordan, for all the love and support given throughout this journey.

ACKNOWLEDGMENTS

I would first like to acknowledge and thank my advisor, Dr. Deborah McAvoy for encouragement and support during my undergraduate and graduate studies. These accomplishments, especially this master’s degree, would not have been possible without her guidance. I would also like to acknowledge and thank my other committee members, Dr. Bhaven Naik, Dr. Benjamin Sperry, and Dr. Douglas Green, for their time spent serving on my committee and for their valuable guidance and suggestions throughout these last two years.

TABLE OF CONTENTS

Page

Abstract ...... 3 Dedication ...... 4 Acknowledgments...... 5 List of Tables ...... 9 List of Figures ...... 10 Chapter 1: Introduction ...... 11 1.1 The History and Development of the Modern Roundabout ...... 11 1.2 Design Characteristics of U.S. Modern Roundabouts ...... 12 1.3 Types of Roundabouts ...... 13 1.4 Advantages of Modern Roundabouts ...... 16 1.4.1 Costs ...... 17 1.4.2 Safety ...... 18 1.4.3 Operations ...... 18 1.4.4 Environment ...... 19 1.5 Disadvantages of Roundabouts ...... 19 1.5.1 Costs ...... 19 1.5.2 Unfamiliarity ...... 20 1.5 Roundabout Location Considerations ...... 20 1.6 Study Rationale ...... 21 1.6.1 Study Objectives ...... 22 1.7 Thesis Outline ...... 22 Chapter 2: Literature Review ...... 24 2.1 Driver Behavior and Knowledge ...... 24 2.1.1 Non-Compliant Behaviors ...... 24 2.1.2 Lane Placement ...... 26 2.1.3 Driver Knowledge ...... 27 2.1.4 Public Opinion ...... 27 2.1.5 Education ...... 30 7

2.2 Roundabout Safety ...... 31 2.2.1 Crash Reductions ...... 31 2.2.2 Contributory Crash Factors ...... 32 2.2.3 Speeds ...... 34 2.2.4 Signage and Pavement Markings ...... 35 2.3 Gap Acceptance ...... 37 2.3.1 Critical Gap Estimation Techniques ...... 38 2.3.2 Implementing Maximum Likelihood Procedures ...... 40 2.3.3 Critical Gap Research ...... 42 2.4 Capacity ...... 43 2.4.1 Calculating Capacity ...... 43 2.5 VISSIM ...... 46 2.5.1 Roundabout Simulation ...... 46 2.5.2 Model Calibration ...... 47 2.5.3 Model Validation ...... 48 Chapter 3: Site Description ...... 50 Chapter 4: Methodologies ...... 58 4.1 Roundabout User Survey ...... 58 4.1.1 Survey Description ...... 58 4.1.2 Survey Questionnaire Administration ...... 60 4.1.3 Implementation ...... 61 4.1.4 Decoding Responses ...... 62 4.2 Field Data Observations ...... 62 4.2.1 Video Data ...... 62 4.2.2 Driver Directional Route and Behavioral Data ...... 64 4.2.3 Gap Acceptance Data ...... 68 4.2.4 Speed Data ...... 69 4.3 Traffic Microsimulation ...... 71 4.3.1 Model Development ...... 71 4.3.2 Microsimulation Runs ...... 78 Chapter 5: Statistical Analyses ...... 79 8

5.1 Sample Size ...... 79 5.2 Statistical Tests ...... 81 5.2.1 Correlation Coefficients ...... 81 5.2.2 Cross Tabulation ...... 82 5.2.3 Chi-Square ...... 83 5.2.4 Z-Test for Proportions ...... 84 5.2.5 Independent T-Test ...... 86 5.2.6 One-Way ANOVA ...... 87 Chapter 6: Results ...... 90 6.1 Roundabout User Survey ...... 90 6.2 Field Observations ...... 102 6.2.1 Driver Errors ...... 103 6.2.2 Speed Observations ...... 107 6.2.3 Gap Acceptance ...... 108 6.3 VISSIM ...... 112 Chapter 7: Conclusions ...... 116 7.1 Questionnaire ...... 116 7.2 Field Observations ...... 118 7.3 VISSIM ...... 119 7.4 Overall Conclusions ...... 120 7.5 Study Limitations ...... 121 References ...... 123 Appendix A: IRB Documents ...... 133 Appendix B: Recruitment Script ...... 145 Appendix C: Questionnaire...... 146 Appendix D: Questionnaire Cross Tabulation Tables ...... 151

LIST OF TABLES Page Table 1: Public Opinion Before and After Construction ...... 29 Table 2: Percent Distribution of Crashes at Single and Multilane Roundabouts ...... 33 Table 3: Factors Contributing to Roundabout Crashes in Italy ...... 33 Table 4: VISSIM Vehicle Compositions for Vehicle Routes ...... 73 Table 5: Preliminary Sample Size...... 81 Table 6: Example of Cross Tabulation ...... 83 Table 7: Questionnaire Chi-Square Results ...... 91 Table 8: The Number of Correct Lane Choice Answers by Driver Characteristics ...... 93 Table 9: The Number of Correct Car Priority Responses by Driver Characteristics ..... 94 Table 10: The Number of Correct Pedestrian Responses by Driver Characteristics ..... 94 Table 11: The Number of Correct Cyclists Responses by Driver Characteristics ...... 95 Table 12: The Number of Correct Priority Responses by Driver Characteristics ...... 95 Table 13: Z-Test Results for Lane Assignment ...... 96 Table 14: Z-Test Results for Priority ...... 98 Table 15: All Priority and Lane Assignment Questions Z-Test Results ...... 100 Table 16: Priority Rule Questions Z-Test Results ...... 101 Table 17: Sample Sizes Collected...... 103 Table 18: Total Daily and Peak Evening Hours Driver Errors ...... 106 Table 19: Driver Speed Distributions Applied to VISSIM ...... 108 Table 20: Vehicle Speeds T-Test Results ...... 108 Table 21: ANOVA Contrast Results of Accepted Gaps ...... 109 Table 22: ANOVA Contrast Results of Rejected Gaps ...... 109 Table 23: Gap Acceptance T-Test Results ...... 110 Table 24: Average Queue Length T-Test Results...... 113 Table 25: Approach Delay T-Test Results...... 114 Table 26: Intersection Delay T-Test Results ...... 114 Table 27: Highway Capacity Software Results ...... 115

LIST OF FIGURES

Page Figure 1: Modern Roundabout Design Features ...... 13 Figure 2: Mini Roundabout ...... 14 Figure 3: Single Lane Roundabout ...... 15 Figure 4: Multilane Roundabout ...... 15 Figure 5: Alternative Classes of Roundabouts...... 16 Figure 6: MUTCD Roundabout Sign Recommendations ...... 36 Figure 7: The Car Following Logic of Wiedemann 1974...... 46 Figure 8: The City of Athens, Ohio ...... 51 Figure 9: The Roundabout at Richland Avenue and SR 682 ...... 53 Figure 10: View of the 33 Approach ...... 55 Figure 11: A Closer View of the 33 Approach ...... 56 Figure 12: View of the Campus Approach ...... 56 Figure 13: View of the 682 Approach ...... 57 Figure 14: View of the Richland Approach ...... 57 Figure 15: Example from Lane Assignment Section of Questionnaire ...... 60 Figure 16: Scout Camera Visual of the Athens Roundabout ...... 64 Figure 17: Athens Roundabout Driver Errors ...... 67 Figure 18: Centerline View of Familiar and Unfamiliar Models ...... 78 Figure 19: Age Distribution of Questionnaire Participants ...... 90 Figure 20: PM Peak Hour Volume Distribution ...... 104 Figure 21: Gap Acceptance Cumulative Distribution Curve of Familiar Drivers ...... 111 Figure 22: Gap Acceptance Cumulative Distribution Curve of Unfamiliar Drivers ... 112

CHAPTER 1: INTRODUCTION 1.1 The History and Development of the Modern Roundabout

The history of the development of the modern roundabout began in 1903 with the creation of traffic circles by traffic engineer William Phelps Eno, beginning with

Columbus Circle in New York City [1]. Several years later, a French architect, Eugen

Henard, also independently developed a traffic circle in Paris [1].

The principal variance in the designs of the two engineers was the diameter of the central island. Eno recommended central island diameters of approximately five feet, while Henard insisted upon a diameter five times larger [1]. Despite the original design inspired by Eno, many traffic circles built during the 1920s in the United States were likely to have large central islands in order to mitigate the issues that arose from the inconsistent priority rules and increasing traffic volumes.

The rules pertaining to right-of-way varied based on location throughout the

United States. New York gave north- and southbound drivers priority over those traveling east- and westbound. Other states adopted the yield-to-right rule, which meant circulating vehicles yielded to drivers entering the traffic circle [1]. As the volumes of traffic increased, the traffic circles began to experience gridlock, which led to the design of larger central islands.

Larger diameters of the central islands and inconsistent priority rules caused issues in safety and capacity. These traffic circles, ultimately, created hazardous higher speeds and could not maintain capacity as volumes continually increased [1] [2]. These issues of safety and capacity led to a decrease in popularity and prompted the conversions of traffic circles to signalized intersections. 12

Despite the conversion of many roundabouts to signalized intersections, Great

Britain began the initiative to alleviate the problems associated with traffic circles by redeveloping the design. Central island diameters were kept at a relatively small diameter and signs were installed encouraging drivers on the approach legs to yield to those circulating. These changes, along with widening the entrance lanes, were officially accepted in 1966 as they were found to increase capacity and decrease delay

[1]. These innovative designs are the foundation of the modern roundabout in the

United States.

1.2 Design Characteristics of U.S. Modern Roundabouts

Design characteristics unique to roundabouts include yield-at-entry priority rule, deflection, flare, splitter and central islands, pedestrian crossings, and truck aprons, where the first three characteristics are the most notable features. The yield-at-entry rule forces drivers on the approach lanes to wait for a suitable gap in circulating traffic to safely enter the roundabout. Excess flow queues on the approaches, allowing for continuous flow of the circulating stream. Deflection is the use of small radii on the entrance and exit approaches, which guides drivers into an easy transition from the approach into the roundabout [3] and forces drivers to slow down to safely maneuver about the central island [4] [5]. The splitter island design element is typically a raised concrete island that improves deflection of vehicles and provides protection for pedestrians midway through crossing [6]. Flare is the widening of the approach, by adding another lane, so the roundabout can accommodate more vehicles. This feature 13 is meant to increase capacity at the intersection [6] [5] and keep the roadways before and after the intersection at minimum widths [6]. The last feature is the truck apron that accommodates for the turning paths of large trucks, such as fire trucks or WB 50 trucks [7]. The feature design is based on these vehicles and the central island radius.

Figure 1 shows some of the typical layout and features of a modern roundabout.

Figure 1: Modern Roundabout Design Features [8]

1.3 Types of Roundabouts

There are five types of roundabouts categorized in two classifications: conventional and alternative. Mini-, single lane, and multi-lane roundabouts are considered conventional types. Visuals of these types of roundabouts can be viewed in

Figures 2-4. Mini-roundabouts service drivers in urban areas and are designed according to a maximum entry speed of 15 to 20 mph [8]. They have an inscribed circle diameter ranging in size from 45 to 90 feet [8] [9], and include with an elevated 14 curb for a central island; this makes them preferred by truck and transit drivers as the entire central island can be traversed. Single lane roundabouts usually have inscribed diameters of 90 to 180 feet and allow for entry speeds of 20 to 25 mph [8]. With the allowance of higher speeds, single lane roundabouts are applied on rural and urban roadways [9]. The multi-lane roundabout is the third type of conventional roundabout and is categorized as such when part of the circulating roadway consists of 2 or more lanes. The typical size of the inscribed diameter of 150 to 300 feet enables drivers to enter at speeds of 25 to 30 mph [8]. Expected capacities increase with the size of the roundabout. Vehicles that can be serviced daily are approximated at 15,000 vehicles per day, 25,000 vehicles per day, and 45,000 vehicles per day for mini-, single lane, and multi-lane roundabouts, respectively [8].

Figure 2: Mini Roundabout [10]

Figure 3: Single Lane Roundabout [11]

Figure 4: Multilane Roundabout [12]

The alternative class of roundabouts includes turbo and “flower” roundabouts, which can be seen in Figure 5. Both of these can address the issues with the multilane roundabouts, including conflicts due to navigation, especially during exiting in concentric circle designs and driver changing lanes. The objectives developed in designing the turbo roundabout include lowering the entering and circulating speeds and eliminating the issues of changing lanes and yielding to three or more lanes. These 16 goals were incorporated by adding spiral alignments, traversable raised lane dividers, and second lanes on the inside of a single circulating lane at no less than one approach.

Other features in this design include small inscribed circle diameters and two lane exits on a minimum of two exits [13]. The “flower” roundabout separates right turn only lanes as slip lanes [14]. Once these lanes are detached, the remaining lanes approach a single lane roundabout meant to service through and left turning traffic. This eases the work on the drivers as they only yield to the single lane of traffic, and this also prevents weaving that can occur in multi-lane roundabouts.

(a) Turbo Roundabout (b) “Flower” Roundabout Figure 5: Alternative Classes of Roundabouts [15]

1.4 Advantages of Modern Roundabouts

Roundabouts have been shown to provide advantages over traditional intersections in terms of costs, operation efficiency, safety, and environmental concerns. 17

1.4.1 Costs

The first major benefit of roundabouts is the cost savings that occur due to implementing a roundabout. The cost of installing a roundabout may greatly depending upon the site and, often, may be more expensive than installing a signal. Construction of a signalized intersection typically costs between $0.25-0.5 million [16]. The cost of roundabouts varies across states, where Alaska reports an average cost of $0.5-0.75 million per roundabout [17] while Texas reported an estimated cost of $0.30-0.47 million on three proposed roundabouts in Frisco, TX [18]. The cost of roundabouts throughout the state of Ohio varies greatly from $0.65, $0.8, $1, $5.4, and $7.8 million in the cities of Cincinnati, Toledo, Avon, Logan and Dublin, respectively [19] [20] [21]

[22] [23]. The extensive costs of the Logan and Dublin roundabouts include multiple roundabouts for Logan and closures along with surrounding limitations for Dublin.

Under the circumstance of a higher cost associated with a roundabout, public agencies can be deterred from considering roundabouts, but jurisdictions are moving towards completing life-cycle cost analyses [24]. While the installation of a roundabout could be costly, the annual maintenance is very little, especially compared to the signals which require electricity, timing equipment, and signal hardware that ranges between 3,000-10,000 dollars annually depending on the location [8] [25].

These annual maintenance cost of a signal in addition to the construction costs may not total to the cost of the installation of a roundabout, but a life cycle cost includes the expenses associated with crashes and safety. Each state varies on the costs related with particular types of crashes. 18

1.4.2 Safety

Traffic experiences a “calming” effect as the geometric design of roundabouts pushes vehicles to reduce speeds. Between merging/diverging, turning, and crossing movements, there are 32 conflict points in a traditional intersection. This number reduces to eight at a roundabout, where there are four merging and four diverging conflict points [2] [8]. Reduced speeds and fewer conflict points decrease the number of crashes and the crash severity, and thus reduces costs to society. Drivers are more conscious of the other vehicles and vulnerable road users at lower speeds, and the counterclockwise motion means drivers only need to focus on vehicles to their left [2].

The advantages in safety extend not only to drivers but to pedestrians, as well. With the use of splitter islands, pedestrians are provided with a place of refuge as they cross the roadway. This allows pedestrians to make the crossing in two movements and focus on only one direction of traffic in each of these movements.

1.4.3 Operations

Roundabouts have also proven to be a better option to traditional intersections in terms of operations. A two lane roundabout can typically service approximately

3,500 to 5,000 vehicles per hour [26]. Volumes exceeding this are serviced better by a signalized intersection. For the roundabout intersections, decreases in delay, most often during off-peak hours, are commonly experienced [2] [8]. Increases in capacity and access management are other advantages. While roundabouts provide the common movements of an intersection (right turn, through movement, and left turn), 19 roundabouts also provide the opportunity for U-turns when access management, such as a median, prevents left turn movements from a driveway onto the roadway.

1.4.4 Environment

Another benefit of roundabouts is their increased environmentally friendly operations. The reduction in vehicle delay decreases the idle time of queued vehicles, thereby reducing the vehicle emissions, noise, and fuel consumption [8]. Not only is the air of greater quality but they are also aesthetically pleasing in that their central islands are typically adorned with natural landscapes. Roundabouts also preserve land by generally requiring less space than signalized intersections [8] [25]. The capacity of

5,000 vehicles per hour would require up to three through lanes and two left turn lanes in both directions at a signalized intersection, but a two-lane roundabout can replace this [26].

1.5 Disadvantages of Roundabouts

Even though the advantages of roundabouts are numerous, there are some disadvantages involved pertaining to costs and driver familiarity.

1.5.1 Costs

The initial installation of roundabouts can be costly and deter jurisdictions from considering them, though as mentioned earlier, the overall life-cycle benefits outweigh those costs. If the option of a roundabout is chosen, temporary traffic controls meant to maintain traffic flow can be very costly, in both terms of monetary value and vehicle travel times, during construction when a signal is converted to a 20 roundabout. Considering and implementing a roundabout during the initial construction of an intersection is an easy way to avoid this issue [27].

1.5.2 Unfamiliarity

Many drivers in the United States are uneducated on the proper knowledge of how to navigate a roundabout. This is a major disadvantage in their usefulness.

According to Shrestha, fear of using these intersections is very common and leads to high public opposition to implementation, especially among elder drivers and disabled citizens [5], and installation is difficult to justify when a community strongly opposes one.

1.5 Roundabout Location Considerations

Roundabouts have the potential to disrupt progression of signals when placed in close proximity to other intersections. While this could be seen as another disadvantage, knowledge of proper placement of roundabouts can prevent problems with progression. Intersections in a coordinated network are not appropriate sites for a roundabout. Not only is the platoon interrupted in instances of close proximity, but queues from other signals could extend to the roundabout where operations would momentarily stop and capacity would be significantly decreased. Other constraints on placement include physical complications, such as drainage, high approaching grades, utility conflicts, high truck traffic and pedestrian areas, proximity to railroad tracks, and any issues which would make construction of a roundabout economically irresponsible

[8]. The Federal Highway Administration (FHWA) lists conditions where roundabouts 21 are appropriate, which include residential subdivisions, school zone areas, rural intersections, interchanges, corridors, and commercial developments. Roundabouts are great for subdivisions and near schools due to the lower speeds and ability to safely accommodate pedestrians. Roundabouts are also a great option at rural locations where they have been shown to provide significant reductions in crash severity [8] when adopted as a safety countermeasure. The FHWA also recommends roundabouts at interchange intersections since delay can be reduced and the bridge structure in between the ramp terminals can be used more proficiently [8].

1.6 Study Rationale

In the last 25 years, roundabouts in the U.S. have substantially increased from only several roundabouts in 1992 to around 1,000 in 2007 [24] and, finally, to where there was an estimated 3,700 roundabouts in operation by December of 2013 [28]. By

2007, the state of Ohio had 15 operating roundabouts, 4 under construction, and 19 still in the design stages [29] [30]. Research has documented the improvements in operations and safety, which is typically attributed to design features such as channelized approaches, yield-at-entry priority rule, and geometric aspects. The advantages gained do not solely rely on design but upon driver understanding and behavior, as well.

A study showed navigation of roundabouts was problematic for at least one year after construction. Compared to the rate that would have been expected with a signalized intersection, there was an increase in the occurrences of property damage 22 only crashes [31]. There are guidelines for signage and pavement markings provided by the Manual on Uniform Traffic Control Devices (MUTCD). Despite the signs and pavement markings, crashes are still prevalent. Currently, the City of Athens is planning to install more roundabouts in conjunction with Ohio University. There are concerns with implementing more roundabouts due to driver unfamiliarity which could continue for quite some time as new, unfamiliar students enroll at the university.

A need exists to identify the impacts driver unfamiliarity has on roundabout performance and potential solutions to mitigating negative impacts. This study aims to identify driver knowledge through a questionnaire, acquire data regarding unfamiliar and familiar driving parameters, and identify the impacts these parameters and driver errors have on the performance of a roundabout through microsimulation.

1.6.1 Study Objectives

This study attempts to answer the following three objectives:

1. Do driver characteristics, including gender, age, area of residence, experience,

or exposure to roundabout education influence adequate knowledge of

roundabout rules and priority?

2. What behaviors are drivers exhibiting while driving through a roundabout?

3. How are capacity restraints, such as delay and queue length, impacted based on

the driver behaviors exhibited?

1.7 Thesis Outline

The remaining chapters of this thesis are organized in the following manner: 23

Chapter 2 summarizes the literature reviewed pertaining to driver behavior and safety at roundabouts, gap acceptance, and capacity, as well as modeling software.

Chapter 3 gives a description of the roundabout where data was collected and used to code various models.

Chapter 4 covers the methodologies of conducting the questionnaire, gathering different parameters needed for coding, and building the models of the roundabout in

VISSIM. Impacted

Chapter 5 discusses all of the statistical analyses used to evaluate the different sets of data collected.

Chapter 6 presents the results obtained from the data collected and the statistical analyses performed.

Chapter 7 contains the conclusions and recommendations that answer the objectives listed.

CHAPTER 2: LITERATURE REVIEW

2.1 Driver Behavior and Knowledge

Driver behaviors can have an important impact on the functionality of any transportation facility. Understanding the different levels of cognitive behaviors, as described by Martens and Fox, is important to aid drivers in an ideal manner. The three levels of behavior drivers can display are knowledge-based, rule-based, and skill- based [32]. Knowledge-based behavior is centered upon the unfamiliarity of drivers, where progressive levels of thinking have to be applied at unfamiliar locations. The other two behaviors are formulated from familiarity of drivers. Rule-based behaviors are applied when a driver understands the procedures in which to act in a given scenario. These procedures are recalled and used to navigate a roadway previously untraveled by a given driver. Skill-based behaviors are more instinctual where the task at hand has been trained into the mind of the driver to the point that the action takes a brief moment of attention. The concern lies with the drivers displaying knowledge- based behaviors because those who cannot provide solid reasoning to their behaviors could demonstrate unsafe maneuvers, potentially causing vehicle accidents.

2.1.1 Non-Compliant Behaviors

Three distinct non-compliant driver behaviors are identified at roundabouts, which include priority abstaining, priority taking, and priority surrendering [33].

Priority abstaining occurs when a vehicle rejects a suitable gap, yields to vehicles exiting on the same leg, or stops unnecessarily at the entrance of a roundabout when no vehicles are circulating. Gårder conducted a study on the state of Maine’s first 25 roundabout. This study indicated 1 of every 50 vehicles exhibited priority abstaining behaviors, particularly vehicles stopping when no circulating vehicles were present

[34]. This behavior was found to cause queues on the approaches and to have a significant relationship with exiting vehicles, where an increase in exiting vehicles would increase the number of abstaining occurrences [33]. Priority taking was seen approximately one time of every 100 vehicles entering [34], which is when an entering vehicle fails to yield to circulating drivers by accepting an inadequate gap. The third behavior of priority surrendering is an error made by vehicles already in the roundabout. These drivers stop within the roundabout and forego their right-of-way to entering drivers. Both priority taking and priority surrendering cause queues among the circulating drivers that can affect other approaches at the roundabout [33].

Other minor observed defiant behaviors included apron encroachment where two-thirds of drivers using the apron drove upon it out of convenience and not out of need. This comprised 14% of passenger cars using the roundabout [34]. In some instances, driver were seen going the wrong way around the central island. This behavior was seen, and eventually stopped in Gårder’s study, mainly due to one particular driver doing so in protest of the roundabout [34]. While this behavior is not as commonly seen as the non-compliant priority behaviors, it does present a noteworthy problem of safety. There were 12 crashes observed due to this driver error over five years [35], with no mention of potential near-misses. Another behavior is the lack of use of turn signals within a roundabout. Approximately 10 percent were found to indicate direction, though, the author declared these vehicles were more trustworthy for 26 entering vehicles to determine appropriate gaps to accept because of the use of their signals which indicated whether they would be continuing or exiting [34]. One more common non-compliant behavior is the lack of correct lane selection before entering into a roundabout, and this behavior is discussed in the following section.

2.1.2 Lane Placement

Single lane roundabouts follow a basic concept for driver navigation and are simple to use when a driver becomes familiar with the intersection. Multi-lane roundabouts are more complex, which makes correctly navigating these roundabouts difficult when these are unfamiliar circumstances. In addition to the difficulties drivers have in single lane roundabout navigation, there are decisions drivers must resolve before entering a multi-lane roundabout that would normally not be conducted for a single lane roundabout. The first decision is what lane should be entered upon. If drivers fail to choose the proper lane, they must determine if a lane change is necessary while in the roundabout or if, somehow, they will be able to exit from the lane in which they are already positioned. Many Americans are not properly experienced or educated with roundabouts, so when drivers do not know how to answer these and other navigation questions, crashes may occur.

Drivers changing lanes within the roundabout also affect those who are entering. This set of drivers encompasses more than merely those choosing the improper lane before entering. Some drivers take the shortest path by entering in the right lane, cutting into the left/center lane while circulating, and finally switching to the right lane again to exit [31]. When those in the entering lanes are uncertain in the path 27 of drivers circulating, vehicles entering from the right lane will yield to all lanes in the roundabout [36]. These behaviors not only increase the risk of a collision but also add to the difficulty of determining the performance and capacity of a roundabout, and in particular, the outside lane on an approach [36]. A simulation study of a three-lane roundabout showed that potentially 20 percent of drivers do not understand lane restrictions while circulating roundabouts, but the methodology of this study also provides the point that there were no other vehicles present in the simulation in which to discourage participants from switching lanes [31].

2.1.3 Driver Knowledge

A study conducted in Nebraska in 2007 aimed to identify relationships between characteristics of drivers and knowledge of roundabout navigation [37]. The researchers developed a survey that defined familiar, unfamiliar, specialty, and passenger car drivers. The survey then asked for driver opinions, evaluated their knowledge of proper roundabout navigation. The survey took place in five cities, where roundabouts were and were not present. Results of the study revealed familiar and specialty drivers understand the rules of roundabouts better than unfamiliar and passenger car drivers, respectively. Other drivers portraying a greater knowledge included younger generations, males, those who wear their seat belts, and those who alter their driving routes according to congestion [37].

2.1.4 Public Opinion

The introduction of the modern roundabout to the United States occurred in

1990, yet after seven years, only around 50 had been built and the majority of states had 28 yet to construct one each [38]. There have been several main concerns with building roundabouts, which include concerns of the ability of the public to comprehend the new intersection rules, whether roundabouts were safe, and whether they were effective in managing traffic. Hesitation toward implementing roundabouts is common due to the public opposition to the idea. Public opinion in surveys has shown that communities generally tend to exhibit negative perceptions toward roundabouts particularly during the period prior to construction.

Since public opposition has been a major concern with implementation, research has extended over a number of years to where several studies were conducted through telephone interviews inquiring the opinion of local roundabouts to each respective study. The following table, Table 1, shows the variations in public opinion before and after construction. The results of all three studies are fairly consistent with one another.

The first study, conducted by Luttrell et al. in 2000, polled residents in six weeks before and eight weeks after the construction of a roundabout in each community of Reno,

NV, Hutchinson, KS, and Harford County, MD. The study by Retting et al. continued the study by Luttrell et al. and further polled residents of the three mentioned communities for a 5-year follow-up. This study also surveyed residents of Greenwich,

NY, Nashua, NH, and Bellingham, WA, for a 1-year follow-up to a roundabout constructed in each of these communities in 2004. The third study by Hu et al. collected opinions before and also six and twelve months after roundabout construction in Bellingham, WA in 2009.

Table 1: Public Opinion Before and After Construction [39] [40] [31]

Concerns of familiarity and experience in roundabout navigation are shown in these polls. Respondents tended to be most strongly opposed when they had limited experience with roundabout use. As the respondents’ level of experience with respect to roundabouts increased, perceptions of efficiency, safety, and overall support all increased, as well [40]. Two of these studies also inquired as to why drivers opposed to the roundabouts felt this way. Often, the same reasons were cited for both that the drivers found the intersection to be confusing, unsafe, and/or they just preferred a signalized intersection over a roundabout [39] [40]. One more reason drivers stated for opposition was a belief that the roundabout caused more congestion, yet the study in which this remark was made found significant reductions in delay and the number of vehicles stopping [40]. Younger drivers were most supportive, and support was found to decrease consistently with age. Older drivers comprised a high percentage of respondents that opposed roundabouts, where some acknowledged they opted for alternative routes in order to avoid trying to navigate the roundabout [31].

Several suggestions were made as to how the roundabout could be improved to help increase drivers’ support and to alleviate confusion. The most common recommendation was to provide drivers with better signage, particularly advanced 30 warning signs. This suggestion comprised 31% of the recommendations. Of the respondents, 13% offered the recommendation of more public education on roundabouts. Another 13% supported improved lighting, while wider lanes, lower speeds, and improved pavement markings accounted for 12%, 9%, and 8% of suggested improvements, respectively [40].

2.1.5 Education

Before the installation of a roundabout, various levels of public education are addressed in the surrounding local areas. This education is crucial in order to allow people the opportunity to instruct themselves and not rely solely upon the signs and pavement markings in the roundabout. There are many different approaches when educating drivers about roundabout navigation and rules, including brochures, videos, television, and the internet [24]. Typical forms include brochures and other handouts made available at meetings for the public. To reach out to more drivers not at the public meetings, brochures have been placed in local shops to be added to grocery bags

[24] or sent as flyers to residents through the mail [41]. Use of the media is another widely used medium by placing advertisements in the newspaper or airing a story and demonstration on the local news station. Public education regarding the rules for navigating a roundabout is constantly available through the internet, which even provides video demonstrations for visual learners.

This type of education is not valuable for adult drivers only. Confusion and fear of using a roundabout can be deterred by exposing younger age groups to education information. Incorporating roundabouts into the curriculum in driver education classes 31 exposes new drivers, and giving presentations to all school grade levels instills the rules of navigation into the future potential drivers [24]. Regardless of the manner of education chosen, the fact that all drivers do not use the same mediums to obtain information should be remembered. Therefore, several mediums should be chosen and implemented to target all ages of the public in order to increase the number of drivers reached [41].

2.2 Roundabout Safety

2.2.1 Crash Reductions

As stated in Chapter 1, there are eight points of conflict in a roundabout compared to the 32 of a signalized intersection. The low numbers of conflict points, along with the locations of these points and low traffic speeds, are attributed to the increases in safety at roundabouts [42]. On occasion, there have been temporary increases of crashes at roundabouts after construction due to driver unfamiliarity [38].

Despite the infrequently seen increases, the consensus of studies show reduction in crashes at the studies’ respective site locations. Three studies in particular yielded similar results in crash reduction. The Insurance Institute for Highway Safety reported a decrease of 76% in injury crashes, 90% in fatalities, and 39% in overall numbers of crashes at 24 intersections [43]. Isebrands studied 17 rural intersections and found reductions of 84%, 100%, and 52% in injury, fatal, and overall crashes, respectively

[44]. The third study, conducted by Persuad et al., reported an 80% reduction in injury crashes and a 40% overall reduction due the conversion of 23 signalized or stop- 32 controlled intersections to modern roundabouts; no data was provided regarding fatal crashes [45].

2.2.2 Contributory Crash Factors

In 2012, the number of intersection related crashes exceeded 4.7 million in the

United States, where approximately one third of these crashes resulted in an injury or fatality [46]. There are fewer crashes occurring in roundabouts; those happening tend to be less severe because serious injuries are not common in the types of crashes in roundabouts. The design of a roundabout basically eliminates the high injury and fatality rates produced by angle and head-on crashes. A crash in a roundabout will occur in one of three positions, while entering, circulating, or exiting. The data of two studies are summarized in Table 2, which shows the distribution of crash types occurring at the study sites. Mandavilli et al. observed the crashes of 38 roundabouts in

Maryland over time periods ranging from 2-14 years. The other study by Rodegerdts et al. was documented in the National Cooperative Highway Research Program Report

572, where crashes occurring in 90 roundabouts were reviewed. The table shows the more common types of crashes are single-vehicle run-off road, rear end, entering- circulating, exiting-circulating, and sideswipe accidents.

Table 2: Percent Distribution of Crashes at Single and Multilane Roundabouts [42] [47]

There are many different factors which contribute to crashes. These include, but are not limited to, factors of the road user, geometric design, pavement markings, road environment, and pavement type. Montella observed 274 crashes occurring between 2003 and 2008 at 15 roundabouts in Italy [35]. Crash reports were analyzed, and the contributory factors for each crash were identified. A summary of the distribution of the crash factors can be seen in Table 3.

Table 3: Factors Contributing to Roundabout Crashes in Italy [35]

Geometric design was the most common factor cited in these crashes for reasons of exaggerated radii of deflection at both entrances and exits, superfluous entry and circulating roadway widths, and insufficient splitter islands. Poor visibility or missing yield signs and pavement markings were another factor seen frequently in the crash reports. This led entering drivers to exhibit priority taking behavior. Reasons cited for road user factors included failure to yield, changing lanes within the roundabout, and traveling in the wrong direction about the central island. The most prevalent causes of crashes due to road environment and pavement were poor sight distance and a lack in skid resistance, respectively. The vehicle contributory factors were considered negligible with only 2 crashes attributed to vehicular malfunctions.

2.2.3 Speeds

Single-vehicle run-off road crashes are generally a large portion of the crashes occurring to roundabouts according to the data from Table 2. Deflection at the entrance of a roundabout is used to help to guide drivers to navigate the roundabout in the proper direction and to decrease drivers’ entering speeds. Deflection can be a hazard to unfamiliar drivers when combined with high approaching speeds. Speeding is a factor frequently seen in crashes. Approaching at high speeds may cause aggressive braking

[42] and, potentially, a loss of control of a vehicle where the driver crashes into the central island or drives off the road.

As drivers continue to utilize roundabouts, familiarity and recognition of safe entering speeds should increase. This idea can be seen in a study conducted by

Isebrands et al., where the approaching speeds of 19 rural roundabouts and two-way 35 stop-controlled were compared. There was a statistically significant difference between the approaching speeds selected at roundabouts versus the typically stop-controlled intersections. At a distance of 1,500 feet before the intersections, those approaching a roundabout were driving approximately 1.3 mph faster than those approaching a stop- controlled intersection. At 250 feet before the intersections, speeds on the roundabout approaches were still higher but only by an average of 0.7 mph. By 100 feet in advance, drivers preparing to enter the roundabouts were traveling 2.5 mph slower, which was attributed to the drivers being guided by splitter islands and deflection [48].

2.2.4 Signage and Pavement Markings

Despite the existence of various forms of public education pertaining to roundabout navigation, there are no formal mean by which all jurisdictions follow to ensure this education, and non-local drivers will not have encountered the education materials issued for those particular roundabouts. This is why proper and visible signage and pavement markings at roundabouts are vital. The task encountered by designers is to guide all drivers and educate the drivers who may have never come into contact with these resources before they actually enter the roundabout. These are important aspects of design that can lead to driver understanding because they can influence how a driver may enter, circulate, and exit a roundabout. Signs and markings are meant to be designed to optimize the roundabout [49]. Safety auditors have identified that some issues in roundabouts come from sign positioning, size, and quantity. Any one or more of these issues at a roundabout could cause poor guidance, 36 high approaching speeds, and a lack of driver recognition of roundabout operations

[50]. There are different signs which can address each of these problems.

Many types of signs are used for roundabouts, but there is very little published about which elicit fewer incorrect driver behaviors. The experimental phase of roundabout signage is still in progress where a better standardization is needed [51].

Markings and signs at roundabouts tend to have similar schemes, but a better consistency among jurisdictions would benefit drivers [31]. The MUTCD provides suggestions on sign and pavement marking conventions for roundabouts, which can be seen in Figure 6.

Figure 6: MUTCD Roundabout Sign Recommendations [52]

As already stated, older drivers tend to favor roundabouts less than younger drivers. In a study conducting a qualitative assessment of this demographic, the participants overall agreed there should be sufficient warning signs ahead of the intersection to convey the number of entering and circulating lanes along with suggested driving speeds. A common theme in the results showed a general distaste for yield signs as the older drivers were confused by the rules regarding yielding at a roundabout and the abundance of yield signs and pavement markings. Guide signs for circulating drivers located in the splitter island was another suggestion given [38].

2.3 Gap Acceptance

Calculating the capacity of a roundabout is crucial to the estimation of its performance. The theory behind capacity includes the calculation of critical gap and follow-up time headways. The critical gap, tc, is the minimum time gap in the major street traffic a minor street vehicle is willing to accept, and follow-up time, tf, is the time gap between two minor street vehicles entering the major street within the same gap [53]. While follow-up time can be directly measured from the field, determining the critical gap is not as straightforward because drivers behave differently. Since the critical gap is not a constant value, tc, it is represented by a distribution based on driver behaviors [54]. An inclusive critical gap of the intersection is based on behavior, geometry, and traffic conditions. Obtaining consistent and applicable estimates of these factors is important to accurately compute capacity [55]. As more models were developed to estimate this parameter, comparisons of their accuracy became necessary. 38

2.3.1 Critical Gap Estimation Techniques

The models of critical gap are created based on the intersection of two one-way streets and two-way stop-controlled (TWSC) intersections. These models can also be applied to roundabouts. This is due to the assumption that a vehicle maneuvering a right turn at TWSC intersection is a very similar motion to a vehicle yielding and making a right turn into a roundabout [56].

There are many models in existence, but the most renowned theories of critical gap include Siegloch’s Method, Lag Method, Raff’s Method, Ashworth’s Method,

Harder’s Method, Logit Procedures, Probit Procedures, Hewitt’s Method, Wu’s

Method, and Maximum Likelihood Estimation Procedures. All of these procedures are based on unsaturated conditions except for Siegloch’s Method, which is based on saturated conditions.

Several studies, including a simulation study by Brilon et al. and a roundabout study conducted by Vasconcelos et al., have compared some of the of the critical gap estimation techniques against one another. Brilon’s study compares all of the discussed procedures excluding Wu’s Method. Siegloch’s Method, Raff’s Method, Maximum

Likelihood Procedures, Wu’s Method, and Logit Procedures are considered in

Vasconcelos’ study.

Brilon et al. conducted a simulation study because this was the only method in which to verify the requirements of certain qualities each model should have. The methods should contain specific attributes in the decision-making distribution, consistency, robustness, and compatibility with capacity models [55]. Driver decisions 39 were distributed similarly to a random variable. This distribution was characterized by a minimum threshold greater than or equal to zero, an expected mean critical gap value, a standard deviation of the critical gap, and a positive skewness factor [57].

Consistency of the models demanded the models to be capable of replicating very similar values of the critical gap. Many assumptions were made in the discussed methods. For the models to meet the criteria of robustness, critical gap results could not be heavily influenced by these assumptions. The final attribute of compatibility required the methods to yield reliable estimates of capacity.

The study randomized two traffic streams, the major and minor traffic volumes.

The constant traffic volumes, qp and qn, were estimated to range between 100-900 vehicles per hour and zero to capacity, respectively. Both traffic streams consisted of

46 combinations of these traffic volumes. All combinations for each case produced a value for the critical gap and the follow-up time according to each method of critical gap calculation.

Results of the study yielded Hewitt’s Method and the Maximum Likelihood

Procedures as acceptable methods. Both calculated correct critical gap values and met consistency criteria. All other methods were rejected for various reasons. Ashworth’s

Method was unable to calculate the correct value of critical gap, leading the authors not recommend this method. Harders’ Method, Raff’s Method, and Logit Procedures did not meet consistency standards since their results were all dependent upon the major street volumes. Siegloch’s Method did produce accurate values of critical gap, but this method failed to meet the criteria standard of robustness when its results were applied 40 to a capacity model. The capacity was found to be sensitive to the major street headway distribution [55].

The second study, conducted by Vasconcelos et al., used video data from six roundabouts in Portugal to test the critical gap procedures. For a roundabout to be chosen in this study, there had to be times of continuous queuing, a simple geometric design, and no disruptions caused by pedestrians or nearby traffic lights. A summary of the results of the critical gap methods was the methods were all fairly consistent with one another. Siegloch’s Method and Logit Procedures were less satisfactory than the other three methods due inaccurate estimations of capacity found using the critical gap from these methods. Raff’s Method, Wu’s Method, and the Maximum Likelihood

Procedures all produced similar results and no clear advantage was found among any of the methods [58].

2.3.2 Implementing Maximum Likelihood Procedures

While some of the results of the two comparison studies do coincide with each other, both studies recommend the maximum likelihood procedures. Using MLE for calculating the critical gap is common due to studies repeatedly proving this to be one of the most reliable methods [57]. The Highway Capacity Manual (HCM) 2000 estimates the critical gap of a single-lane roundabout to fall between 4.1 and 4.6 seconds [56]. No estimates for multi-lane roundabouts are provided. However, the

NCHRP 572 estimates the critical gap to equal 5.1 seconds for a single-lane roundabout and 4.5 seconds for the left lane and 4.2 seconds for the right lane of a multi-lane roundabout [47]. 41

A study conducted in California analyzed data collected from seven single-lane and three multi-lane roundabouts. The maximum likelihood procedures calculated the critical gap of a single lane roundabout at 4.9 seconds [59]. This falls directly in between the HCM 2000 and NCHRP 572 Report. Results of the multi-lane roundabouts were higher than the standard values. Values of 4.8 and 4.2 were calculated for left and right lanes, respectively. Another study conducted in Stockholm analyzed the critical gap of a multi-lane roundabout using MLE. The values calculated were 3.29 and 3.58 seconds for left and right lanes [60]. These are considerably smaller than the typical values suggested by the HCM and the NCHRP report. An explanation for this could be drivers in Europe are more familiar with navigating roundabouts, and therefore, they are more comfortable accepting smaller gaps.

Despite wide acceptance of the maximum likelihood estimation procedures, this method is appropriate under times of normal conditions. There are non-normal situations that can create bias in the assessment, which include pedestrians and varying traffic flows. High pedestrian volumes can affect the results of the critical gap. Drivers on both the major and minor streets can be interrupted by pedestrians at a roundabout

[61]. Abnormal gaps may be created by briefly halting the major stream traffic, which then gives minor street vehicles opportunities to enter the roundabout in which they would not typically have be able to enter. Pedestrians could also cause a minor street vehicle to miss a gap in the circulating traffic that the driver would have accepted.

Since the maximum likelihood estimation procedures only uses drivers who reject at least one gap, the results of using this method will be biased if data is collected under 42 conditions of low conflicting traffic volumes [58]. One more source of bias for the

MLE is nearby intersections with high delay. If the major stream of the other intersection causes large queues that spill back into the intersection of interest, driver behaviors will be affected [61]. Two of these conditions were part of the criteria in selecting the roundabouts in the Portugal study, where roundabouts with pedestrians and other nearby intersections were excluded from the study.

2.3.3 Critical Gap Research

The studies using the MLE to calculate the critical gap found several factors influence the size of the critical gap. These factors include geometry, vehicle type, and major street speeds and volumes. The geometry of a roundabout, including the number of lanes and turning angle, affects the critical gap in different manners. The critical gap will increase as the number of lanes and difficulty of accepting a gap increases [60]

[62]. The left lane at a multi-lane roundabout has two lanes of conflicting traffic to consider when accepting a gap as compared to the right lane which only has one lane.

Another geometric factor is a small turn angle that allows vehicles to turn easier and reduces the value of the critical gap [62]. Vehicles are deflected around the central island in roundabouts, which means the turn angle is less than the 90 degrees of a normal two-way stop-controlled intersection (TWSC). This is why critical gaps of roundabouts tend to be smaller than TWSC intersections. The second factor, vehicle type, affects the size of the critical gap because passenger cars are smaller and are able to enter the major stream in smaller gaps that large trucks cannot safely accept [60]

[62]. The last factors of approach volume and speed of the major stream have an 43 inverse effect on the critical gap. As the volumes and speeds increase, drivers on the minor street will become more impatient and willing to accept smaller, riskier gaps [59]

[62].

2.4 Capacity

The entry capacity of a roundabout is the maximum inflow of vehicles on a given approach under a given circulating flow and is dependent upon the amount of conflicting traffic, as shown below in Equation 1. The total capacity of the roundabout, defined as the maximum inflow that can be serviced at times of saturated conditions, is dependent upon the origin-destination (O-D) traffic flow [63]. For example, high levels of left-turns will lower the full capacity of a roundabout. Several methods of predicating entry capacity at roundabouts exist, including the Tanner-Wu equation, the

U.K. model, and the computer programs GIRABASE, aaSIDRA, and RODEL. A deficiency of these methods is they all calculate the total entry capacity of an approach instead of the entry capacity of each individual lane [64]. The Highway Capacity

Manual 2010 offers methods of estimating capacity for individual lanes entering a roundabout under various circumstances.

2.4.1 Calculating Capacity

The entering capacity, according to the HCM 2010, should be calibrated on a case-by-case basis due to varying levels of familiarity, numbers of roundabouts, and driver aggressiveness. Capacity, the number of vehicles per hour, is calculated for the 44 critical lane through the use of the two previously discussed gap acceptance factors, as shown in the following equation:

(−B∗vc) cpce = A ∗ e [65]

Where,

vc is the conflicting flow,

3600 A = , and tf

t t − f B = c 2 3600

This model is only designed for single lane roundabouts because entrance into a multilane roundabout demands further considerations [66].

As the NCHRP states in Report 672, the HCM 2010 has several “simple, empirical regression models” that can be applied generally to multilane roundabouts

[67]. The first equation, can be applied to a one-lane entry or either lane of a dual-lane entry roundabout that has one conflicting circulating lane.

−3 (−1.0×10 )vc.pce ce,pce = 1,130e [67]

Where, ce,pce is the lane capacity (pc/h), which is adjusted for the heavy vehicles, and vc,pce is the conflicting flow (pc/h)

The next equation uses the same variables stated for the previous and is used during instances where there is one entering lane that has two conflicting circulating lanes.

−3 (−.07×10 )vc.pce ce,pce = 1,130e [67] 45

When two lanes enter on an approach that has two conflicting circulating lanes, separate equations are applied to the right and left entering lanes.

−3 (−.07×10 )vc.pce ce,R,pce = 1,130e [67]

−3 (−.075×10 )vc.pce ce,L,pce = 1,130e [67]

Where, ce,R,pce is the capacity of the right lane (pc/h), which is adjusted for heavy vehicles, ce,L,pce is the capacity of the left lane (pc/h), which is adjusted for heavy vehicles, and vc,pce is the conflicting flow (pc/h).

The final set of entry capacity equations are applied under the circumstance of a high-angle merging right-turn bypass lane, which is also referred to as a slip lane.

They may be applied when a slip lane merges into one or two exiting lanes, respectively.

−3 (−1.0×10 )vc.pce cbypass,pce = 1,130e [67]

−3 (−0.7×10 )vc.pce cbypass,pce = 1,130e [67]

Where, cbypass,pce is the bypass lane capacity (pc/h), which is adjusted for heavy vehicles, and vex,pce is the exiting flow of the conflicting lane (pc/h).

The capacity of slip lanes merging at low angles with exiting lanes or of slip lanes forming additional lanes alongside exiting lanes is yet to be determined as the

HCM 2010 has not evaluated these occurrences.

2.5 VISSIM

2.5.1 Roundabout Simulation

Roundabout design and performance has been modeled in the United States through various types of software programs including the microscopic simulation models CORSIM, PARAMICS and Synchro-Sim Traffic, and analytical models

ARCADY, SIDRA and RODEL. The most recent development in roundabout analysis software comes from Planung Transport Verkehr (PTV), a German traffic engineering

Software Company. PTV created the stochastic microscopic simulation model VISSIM in 1992, which is capable of modeling a roundabout as part of a transportation system

[68]. The model is based on the Wiedemann 1974 car-following theory, which models driver behavior through driver perceptions and actions. The following vehicles adjust their speed due to changes in perception of “apparent size” and relative speed of the lead vehicle [69]. Figure 7 displays the car-following logic of Wiedemann’s 1974 model.

Figure 7: The Car Following Logic of Wiedemann 1974 [70] 47

This model is based on four different states including the free driving, approaching, following, and emergency regimes [71]. Under the condition of free driving, the following car is not influenced by the lead vehicle travels at a desired speed. The following car in free driving will eventually enter into the approaching mode when a threshold of perception is exceeded. The lead car then dictates the speed of the following car as the latter enters the following mode. The lead and following cars remain in this state until one car exits the roadway or the regime changes to emergency mode. This fourth mode occurs when the minimum headway between the two cars is surpassed, forcing the following car to apply the brakes.

2.5.2 Model Calibration

As with any microscopic simulation model, calibration of the model is necessary to achieve a high level of accuracy. Various parameters can be changed to calibrate the model to a specific site. These variables include, but are not limited to, roadway geometry, desired speeds, reduced speed zones, acceleration, lane position, lane change movements, driver reaction time, yield bar placement, signal controls, and minimum headways and gaps [68] [72] [73].

VISSIM operates from the drawing of the experimenter through the use of links and connectors for roadway and intersection designs. There are additional elements in design that are vital in the process of modeling a roundabout. Route decisions consist of a decision point located upstream on an approach leg, which is then associated with several downstream routes, i.e. three downstream routes at a typical four leg 48 roundabout. The barring of changing lanes within the circulating lanes of a multilane roundabout requires the mode to be designed to where vehicles are properly guided into their appropriate lane for their route before entering the roundabout. The next element is the priority rule. This allows an experimenter to set the right-of-way in order to force approaching vehicles to yield to circulating vehicles. A yield line for the entering vehicle is placed on the approach, while two markers are placed in the circulating stream to signify the conflict area between the entering and circulating vehicles. This method of priority tends to be less friendly to the user. Newer versions of VISSIM have a new network object labeled “conflict areas”, which predefines conflict points between conflicting traffic streams. Using conflict areas has been reported as an alternative where “vehicle behavior is more intelligent” in the model [74]. Priority rules determine the results of the minimum headway and gap times [73] [64]. The last important design component is speed control. A speed distribution can be assigned to particular vehicles classes, and the speed all vehicles can be changed through the use of reduced speed zones and acceleration and deceleration rates.

2.5.3 Model Validation

Various studies have been conducted in recent years to test the validity of the software comparatively to the already existing models. Gagnon et al. analyzed the results of five models, including PARAMICS, Synchro-Sim Traffic, aaSIDRA

(SIDRA), RODEL, and VISSIM. This paper aimed to evaluate each model in terms of calibration potential for two roundabouts located in New England. Their results 49 indicated VISSIM had the most calibration factors of any other model, and the calibration of these factors significantly enhanced the results of the test runs [68].

Another study evaluated the results of delay and vehicle queues in VISSIM and

SIDRA by manipulating volumes and the quantity of left turning vehicles. Calibration was required and completed for both software programs, especially through the use of typical values of critical gap. There was no statistical difference in predicted delays between VISSIM and SIDRA, but VISSIM did calculate significantly longer queues

[73]. The study found the two models to be similar, but further calibration of each model with field data would provide a more detailed comparison.

A third study compared capacity through VISSIM and SIDRA, but in addition, this study compared the NCHRP-572 capacity equations for single and multilane roundabouts. All three models yielded similar results for a single lane roundabouts, especially VISSIM and SIDRA. The capacity of the NCHRP-572 equations tended to vary more from the other models at extreme low and high volumes. SIDRA consistently calculated higher capacities at two-lane roundabouts for both right and left lanes. While the NCHRP-572 did not predict inside lane capacities, this model was similar to VISSIM when examining results for the right lane [64].

Based on studies conducted comparing various roundabout modeling software,

VISSIM produces results of performance very much alike other leading programs. Due to the ease of practice and reliable results, the use of VISSIM is an established method of evaluating the potential or existing performance of a roundabout.

CHAPTER 3: SITE DESCRIPTION

The city of Athens is located in the southeastern section of the state of Ohio and is home to Ohio University. The university was the first in the Northwest Territory and was founded in 1804. In recent years, student attendance has reached approximately

23,000 when taking into consideration not only undergraduate but masters, doctoral, and medical students, as well [75].

The most prevalent highways providing access to the city of Athens are US 33,

US 50, and SR 32. US 33 is the main connecting the cities of Athens and Columbus.

This thoroughfare, wraps around the east side of the city and merges with US 50 and

SR 32 for approximately 2.5 miles. US 50 provides access from Athens to Parkersburg,

West Virginia and Cincinnati, Ohio. SR 32 is the main highway between Athens and

Chillicothe, Ohio.

There are four main exits from these three highways which directly access the city of Athens. Figure 8 is an image of Athens and shows these exits. The first exit shown in the figure is for East State Street, which is then followed by Stimson Avenue,

SR 682, and Richland Avenue exits. The latter two intersect on the south side of the

Hocking River and Ohio University’s campus. The SR 682 exit proceeds to the intersection that is the location for this study.

Figure 8: The City of Athens, Ohio

At one point, the intersection of State Route 682 and Richland Avenue was considered the most dangerous intersection in the city [76]. In 2005, the intersection was evaluated for safety because the intersection was ranked 78th on Ohio’s Crash List by the Ohio Enhanced Crash Identification Location System in 2003 [77]. Over a span of five years, 2003-2007, there were 73 crashes reported at the signalized intersection, which equated to a crash rate of 2.36 crashes per million vehicles [77]. Variation in speeds of the two roadways was a contributor to the high crash rate. The posted speed limit of Richland Avenue is 25 mph, while on SR 682, the posted speed limit is 50 mph. 52

City officials, along with the Federal Highway Administration (FHWA) and the

Ohio Department of Transportation (ODOT), assessed improvement project alternatives for the Richland Avenue/SR 682 intersection. Three alternatives for the intersection were evaluated, and all three included structural improvements to the existing bridge over the Hocking River. The first and second alternatives expanded the signalized intersection through increasing the bridge from four lanes to six lanes. There are several large apartment complexes are located on Richland Avenue south of SR 682 and are the source of the pedestrian traffic. To accommodate these pedestrians, the first alternative called for another 30 foot expansion on the bridge to provide access for cyclists and pedestrians, while the second alternative would build a separate bridge for these users. Alternatives 1 and 2 would have cost an estimated $7.1 million and $5.75 million, respectively [78]. The third alternative of introducing a multilane roundabout would keep the number of lanes at four and would have cost $4.5 million [78]. The roundabout would address three issues: unsafe intersection operation, repairs of the

Richland Avenue Bridge, and increased safety for non-road users [77].

Due to the city’s budget and the potential for increased safety through the implementation of a roundabout, the third alternative was chosen. Construction of the multilane roundabout began in March 2010 and was completed in late summer of that year. An aerial picture of the roundabout can be seen in Figure 9. There are four approaches, each having two approach lanes except one which has an additional slip lane. For simplicity, the northern approach is referred to as Campus approach, the 53 western approach is the 682 approach, the southern side is the Richland approach, and the eastern approach is the 33 approach.

Figure 9: The Roundabout at Richland Avenue and SR 682

Each approach has a different lane configuration. This can be seen in the above figure. The campus approach lanes consist of a combined right and through movement lane and a left turn only lane. SR 682 approach utilizes the design characteristic of flare by adding a right turn only lane in addition to the through and left turn combination lane. The approach from 33 has the same lane configuration as the 54 approach from 682 but does not incorporate flare as two lanes merge at the highway exit and continue to the roundabout. The Richland approach, unlike all other approaches, has three lanes. The first is a slip lane for right turning vehicles; the second is a through movement only lane, and the third is a through and left turn lane.

This approach uses flare to widen the single lane from S. Richland Ave. to the three lanes described.

There are several other notable features of this roundabout. The central island is

70 feet in diameter adorned with a plain, yet unique rock formation, which is then surrounded by a 14 foot wide brick truck apron. The 682 approach is equipped with a pedestrian crosswalk and splitter island refuge for pedestrians. Further west, there is also a tunnel under the roadway for cyclists and pedestrians to access.

Positive guidance and direction is provided to drivers through multiple signs and pavement markings. These signs and pavement markings follow both the requirements and recommendations made by the MUTCD, which were previously discussed in Chapter 2. All approaches begin with roundabout warning signs that recommend navigating the roundabout at a speed of 20 mph. Advance diagrammatic guide signs are mounted on overhead structures on the US 33 and Campus approaches.

There is also an overhead sign on the Richland approach that indicates the right slip lane should be used by drivers traveling to US 33/US 50/SR 32 via SR 682. The 682 approach contains pedestrian crosswalk signs for both entering and exiting drivers on this leg of the roundabout. Entering drivers then pass lane guide signs and fishhook pavement markings to aid drivers in choosing the proper lane before they are directed 55 to yield at the roundabout entrance. A yield sign, as well as “YIELD” being written on the pavement in conjunction with triangular yield markings, are implemented at all approaches. Accompanying the yield sign are additional plaques that state “To Traffic in Roundabout”. Black and white chevrons and one-way signs direct vehicles counterclockwise around the central island. While in this roundabout, directional arrows are marked on the pavement, and route signs are posted in the splitter islands to aid in exiting the circular lane. Figure 10 and Figure 11 show two views of the 33 approach. Figures 12-14 illustrate the campus, 682 and Richland approaches, respectively.

Figure 10: View of the 33 Approach

Figure 11: A Closer View of the 33 Approach

Figure 12: View of the Campus Approach

Figure 13: View of the 682 Approach

Figure 14: View of the Richland Approach

CHAPTER 4: METHODOLOGIES

The specific aim of this thesis was to identify the impacts on roundabout operations due to familiar and unfamiliar drivers. A multifaceted approach was used and included a survey, street observations, and traffic microsimulation. Hypotheses of the methodologies include (a) the survey would give insight on driver’s knowledge about roundabout rules and navigation (b) direct observation of the study roundabout would provide actual data on driver behavior that could be used in microsimulation scenarios, and (c) the development and analysis of a traffic microsimulation model would present the opportunity to analyze operations at the study sight. The specifics of the approaches are presented in further detail in the following subsections.

4.1 Roundabout User Survey

4.1.1 Survey Description

The survey questionnaire, provided in Appendix C, was designed to gain insight on the decisions familiar and unfamiliar drivers are likely to make when approaching and also driving through a roundabout and to further determine potential countermeasures for driver errors at roundabouts. The survey comprised 16 questions that were divided into three sections.

Section A, “General Information”, asked seven questions that were focused on collecting demographic information about each survey participant in regard to gender, age, city/town of residence, frequency of use of roundabouts, and the educational information on roundabouts encountered. The question pertaining to frequency of use 59 was used to determine the familiarity/unfamiliarity of the participant with roundabouts.

If the participant answered that he or she rarely or never drove through roundabouts, this participant was decreed as unfamiliar. Any response that indicated the participant drove through a roundabout one or more times a week was categorized as familiar. If a participant utilized a roundabout enough to consider his/her usage more than rare, yet the frequency of use was less than once a week (i.e. once a month/several times a month), this person was categorized in between familiar and unfamiliar as an occasional user.

Section B, “Lane Assignment”, asked participant questions that were focused on gaining insight on the appropriate choice of lanes on the approach to a roundabout and, also, the knowledge about recognition or interpretation of signage and pavement markings on roundabout approaches. The four questions were related to the two images in Figure 15, which show a two-lane approach to a roundabout and a typical diagrammatic guide sign at a multilane roundabout. All possible directional movements (right turn, through movement, left turn, and U-turn) that can be made at an approach to a roundabout were covered in the four questions of this section. As an example, Figure 15, illustrates the two images and also question 8.

The ability to yield or give priority, at roundabouts is one very important operational aspect that essentially contributes to the marked safety. Section C,

“Priority”, asked participants questions aimed at gaining knowledge on yielding opportunities at roundabouts. This section comprised five questions, where three 60 pertained to the yield-to-left rule of vehicles and the last two inquired about pedestrian and cyclist priority.

Figure 15: Example from Lane Assignment Section of Questionnaire

4.1.2 Survey Questionnaire Administration

The questionnaire was anticipated to be distributed to drivers of varying familiarity with roundabouts. To accomplish this, booth spaces at two county fairs were purchased. The first county fair attended was the Washington County Fair in

Marietta, Ohio due to the county’s vicinity to the nearest roundabout, which is the roundabout in Athens, Ohio. Marietta is located approximately 55 miles east of

Athens, and the city contains no roundabouts. An assumption was made that many residents would have little experience with roundabouts since they would not typically drive through one often, and therefore, participation from unfamiliar drivers would be 61 obtained. The second fair was the Hocking County Fair in Logan, Ohio. Logan is located approximately 25 miles northwest of Athens. In December of 2013, Ohio’s first double roundabout interchange was officially opened at the SR 33 and SR 664 interchange on the edge of Logan. Many of the attendees of the Hocking County Fair would likely be residents of Logan, therefore, the assumption was made that participants with high levels of experience would be obtained at this location.

4.1.3 Implementation

The questionnaire was conducted at the Washington County Fair over a period of four days, September 5th-8th, during the Labor Day holiday weekend. Over a period of six days, the survey was administered at the Hocking County Fair. This fair began

Monday, September 14th and concluded Saturday, September 19th. The survey was implemented from approximately 10:00 AM to 9:00 PM each day at both fairs. Over the holiday, a variety of people were expected to attend the Washington County Fair due to the extended weekend and livestock sale on Monday. A smaller crowd was expected on Tuesday during the morning and afternoon hours since this was a work day, but fair attendees were anticipated in the evening hours due to a live concert. The number of attendees at the Hocking County Fair were expected to be low during the first three days but were expected to increase during the next three days as the local school district was closed Thursday and Friday and livestock sales were held on those final days.

As people circulated in the booth space locations, potential participants were greeted according to the Recruitment Script, which is located in Appendix B. If 62 someone agreed to participate in the survey, the questionnaire was given to the participant and the introductory page of the survey was discussed. Any questions asked due to confusion were clarified at the discretion of the experimenter based on the content of a question. At the conclusion of the survey, answers were reviewed per request of the participant.

4.1.4 Decoding Responses

Answers were recorded using a coding scheme, i.e. answers to question one were coded as 1 = male and 2 = female. This was continued throughout the remaining questions in order to input the answers into IBM SPSS for analysis. During analysis of the questionnaire, the responses had to be recoded as correct or incorrect to satisfy statistical analysis assumptions.

4.2 Field Data Observations

Observations of real conditions in the field served a number of purposes including: the provision of traffic data necessary for coding a traffic microsimulation model, provision of driver behavior data, gap acceptance data, and also speed data.

Specific details on each of these aspects of field observations are in the following subsections.

4.2.1 Video Data

The video data collection was performed to provide a factual source of traffic operations at the study site. Video data for the study intersection was collected using the Miovision Scout Video Collection Unit. This unit is a transportable device that 63 allows for the collection of video data for up to seven days in the field without needing checked. The Scout has the capability of recording a “bird’s eye view” of a roadway or intersection. Components of the Scout include the camera, control unit, extra power pack, pole frame, and tripod frame. The pole frame can be attached to posts, trees, or other poles and extends to a height of 21 feet. The tripod frame is used when none of the mentioned objects are present in which to attach the pole frame.

Data was collected in the field in order to determine parameters for familiar and unfamiliar driver scenarios in PTV VISSIM. To code network in the microsimulation software, parameters to be gained from the video data include the volumes, driver behaviors, and critical gaps. The Scout was attached to a light post at the roundabout on the north approach and extended to a height where the camera could capture the entire roundabout. An example of the camera visual can be viewed in Figure 16. The unit was programmed to collect video during a control day and also over the course of three more days during Ohio University’s “Move-In” Weekend. The control day, August 4th, was chosen based on the university’s calendar. This control data was one on which no special events were to occur on the Ohio University campus, and the assumption was made that drivers would be comprised mainly of faculty, staff, and commuters with few potentially unfamiliar drivers. In contrast to the control day, during “move-in” weekend, many drivers were expected to be unfamiliar as these were day during which out of town parents traveled to Athens. An assumption cannot be made that only unfamiliar drivers were traveling through the roundabout considering residents were not restricted and were likely using the roundabout, as well. 64

Figure 16: Scout Camera Visual of the Athens Roundabout

4.2.2 Driver Directional Route and Behavioral Data

The directional volumes and behavioral data were obtained from the control day videos. The entrance and exit path of vehicles using the roundabout was documented in

15-minute intervals over a full 24 hours. Throughout recording the vehicle directional routes, driver errors were also recorded in order to obtain the number of errors which may occur in a typical day. The peak hour of the day was determined by the four highest totaled consecutive 15-minute intervals.

Driver errors were documented by tracking the type of error, the location of the error, and the time the error occurred. There were nine types of errors that occurred in the roundabout during the control day. Three of these errors were the non-compliant 65 behaviors discussed in Chapter 2, priority abstaining, priority surrendering, and priority taking. The other six of these errors are depicted in Figure 17 and include:

(i) Incorrect Left Turn: the incorrect left turn error is shown in Figure 17a. A portion of the drivers traveling westbound toward the roundabout on SR 682, referred to as the 33 approach, that make a left turn to southbound Richland Avenue choose the incorrect lane while circulating. They enter the inside lane, the circulating lane taking left turning drivers exiting campus to US 33. This means the incorrect left turners change lanes just before exiting, which can cause traffic on the inside lane to stop to wait for vehicles in the outside lane. This error occurs due to the design of lanes in the particular roundabout. Sometimes, the driver erring may cutting in front of a vehicle in the outside lane. In addition to potential rear end or sideswipe collisions, this error has the potential to increase delay for drivers turning right from the 682 approach.

(ii) All the Way Around: Figure 17b depicts the path one driver took on the control day when he/she made the all the way around error. This error occurs when drivers do not properly take their intended exit the first time. This also can occur if a driver chooses an approach lane that does not go to the exit desired and chooses to forego making a lane change. The driver can continue around the central island and enter into the appropriate lane at an acceptable location in the roundabout.

(iii) Apron Encroachment: an example of apron encroachment seen while reviewing the video data is shown in Figure 17c. An error was labeled apron encroachment when a passenger car willingly drove out of the designated lane onto the 66 truck apron unnecessarily until the driver returned to the lane. In the Athens roundabout, this typically occurred on the single circulating lane sides.

(iv) Shortest Path: this error is shown in Figure 17d. Similar to the apron encroachment, shortest path errors occurred when a driver was unwilling to follow the path of a lane and changed from the outer lane to the inner lane when entering the roundabout from campus and then switched back to the outer lane to exit.

(v) Wrong Direction: one of the wrong direction errors made during the control day is illustrated in Figure 17e. This driver failed to acknowledge the one way and chevron signs posted in the central island when turning into the roundabout towards oncoming traffic. The driver then proceeded to exit via the Richland approach and jump the median before encountering traffic traveling north on Richland Avenue.

(vi) Wrong Lane Choice: this error is depicted in Figure 17f. The wrong lane choice error is similar to the left turn error because a lane change was made. The difference is these drivers made an error on the approach when the wrong lane was initially chosen.

The final three errors, based on priority rules, were described in the Chapter 2, but are errors which cannot be depicted. To summarize them again, priority abstaining occurs when a driver needlessly yields to no oncoming traffic, priority surrendering occurs when a vehicle stops while circulating to incorrectly yield to approaching traffic, and priority taking occurs when a drivers fails to yield to circulating traffic. The first two have the potential to cause rear-end crashes and, the third, a side-swipe crashes. 67

(a) Incorrect Left Turn (b) All the Way Around

(e) Wrong Direction (f) Wrong Lane Choice Figure 17: Athens Roundabout Driver Errors 68

4.2.3 Gap Acceptance Data

The video data collected also allowed for the observation of gaps that were being used by drivers. The observed gaps (i.e. accepted and/or rejected) were collected and used to determine the critical gaps of familiar and unfamiliar drivers were recorded on the control day and on three Ohio University “Move-In” Weekend days. On each of these days, a minimum of 500 accepted gaps were recorded. Only accepted gaps less than 10 seconds were recorded because the assumption was made that no gap over 10 seconds would be rejected. Gaps were collected from 3:00-5:00 PM on the control day and Thursday and Friday of move-in weekend. Less traffic was present Saturday of the move-in days so data was collected 3:00-6:00 PM.

Gaps were based on time headways, the amount of time between vehicles in the roundabout. The accepted gap for each vehicle was measured from the time the entering vehicle crossed the triangular yield pavement markings until conflicting traffic crossed over the centerline of the approach lane. When an entering vehicle rejected a gap, the headway was measured from the time the rear bumper of conflicting traffic crossed the centerline of the approach lane until the next conflicting vehicle’s front bumper crossed the centerline of the approach lane.

The accepted and rejected gaps of each day were grouped into increments of

0.25 seconds. Any gap within each range of 0.25 seconds was categorized based on the midpoint of that range, where if two vehicles accepted gaps of 4.05 and 4.92 seconds, these gaps would be classified as 4.125 and 4.875, respectively. The midpoint of these 69 quarter of a second ranges was used so as to not underestimate or overestimate the value of the critical gap by using the low or high end of the range, respectively. The frequencies of the grouped time headways were graphed to form cumulative distributions. This is done according to Raff’s method of gap acceptance [58], where the intersection of the accepted and rejected graph frequency curves is the critical gap.

4.2.4 Speed Data

Speed data was collected through the use of an UltraLyte Laser Technology radar gun over the course of four days during Ohio University Freshman Student

Orientation and during the control day. Data was collected from 7:00 AM-5:00 PM on a Monday, Tuesday, and Wednesday and 10:00 AM-5:00 PM on a Thursday. Speeds of approaching and circulating vehicles were recorded continuously except during periods of unsuitable weather. Driving behavior changes when the weather is rainy, and the radar gun could not work properly throughout these times due to visibility issues anyway. On the control day, speeds were documented from 7:00 AM-5:00 PM.

Circulating and approach speeds were taken during alternating hours, where each 15-minute interval was a different circulating side or approach of the roundabout.

Monday and Tuesday began with the first hour as the approach speeds and the second as the circulating speeds. In odd numbered hours, approach speeds were collected and in even numbered hours, circulating speeds were collected. Wednesday and Thursday, the hours of collection were opposite with circulating speeds collection during odd numbered hours and approach speeds during even numbered hours. 70

During an orientation week, there are three sessions. The first session is

Monday and Tuesday, the second Tuesday and Wednesday, and the third Wednesday and Thursday. On the first day of each session, parents and students were to arrive between 7:30 AM and 9:45 AM. 33 approach speeds from this time frame were used for analysis between the orientation unfamiliar drivers and the familiar drivers of the control day. There are no circulating speeds associated with the morning arrival of orientation attendees.

On the second day of each session, orientation activities end at noon, but not all attendees will stay until this hour. From 10 AM-12PM are residence hall visits, which some will opt to not attend. Vehicles leaving campus between 10 AM and 2:00 pm were the focus of unfamiliar drivers. Vehicles approaching the roundabout from campus contained the unfamiliar drivers. These vehicles were likely to exit campus to

US 33, so circulating drivers on the west and south sides of the roundabout were analyzed in comparison with the circulating speeds on these two sides of the roundabout during the control day.

All speeds from the control day were necessary to determine the minimum, maximum, and 15th, 50th, and 85th percentiles at each approach and circulating side of the roundabout to use in the network coding of the VISSIM models. There is importance in noting that there are familiar drivers mixed together with unfamiliar drivers during orientation. Taking speeds during this university event is a practical representation, where most drivers are familiar but some are not. Only when a 71 roundabout is newly introduced is everyone unfamiliar, and this is only for a short while.

4.3 Traffic Microsimulation

A traffic microsimulation approach was than because this would allow to effectively study these issues at roundabouts. Microsimulation models have become useful tools for planning and design of transportation systems because they provide the ability to evaluate alternatives prior to their implementation. For this study, VISSIM was used.

4.3.1 Model Development

The Athens study roundabout was coded in VISSIM. The geometry of the roundabout was coded by tracing the network on Bing Maps images of the study location. Two specific models were developed. The first scenario is the condition of familiar drivers and was based on the control day speeds, volumes, and gap data collected. This model assumes familiar drivers understand how to properly navigate a roundabout perfectly. The second scenario incorporates unfamiliar drivers through speeds and gaps collected during orientation and “move-in” weekend, respectively, and also includes driver behavioral errors found during the control day.

The roadways of the roundabout for the familiar drivers were drawn in VISSIM with Richland Avenue as the continuous link, so south to north Richland Avenue, and vice versa, were drawn as continuous links. The left turn lane from campus to 33 was also a continuous link through the roundabout. The approaches and exits of SR 682 72 were drawn up until the roundabout where connectors then joined these roadways with each other around the roundabout and with the Richland Avenue lanes.

After establishing the roadways, the 2D/3D Model Distributions were adjusted from the default settings. These are the types of vehicle models that can be used in simulation. This element of the program defines the types of vehicles in the system and allows for them to be grouped as needed. There were seven small cars or sedans under the distribution name of “Car”, which were set to represent passenger cars in the model distributions. This included the Volkswagen Golf, Audi A4, Mercedes CLK, Peugeot

607, Volkswagen Beetle, Porsche Cayman, and Toyota Yaris. Five more vehicle models were added to the car distribution to account for the SUVs and pickup trucks that utilize the roundabout. The Jeep Grand Cherokee, Ford Explorer, GMC Yukon,

Ford F150, and Toyota Tundra were incorporated in the “Car” model, which was used on every vehicle route. All vehicles had an equal share in the model distribution.

The second distribution used was called Heavy Goods Vehicles (HGV). The only HGV vehicle was the EU 04 box truck. A wheelbase 40 tractor and trailer were additions made to the default model. Each was given an even share of the distribution.

Based on the peak hour volumes used from the control day, there were only three vehicle routes which did not apply this distribution.

The third model used in the network coding was called “Bus”. This distribution modeled the transportation systems provided by the University Courtyard and Summit at Coates Run apartment complexes. University Courtyard utilizes old school bus of the AASHTO School 36 model, which had to be downloaded and imported into 73

VISSIM from PTV’s expanded vehicle fleet online. The Summit at Coates Run bus is modeled by the EU Standard model that was already provided in the default bus model.

Both buses were given an equal share of the distribution.

A second bus distribution was created to model the Go Bus, which is also the name for the model. This is a charter bus that services the transport to and from Athens to various cities throughout Ohio. Only one vehicle was used in this distribution, which is the bus labeled AASHTO Bus 40, which was also downloaded and imported into

VISSIM from the expanded vehicle fleet.

The model distributions were combined according to the vehicle volumes counted throughout the control day to create the vehicle compositions. These combinations can be viewed in Table 4. The vehicle compositions are labeled according to the approach and destination of the automobiles in the vehicles routes.

Table 4: VISSIM Vehicle Compositions for Vehicle Routes

To finish the vehicle compositions, speed distributions found from field data collection had to be assigned to each vehicle model listed in each composition. Each of 74 the four approaches and each of the four circulating sides of the roundabout have a different car and truck speed distribution. Buses were assigned the same distribution as

HGVs since all large trucks and buses were combined in the speed data collected. The speed distributions were labeled based on the vehicle type (passenger car or truck) and the location the data was collected. The approach speed distributions were assigned to each the appropriate vehicle types. Desired speed decisions were applied on the approaches where deflection began. This speed change was based on advisory entry speeds. To account for circulating speeds, reduced speed areas were inserted in the circulating lanes, and the corresponding circulating speeds distributions were assigned.

Along with the speed distributions, relative flows were apportioned to each vehicle model in each composition. Actual volumes from the control day peak hour were used.

Vehicle routes were defined, but issues arose when there was the realization that vehicle compositions could not be assigned to vehicle routes and only to vehicle inputs.

This is a typical issue, and fixing this requires building roadways outside of the area of focus for each vehicle route. All of these lanes were slowly merged long before the focus area. Once in a single lane, a connector was used to attach this lane to the original approach lane(s) in the study. After entering the original lanes, vehicles would cross a boundary, called a node, which is the limits in which data is collected in the system. Any occurrences or conflicts merging outside of the node are not recorded in the output of the model. Once this was completed total volumes for each turning movement and vehicle compositions could be assigned to the respective vehicle inputs. 75

Manipulating the limitations of the program allowed for a simple solution in order to have specific vehicle compositions attached to specific directions.

Final elements of coding included assigning priority in conflict areas and applying the critical gap. There were many potential conflict areas within the scenario because of the merging lanes outside of the node. These areas were all left with

“passive” status, meaning no priority is defined. The conflict area was left color coded as yellow. Where conflicting flows cross paths (the meeting of approach and circulating lanes) priority was assigned to the circulating drivers, coloring the circulating path green and the approach paths red. In situations where traffic diverged, priority was marked undetermined, where both pathways were colored red. A few of these conflict areas had to be adjusted. The proximity of so many conflict areas can cause unintentional inappropriate vehicle behaviors, particularly by circulating vehicles. Adjustments were made to conflict areas until vehicles were acting properly according to the scenario. In all situations where priority was assigned to one of the two paths, the critical gap was applied to the rear gap. The rear gap is another name for the critical gap as it is defined as the minimum gap between the rear of the minor road vehicle and the front of the first conflict flow vehicle.

The model was reviewed to ensure the program was running smoothly and accurately. Queueing of traffic on the campus approach was an issue since vehicles were backed up to what would be the parking lot entrance of the basketball arena on campus. This was thought to be more than twice what actually may occur. The roundabout was further monitored to determine the typical length of the line. VISSIM 76 was run in sets of 50 simulations under various inputs of critical gap. Based on these various critical gaps, the maximum queue lengths of the campus approach that most accurately matched what was observed in the field was used in the model for the calibration input. This was used with calculated critical gaps of familiar and unfamiliar drivers to determine the calibration input appropriate for the unfamiliar scenario.

The familiar scenario was copied to use for the unfamiliar scenario. All roadways and connectors could be kept with the exception of through lanes from

Richland to Campus and the two approach lanes from 33. To accommodate for the driver error in the unfamiliar scenario, all of the roadways had to be modified to one lane so the behavioral errors could be simulated, particularly the wrong lane choice error. Additional connectors and roadways were added throughout the scenario to encompass all other types of errors, too. An additional roadway was added for each type of driver error outside the monitored system which also had to merge into the single lane before entering the node. A vehicle input and vehicle route were assigned to each of these new lanes according to the error associated with each lane,which had to be done carefully to ensure the program did not switch paths to the shortest route possible without the realization this had occurred.

In copying the familiar scenario, vehicle models, compositions, and most speed distributions were already in place. One more vehicle model was added to model drivers who made the priority abstaining and surrendering errors. This model consisted of the same cars within the “Car” distribution. Special reduced speed areas had to be placed on the appropriate locations on the approach lanes or in the circulating lanes 77 where these two priority errors occurred on the control day. These reduced speed areas were programed to only affect the cars of the new model. All other vehicles retained their current speeds when crossing into those areas.

Vehicle compositions were adjusted according to the errors. During three of the evening peak hours, the number and types of errors were noted in order to be used in the simulation. Since the peak hour volumes and the errors over three hours were used, the errors made were each divided by three. The number of vehicles input into the system were kept consistent by adjusting the volumes on the vehicle inputs of the familiar scenario. This was done to ensure any variation in the stochastic model was done according to the software. A simple example of adjusting the volumes due to error can be given. If 100 cars traveled from campus to south Richland Avenue, the familiar scenario would have 100 cars traveling that vehicle route. If 9 drivers over the course of the three hours used in recording driver errors made the wrong lane choice and had to switch from the left lane to the right when entering the roundabout, then this number would be divided by three for the three hours. In the unfamiliar scenario, there would still be 100 vehicles traveling south from campus, but 97 would be navigating properly, while the other 3 would be making this driver error.

Speed distributions were adjusted for only the routes unfamiliar drivers would be expected to take, the 33 and campus approaches and the west and south circulating sides. These were adjusted based on the distributions found during the Student

Orientation week. One more speed distribution was added to the scenario for the priority abstaining and surrendering reduced speed areas. 78

With the parameter inputs complete, simulations were monitored to ensure the unfamiliar model was working as anticipated. Figure 18 depicts the visible differences in the two models. The familiar scenario has fewer connectors and is much less complicated than the unfamiliar scenario.

(a) Familiar Model (b) Unfamiliar Model Figure 18: Centerline View of Familiar and Unfamiliar Models

4.3.2 Microsimulation Runs

The stochastic nature of VISSIM comes from the random seed number assigned to the simulation run. If random seed 1 is used repeatedly, the exact same results will be given every run. An increment of three was set throughout testing. The first 50 runs for each scenario were used to find an estimate of the standard deviation of average delay and the average queue lengths. In accordance with the level of significance chosen and the expected errors, sample sizes were calculated, and additional runs were conducted to exceed the highest estimated sample size needed.

CHAPTER 5: STATISTICAL ANALYSES

5.1 Sample Size

Sample size is a way of determining the extent to which data should be collected in order to complete a study statistically justified. Having sufficient samples is important because there are errors associated with inadequate sample sizes, Type I and Type II. A Type I error occurs when the effect in a population is perceived to be significant and is actually non-significant [79]. A Type II is the opposite, where the population is determined to be non-significant but is actually significant. The equation used to find a sample size, n, is:

2 (Z −Z ) ∗σ2 N = β α⁄ 2 ε2 [80]

Where,

Zβ is the likelihood of making a Type II error,

Zα/2 is the Z-score associated with the level of confidence, s is the standard deviation of the variable, and

ε is the error deemed acceptable.

In most studies, Zβ is associated with a power of 80% and thus equals 0.842.

The chances of making a Type II error decreases as the power associated with β increases. The value of Zα/2 in this study is -1.96, which is based on a 95% confidence interval. This means there is a 5 in 100 chance the results of the statistical analysis are actually due to chance, so there is a 95% confidence in that there is no Type I error being made [81]. 80

The problem investigated by the questionnaire was how familiarity affects a driver’s knowledge regarding roundabout rules and priority. The target population was the state of Ohio, but the accessible population was that of Hocking and Washington

Counties in southeast Ohio. Typically 10 % of the accessible population should be sampled, which meant approximately 6,900 surveys would have needed to be completed. According to Statistics with Applications to Highway Traffic Analyses, the sample size of a survey should also be based on the considerations of time and cost

[82]. In having time and cost restrictions, a sample size of at least 100 respondents is acceptable for a descriptive study. This number will allow for inferential statistical evaluations to be performed, which include cross tabulation and chi-square analyses

[83].

The sample sizes for speeds, gaps, and VISSIM trials were determined through the sample size equation discussed. The first 20 samples in gaps and each speed location used to determine an estimation of the standard deviation. The maximum value of average delay and queue length of 50 different VISSIM runs were used to determine the standard deviation of VISSIM runs. Through this standard deviation and the desired maximum error, a preliminary estimate of sample size was calculated for each. Table 5 provides these sample sizes needed for familiar and unfamiliar drivers regarding speed, and gap. 81

Table 5: Preliminary Sample Size

5.2 Statistical Tests

5.2.1 Correlation Coefficients

Pearson’s correlation coefficient measures the degree to which two variables are related, but provides no actual insight as to how they are connected. The correlation of two variables is found by the following equation:

Σ(x −x̅)(y −y̅) r = i i [84] (N−1)sxsy

Where, xi is the corresponding value to yi, x̅ is the average value of variable x, y̅ is the average values of variable y, and 82 sx and sy are the standard deviations of variables x and y, respectively.

The correlation coefficient was used first in the analysis of the questionnaire responses. This allowed for the determination of any potential relationship between responses and questions. The main concern through this questionnaire was determining how different characteristics of drivers answered each question or even a series of questions. The main objective was find how frequency of use, residence, gender, age, and exposure to educational information influenced proper decision making. As mentioned, though, this test does not show how the variables are related but was used to narrow the list of relationships to study further.

5.2.2 Cross Tabulation

Cross tabulation is a tool which can provide some understanding as to how two different categorical variables are related, and this was the next analysis conducted for all of the significantly related variables found in the correlation coefficient analysis.

This analysis allows for potential insight on why the correlations may have been significant. Crosstabs provided a breakdown of how survey participants answer the questions. By grouping different series of questions, such as all lane assignment questions, crosstabs could be used to provide an output of how people answered the series of questions and then be grouped according to however many each participant answered correctly. For example, if a person thought the right lane could be used for all four routes (right, through, left, and U-turn), the grouped variable in SPSS would be

1111, since “the right lane” is the first possible answer for each question. All 83 participants were recoded this way to allow for a deeper analysis. The following table,

Table 6, is an example of the information crosstabs can provide.

Table 6: Example of Cross Tabulation

5.2.3 Chi-Square

The Chi-Square test is a commonly used nonparametric statistical test that examines nominal data. This test determines the goodness of fit in regard to a hypothesis concerning the actual data collected and the theoretical data. These are typically referred to as the observed and expected samples. The theoretical data may be an already established and accepted distribution [85]. The formula for the Chi-Square test is as follows:

(Observed Frequency−Expected Frequency)2 χ2 = Σ Expected Frequency [85]

The null hypothesis in this test is always that there is no significant difference in the results of the expected and observed data. The chi-square value calculated is then compared to the critical value of 휒2associated with the level of confidence. Should the 84 calculated value exceed the critical value, the experimenter would reject the null hypothesis [79].

The chi-square test was performed for all significant correlations between demographics and lane assignment or priority rules questions. Significance of the

Pearson’s Chi-Square was investigated to determine if a relationship actually did exist between variables and if so, what that relationship meant in terms of the results.

If the chi-square test was significant, crosstabs was reviewed between the significant variables to determine the relationship.

In reviewing the output of SPSS for this statistical analyses, many tests showed a high percentage of cells with an expected count of less than 5. An assumption of the test is that this percentage should be no greater than 20%. Answers on the questionnaire were re-coded in SPSS to lower the percentage to an acceptable range.

For example, upon observing a high percentage in violation of this assumption for the comparison of age and the answers chosen for the lane to make a left turn at the roundabout (Question 9), the answers were no longer grouped as the right lane, left lane, either lane, or “I don’t know” but as correct or incorrect. This process gave percentages of an acceptable range to meet the assumption in all tests.

5.2.4 Z-Test for Proportions

The z-test for proportions is used to determine if the results of two groups are significantly different. This test assumes the data is independent and is of a sample size of 30 or more. The proportions of group 1 and group 2 were calculated:

x1 p1 = n1 85

x2 p2 = n2

Where: x1 and x2 are the number of occurrences in groups 1 and 2, respectively, and

n1 and n2 are the sample sizes of each group

The standard error is calculated through a pooled sample proportion:

n p +n p p = 1 1 2 2 [86] n1+n2

With the standard error computed, the z-score can be calculated. This number is then compared to the z value associated with the significant level chosen.

푝 −푝 푧 = 1 2 1 1 [86] √푝(1−푝)( + ) 푛1 푛2

In this study, the z-test was used to determine the significance within the questionnaire. Significance in participants’ abilities to answer questions or series of questions correctly was tested among demographics including gender, age, residence, familiarity, and exposure to educational information. This was done in order to determine how demographics affected proper decision making. The z-test was also used to observe which question or series of questions was most difficult for each group of participants to answer correctly. Correct answers for the series of lane assignment questions and for the series of priority questions, including all five priority questions, were compared. If the priority questions were found to be significantly more difficult for the participants of a particular group to answer, the z-test was further used to compare the series of vehicle priority questions, the pedestrian question, and the cyclist question against one another to pinpoint which was more challenging. In all z-tests, a 86 significance level of 0.05 was used, which corresponds with a z-score of 1.647. Any value of z greater than this proved a significant difference existed between the two proportions tested.

5.2.5 Independent T-Test

The Independent of Student’s T-Test compares the means of two independent data samples. The test is based upon the observed difference, expected difference, and the standard error. The estimated standard error is calculated by the following equation:

S2 S2 √ pooled pooled Sx̅1−x̅2 = + [87] n1 n2

Where, n1 and n2 are the samples of variables 1 and 2, and

2 S pooled is:

Σ(X −X̅ )2−Σ(X −X̅ )2 S = √ i1 1 i2 2 [87] n1+n2−2

Where, the numerator is the difference between the sum of squared errors for variables 1 and 2 and the denominator is the degree of freedom for the test.

The equation for the independent t-test is written below and is also modified for the assumption of the null hypothesis that the manipulation applied in the experiment has not effect on the results (μ1 = μ2).

(x̅ −x̅ )−(μ −μ ) (x̅ −x̅ ) t = 1 2 1 2 = 1 2 [87] Sx̅1−x̅2 Sx̅1−x̅2 87

In this study, the manipulation of the variables is the familiarity or unfamiliarity of drivers based on which the days data was collected. T-tests were conducted to determine statistical significance between approach and circulating speeds during arrival and departure times of the control group and orientation attendees. Similarly, t- tests were conducted for the analysis of the accepted and rejected gaps of the control group and the accepted gaps and rejected gaps of the move-in traffic. The results of average queue length and delay from VISSIM were also analyzed through a t-test. If the results of these tests show a significant difference, then a conclusion can be drawn that driver familiarity does impact the performance of a roundabout.

5.2.6 One-Way ANOVA

The One-Way ANOVA statistical analysis is the same as an independent t-test, where the hypothesis that the means are the same is tested, but has the capability of testing more than two means without increasing the chances of a Type I error. These are both limitations of the independent t-test. A number of assumptions are associated with the ANOVA analysis [88]. The first two assumptions are the dependent variable must be measured as an interval, while the independent variables are categorical. Two more are the observations should be independent of one another, meaning there are different people in each group, and there should be no significant outliers in any of these groups. The final assumption is there should be homogeneity of variances with a normal distribution of the dependent variable for every independent variable. The first step of ANOVA is calculating the sum of squares:

2 SStotal = (푋푖푘 − 푋̅) [79] 88

Where,

푋푖푘 is the ith score in the kth group, and

푋̅ is the grand mean

The total sum of square can be divided into two groups: the sum of square between groups and the sum of squares within groups. The following two equations these two groups.

2 SSB = ∑ nK(X̅K − X̅) [79] and

2 SSW = ∑(Xik − X̅K) [79]

Where, nk is the number of group k, and

X̅K is the mean of the kth group

The degrees of freedom associated with the sum of squares between and within are as follows:

dfB = K − 1

Where, dfB is the degree of freedom between groups, and

K is the number of groups

dfW = N − K

Where, dfW is the degree of freedom within groups, and

N is the total number of samples 89

By calculating the sum of square within and between and their respective degrees of freedom, the mean squares between and within groups can be computed using the following equations:

푆푆 푀푆 = 퐵 퐵 퐾−1 [79] and

푆푆 푀푆 = 푊 푊 푁−퐾 [79]

The F ratio is the ratio of the mean squares between to the mean squares within.

This value is a comparison of the means in accordance to the variability of each sample.

The expected value of the F ratio should be 1 if the null hypothesis was true since the means are equal in this hypothesis [79]. The value of F should then be compared to the critical value of the F distribution associated with the degrees of freedom of the numerator and denominator and the level of confidence.

A one-way ANOVA was performed on the three sets of days of move-in traffic.

This test allowed for the contrasts between the rejected and accepted gaps of each day without increasing the chance of making Type I errors, which would occur if multiple t- tests were used.

CHAPTER 6: RESULTS

6.1 Roundabout User Survey

A total of 252 participants agreed to take part in the questionnaire. There were

145 male participants (57.5%) and 107 females (42.5%). Participants were categorized by familiarity based on their frequency of use. 44.4% were considered familiar, 21.0% occasional users, and 34.5% unfamiliar. The residence of each participant was categorized as a city with or without roundabouts. If the town or city written down in the survey was located within 15 miles of a roundabout, that participant was considered to live in an area with a roundabout. There were 139 participants living within a 15 mile range of a roundabout, leaving 113 participants living in areas without roundabouts. The breakdown of age is shown Figure 19. The distribution in age was fairly even among the six age groups with percentages ranging from 11.1% of participants ages 25-34 to 21.0% of participants ages 45-54.

Figure 19: Age Distribution of Questionnaire Participants 91

Demographics were compared to the lane assignment and priority questions individually. The chi-square test results conducted are listed in Table 7. All cross tabulation tables are in Appendix D. There were 19 relationships analyzed, and 13 were found to be significant, meaning the variables were not independent of one another. The first two are between age groups and the answers chosen on which lane should be used when making a through movement and a left turn. Cross tabulation shows a trend where selection of the incorrect answer increases with age.

Table 7: Questionnaire Chi-Square Results

Residence, based on whether a participant lived near a roundabout, exhibited a significant relationship with four questions including choosing the appropriate lane for through movements and left turns, the decision appropriate for the green car, and the question about the cyclist. All relationships showed that participants living near roundabouts answered these questions significantly better than those who do not live near a roundabout.

The question of whether or not participants had experience with roundabouts was statistically significant with three other questions in the survey. These questions asked participant which lane was the appropriate choice to make a right turn, what decision the red car should make, and if a cyclist erred in using the roundabout. All three exhibited the same relationship that drivers reporting with experience chose the correct answer more often.

The final four significant chi-square tests were between frequency/familiarity and choosing the correct lane to drive through the roundabout, determining what decisions both the green and red cars should do, and identifying the priority of cyclists.

The same trend could, again, be seen in these relationships. Those with higher frequency of use were more likely to answer these questions correctly. In the question of lane choice for through movements, 75% of unfamiliar drivers chose the incorrect answer. This question was the first in section two, and may show that drivers rely solely on guide signs instead of using these signs in conjunction with the pavement markings 93 that would have given these drivers the correct answer. All of these significant relationships had small to moderate effect sizes.

The recoded answers of sections two and three of the survey were grouped according to correct and incorrect answers. The cross tabulation of the number of correct answer by gender, age, residence, familiarity, and exposure to education information are recorded in the following tables. Alongside the frequency of answers is the proportion of that frequency. Table 8 displays the correct lane choice answers according to driver characteristics. Tables 9-11 contain the correct priority responses concerning car priority, pedestrians, and cyclists, respectively. Another table is included, Table 12, which has grouped all priority questions (car, pedestrian, and cyclist) according to how many questions in the series the participant was able to answer correctly.

Table 8: The Number of Correct Lane Choice Answers by Driver Characteristics

Table 9: The Number of Correct Car Priority Responses by Driver Characteristics

Table 10: The Number of Correct Pedestrian Responses by Driver Characteristics

Table 11: The Number of Correct Cyclists Responses by Driver Characteristics

Table 12: The Number of Correct Priority Responses by Driver Characteristics

Through the use of these tables, specifically the proportions, significance between groups could be determined. The z-scores of the all z-tests are shown in Table

13 and Table 14 for lane assignment and priority, respectively. On a 95% confidence 96 level, answers could be found for how gender, age, residence, familiarity, and exposure to education information affected a participant’s ability choose correct responses.

In studying the responses to correct lane choice, a higher percentage of men were able of answering all four questions correctly, but there was no significant difference between men and women. The same held true for participants who had encountered educational information. Those saying they had used or seen a source of information tended to answer more correctly, yet the difference was not significant.

Table 13: Z-Test Results for Lane Assignment

There were, however, significant differences seen among age groups, residences, and familiarity. The 18-24 age group had significantly better responses in terms of answering all four lane assignment questions correctly than the 45-54, 55-644, and 65 and older age groups. The same holds true for the age group 25-34, where age groups 45-54, 55-64, and 65 and older were unable to answer the lane assignment questions as well as the 25-34 year olds. Although the other z-tests were not significant, the values do show the younger age group in every comparison had better responses.

There was a significant difference for residence. Participants living near a roundabout has significantly better answers on the lane assignment questions. The final characteristic of familiarity showed familiar drivers were capable of answering all questions correctly more than occasional users and unfamiliar drivers. There was no difference between occasional users and unfamiliar drivers.

Z-tests performed on the three vehicle priority questions had the same trends seen as in the lane assignment questions, where men and those marking yes to encountering education information had better decision making. Again, these differences were not substantial. Younger age groups were found to answer these three questions correctly more often, but only one comparison of proportions was actually significant. 18-24 year olds had significantly better responses than 45-54 year olds.

Vehicle priority questions were answered correctly significantly more often by residents living near a roundabout than those who do not live near a roundabout.

Analyzing familiarity on these questions provided significant results in all three 98 comparisons. Familiar drivers answered the questions significantly better than both occasional users and unfamiliar drivers. Occasional user also did significantly better than unfamiliar drivers.

Table 14: Z-Test Results for Priority

The next set of z-tests was conducted on the answers of the pedestrian priority.

Again, there was no significant difference between males and female or between those with and without exposure to educational information, but males and participants with some education on the subject did answer the question correctly slightly more than females and those without education. No significant difference was found in the 99 comparison of residence or familiarity. Three significant differences in age groups were between 25-34 year olds and 45-54 year olds, 25-34 year olds and 65 and older contributors, and 35-44 year olds and 45-54 year olds. In each of these comparisons, the younger age groups were significantly better at answering the pedestrian question correctly.

The comparison of the question pertaining to cyclists yielded no significant difference in the proportions of gender or exposure to education information despite males and those having sources of education tending to answer the question correctly more often. The 25-34 age group and the 35-44 age group was the only significant z- test in comparing ages. There was a significant difference in participants’ residence, where those living near a roundabout were able to correctly answer this question more often. Familiar drivers performed better on the cyclist question than both occasional users and unfamiliar drivers. No significant difference was found between occasional users and unfamiliar drivers.

Another set of z-tests was performed between completing all lane assignment questions and all five priority questions perfectly. The results are displayed in Table

15. Three comparisons were significant, which were 55-64 year olds, residences living near a roundabout, and unfamiliar participants. The 55-64 year old age group and participants living in a city/town with a roundabout answered the priority questions significantly better than the lane choice questions. Unfamiliar drivers answered the lane assignment series of questions significantly better. Any positive z scores 100 corresponded with that group performing better on the priority questions, and if the value was negative lane assignment questions were more easily answered.

Table 15: All Priority and Lane Assignment Questions Z-Test Results

From Table 15, groups which had more difficultly answering priority questions included 25-34 year olds, 45-54 year olds, residents living in cities/towns without roundabouts, and unfamiliar drivers. These groups were further studied by comparing the correct answers among car, pedestrian, and cyclist priority rules. The results are shown in Table 16. Car priority rules were the most difficult for all of these groups to answer. For both age groups, the cyclists question was the most difficult to answer.

Participants living in places without roundabouts and participants that were considered 101 unfamiliar, the vehicle priority series of questions were the most difficult questions on the survey questionnaire.

Table 16: Priority Rule Questions Z-Test Results

No analyses were going to be studied between demographics, but upon recording participants’ answers in statistical analysis software, the number of participants reporting having had no exposure to educational information required examination. Of all 252 survey participants, 43 said they had encountered educational information regarding proper navigation of a roundabout. There were 31 that marked coming across only one source, while six people checked two sources and the final six of these participants listed three or more sources of information they encountered. The most common source marked in the questionnaire was the newspaper article option, where 49% used this source. Driver’s education the second most common with 37% of these 43 participants marking this format of education. There is importance in noting that the majority, 11 participants (69%), of encounters with driver’s education occurred for participants in the group 18-24 years of age. During the recruitment of participants, two of the older participants having learned about roundabouts in driver’s education did 102 so through the completion of obtaining a CDL license. Education through watching a video was marked by 19%. Brochures and public meetings each were listed by 14% of participants. 7% used a website, and 2% gained information on roundabouts through the television or other educational entities.

Participants residing in the Logan, Ohio area showed a higher frequency of drivers with roundabout educational information. The double interchange roundabouts were established two years ago. The local newspaper had an article and public meetings were held, as stated by several participants, explaining the rules of the double interchange roundabouts. The concerning element of this is that just over three quarters of Logan/Lancaster residents (79 participants) reported not using any educational information. One resident stated seeing the article in the paper but did not read the article because the article was perceived as too long to read. Of the 26 participants from Logan encountering educational information sources, 19 of the contributors utilized the newspaper. This accounts for only 18.1% of this city’s participants.

6.2 Field Observations

The preliminary sample sizes were reported earlier in Chapter 5. Based on samples of speeds and gap acceptance, the real standard deviation of the data could be calculated for parameters. The required number of samples was calculated and compared to the actual number of samples collected. Table 17 shows the number collected is well above that which is required for a 95% level of confidence.

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Table 17: Sample Sizes Collected

6.2.1 Driver Errors

Video data collected revealed the approximate ADT of the intersection at

23,750 vehicles. Using the 15-minutes intervals, the peak hour was found to occur when 2,115 vehicles used the roundabout from 4:30pm to 5:30pm. Figure 20 shows the distribution of these vehicles over the peak hour. A left turn from campus was the most travelled path in the peak hour. Using the volume data collected, the Highway

Capacity Software 2010 was used to establish the level of service of the intersection.

Based on these passenger car and truck volumes, the peak hour factor, and lane configurations, the level of service for the overall roundabout in the peak hour is a LOS

B. The left lane on the 682 approach has a LOS C and the right a LOS A. Both 33 approach lanes have a LOS A, and the Richland approach lanes were both measured as a LOS B. The right lane exiting campus has a LOS B and the left lane has a LOS C. 104

Figure 20: PM Peak Hour Volume Distribution

On the control day, the observation was made that 953 drivers made a total of

956 errors. Three drivers made a second error after the first. The frequency and location of all driver errors is in Table 18. The incorrect left turn errors accounted for

75.7% of the errors with 724 incidences. 1,244 drivers made a left turn from the 33 approach to south Richland. This means 58.2% drivers on this route erred.

The second most common error made was choosing the wrong lane. This type of error occurred more frequently by drivers leaving campus. 41.3% of the 121 wrong lane choice errors happened when drivers leaving campus chose the right lane and should have chosen the left. 29.8% chose the left instead of the right leaving campus. 105

A 33 lane switch from right to left tallied the third highest lane choice error. 16 drivers originally chose the right lane when approaching. Troubles with choosing the proper lane on the Richland approach was slightly unexpected. Despite the right slip lane on

Richland and the overhead sign specifically present to guide Richland approach right turners, 10 people failed to choose this lane and had to switch from the through only lane to the 33 exit lane.

Another error with fairly high occurrences was taking the shortest path. 37 drivers were seen switching lanes traveling from campus to south Richland Avenue.

This error may have happened for drivers leaving Richland to go to campus, but this was not witnessed when reviewing the footage. The camera angle did not provide a clear enough image to determine if a car made this error. 17 of these error took place when traffic volumes were lowest throughout the 8:00 PM to 7:00 AM hours.

Non-compliant priority behaviors accounted for 61 errors. Priority abstaining comprised 35 of the priority errors. The most common location for this was seen was on the 33 approach in the right lane. There were 18 priority surrendering errors and 8 priority taking errors. There were no distinctive locations within the roundabout where priority surrendering occurred, but priority taking was more common of drivers exiting campus.

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Table 18: Total Daily and Peak Evening Hours Driver Errors

107

Chapter 4 mentioned only a select frequency of these errors were represented in the VISSIM unfamiliar model. Table 18 highlights the errors that happened over the course of 3:00 PM to 6:00 PM. The errors seemed evenly reduced overall except the shortest path error, where only one was simulated. At least one error from each category was modeled, except for an all the way around error.

6.2.2 Speed Observations

The distributions used for VISSIM model coding are shown in Table 19. The results of the t-tests conducted between the locations having both familiar and unfamiliar driver vehicle speeds are shown in Table 20. There was no significant difference found between the means on the campus approach and west and south circulating side of the roundabout. However, the means of the 33 approach were determined to be significantly different between familiar and unfamiliar drivers, where familiar drivers approached 1.54 miles per hour faster than unfamiliar drivers. Despite finding no significant difference in three of four tests, the unfamiliar speed data was applied to the microsimulation for network coding of the model.

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Table 19: Driver Speed Distributions Applied to VISSIM

Table 20: Vehicle Speeds T-Test Results

6.2.3 Gap Acceptance

The analyses of variance performed during the move-in weekend yielded varying results. Accepted gaps produced no significant differences in the means 109 between the accepted gaps of Thursday, Friday, or Saturday, meaning the null hypothesis failed to be rejected. These results are shown in Table 21. The results of the rejected gaps in Table 22 showed a significant difference among all three days. The null hypothesis was rejected, and the day for each set of rejected gaps had an effect on the data.

Table 21: ANOVA Contrast Results of Accepted Gaps

Table 22: ANOVA Contrast Results of Rejected Gaps

All accepted and rejected gaps were respectively combined. The unfamiliar gaps of move-in weekend generated no significant difference when compared to the familiar driver gap data. In both instances of accepted and rejected gaps, the null hypothesis failed to be rejected. Familiarity did not have a significant effect on gap acceptance. These results are shown in Table 23.

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Table 23: Gap Acceptance T-Test Results

Raff’s method of estimating critical gap yielded a critical gap of 3.86 seconds for familiar driver. This is value of the intersection of the two curves in Figure 21. The following equations are the trend lines and R-squared statistical measures of the accepted and rejected gaps for the control day. R-squared values show the trend lines fit the data well.

Accepted Gaps: y = 0.0027x6 − 0.1063x5 + 1.7287x4 − 14.749x3 + 69.912x2 − 159.58x + 134.72

R2 = 0.9998

Rejected Gaps: y = −0.016x6 + 0.4897x5 − 5.9719x4 + 36.243x3 − 108.09x2 + 113.49x + 61.063

R2 = 0.9992

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Figure 21: Gap Acceptance Cumulative Distribution Curve of Familiar Drivers

Figure 22 depicts the accepted and rejected gaps of the move-in weekend traffic.

The critical gap, also found through Raff’s method, is 4.07 seconds. Equations representing the data and the R-squared values are listed:

Accepted Gap: y = −0.0049x6 + 0.1717x5 − 2.3863x4 + 16.271x3 − 54.146x2 + 85.484x − 50.992

R2 = 0.9998

Rejected Gap: y = −0.0006x6 + 0.2072x5 − 2.791x4 + 18.08x3 − 53.566x2 + 36.342 + 93.808

R2 = 0.9995 112

Figure 22: Gap Acceptance Cumulative Distribution Curve of Unfamiliar Drivers

The critical gaps calculated did not accurately model the queues in the Athens roundabout in VISSIM. Through trial and error, the gap specified in the familiar model that most accurately represented queue lengths was 0.90 seconds. The calibration input for the unfamiliar scenario was 0.95 seconds.

6.3 VISSIM

There were two parameters evaluated from the VISSIM output. The first is the average queue length. Queue length data was assessed by lane for the campus and 682 approaches and by the entire approach for the 33 and Richland approaches. Based on how the familiar scenario was drawn in VISSIM, a single queue counter was inserted 113 over both lanes. The second parameter was delay, which was evaluated on an approach basis and also as an entire intersection.

The results of the t-tests used to compare average queue lengths are shown in

Table 24. Queue counters combined both 33 Richland approach lanes except for the slip lane. Both lanes on the campus and 682 approaches and the combined lanes of the

Richland approach had significantly different average queues. The only non-significant difference was found between the means of the 33 approach queues, where the queues on US 33 were not affected by familiarity. The other results show queue lengths of unfamiliar scenario are significantly longer than those of the familiar scenario.

Table 24: Average Queue Length T-Test Results

Delay also showed significant differences in the mean delay of each approach.

The familiar scenario had significantly lower mean values of delay on all approaches than the unfamiliar scenario. These results can be viewed in Table 25. As an entire 114 intersection, the mean delay is 14.46 seconds per vehicle in the familiar scenario.

Delay is 22.73 seconds per vehicle in the unfamiliar scenario. This difference was found to be statistically significant, as shown in Table 26.

Table 25: Approach Delay T-Test Results

Table 26: Intersection Delay T-Test Results

The level of service of the entire intersection and by lane were mentioned earlier. Those results accompany the results of delay by lane and approach in Table

27. VISSIM yielded results of 5.32, 20.84, 15.74, and 15.92 seconds per vehicle for the

US 33, campus, 682, and Richland approaches, respectively. The Highway Capacity

Software results were very similar with 7.47, 19.4, 13.46, and 11.12 seconds per vehicle for the same approaches. The greatest difference was seen between the

Richland approach delays. HCS estimated the intersection delay to be 14.37 seconds 115 per vehicle, while the familiar model generated a delay of 14.46 sec/vehicle for the entire intersection. Since all but the 33 approach familiar driver delays higher than the

HCS results, the difference between the unfamiliar model results and HCS would be significant, as well.

Table 27: Highway Capacity Software Results

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CHAPTER 7: CONCLUSIONS

7.1 Questionnaire

Questions regarding how driver characteristics influence proper decision making can be answered by the results of the survey. Gender did not have an effect on proper decision making. Although, males tended to answer all questions correct more often than females, the differences were not significant enough to conclude that gender led to more knowledge of roundabouts. Age, however, did have an effect on the questionnaire answers. Younger drivers had significantly more knowledge regarding lane choice and priority of cars and pedestrians. As age increased, knowledge of lane assignment and these priority rule decreased. The only question age did not have an effect on was that regarding cyclists using a roundabout.

Residence did show significant differences in roundabout knowledge.

Participants living within 15 miles of a roundabout performed significantly better on all lane assignment and priority questions on the survey, with the exception of the pedestrian priority question, where there was no significant difference. These are results that should be expected. Residents of cities with roundabouts should be more experienced, and therefore, more familiar with the rule of roundabout navigation. This complements the relationship between familiarity and proper decision making.

Familiar drivers had significantly better responses than unfamiliar drivers in every question except the question on pedestrians.

Overall, the answers to the pedestrian question were mostly correct. Pedestrian rules are not particular to roundabouts. At a crosswalk that lack traffic controls, 117 pedestrians have the right of way when the pedestrians are in the half of the roadway of the approaching traffic. Most road users understand this law, and that knowledge can be applied to roundabouts to properly answer the survey question through rule-based behavior. This is likely the explanation as to why most groups high a majority of correct responses and why the comparisons were non-significant.

The other characteristic of drivers examined was the participant’s use of education information. There was no significant difference between drivers reporting having and those having not encountered roundabout education information. The questionnaire verified education covering roundabout rules is not widespread with how few of participants responded yes to this question. Drivers cannot apply rule-based behaviors at roundabouts because they are very different from other types of intersections and, thus, leads to confusion. Without previous education, unfamiliar drivers are forced to try to apply progressive thinking (knowledge-based behaviors) under pressure when approaching roundabouts. Under the circumstances of a lack of public education on roundabouts, signs could have an impact on educating the unfamiliar driver on an approach. Future research on the impact of signage is necessary. Some diagrammatic guide signs are not as detailed as others, whereas others show the pathways of the multiple lanes. The survey was designed to use a simple diagrammatic sign where participants also had to depend on the pavement markings.

With 75% of unfamiliar drivers unable to answer Question 8 (the example in Figure

15), drivers are not using the sign in concurrence with pavement markings to choose the 118 proper lane(s). More conclusive signs should be used, where the pavement markings are only necessary as a precaution.

The extent, whether great or small, to which public officials are trying to educate the public on roundabout operations and navigation is not going far. If an article is put in the local newspaper and approximately 18.1% are using this source, then alternative methods need to be considered. There is the possibility that some of the Logan/Lancaster participants could have been previously familiar with roundabouts through various driving experiences before the double interchange roundabouts were constructed, but a higher percentage was still expected from this particular group.

7.2 Field Observations

Unfamiliar drivers seemed to approach the roundabout at slightly lower speeds, but the difference of means was not large enough to conclude that there may be some hesitation of unfamiliar drivers using the intersection. The same hold true for the accepted and rejected gaps found for the familiar and unfamiliar drivers. Unfamiliar drivers accepted and rejected marginally longer gaps than familiar drivers. The critical gaps found in this study are shorter than the critical gaps discussed in Chapter 2 from the NCHRP 572 report and the HCM 2000. Both sources reported typical values for critical gap of 4.1 seconds or higher.

The control day exhibited worse driving behaviors than what would be expected of familiar drivers and leads to the question of whether they fully understand lane restrictions and how to properly navigate a roundabout. Based on patterns seen, some 119 errors may be occurring intentionally. The shortest path error is more frequently in off- peak hours when few other cars are using the roundabout. At these times, there is almost certainly no impact to other vehicles, and the driver erring is able to maintain his or her speed that deflection of the right lane would normally decrease.

The disproportionate amount of incorrect left turn errors causes concern when this path is most likely travelled by familiar drivers. A possible explanation is that drivers have been making this error since the roundabout was open. If drivers have had no negative experiences making this error, they may not actually understand that this behavior as an error and could lead to a collision.

An error that was deliberate was the all the way around error when the driver entered from Richland and circled around the central island multiple times before exiting to west on SR 682. Some drivers miss their exit or choose the wrong lane when approaching. Rather than risk a sideswipe crash, they circle around the central island and exit properly. However, 18 times around the roundabout is too excessive an amount to be considered a mistake.

7.3 VISSIM

Despite the differences in familiar and unfamiliar driver’s speeds and gap acceptance being statistically equal, these differences in combination with the driver errors caused a large enough difference in the models to be a significant difference between the two types of drivers. All approaches or lanes, except for the 33 approach, saw a significant difference in average queue length during the peak hour. Delay was 120 of vehicles on each approach and of the entire intersection increased considerably when unfamiliar driver parameters were applied to the model.

The left lanes of the campus and 682 approaches both had a level of service C.

Entry lane capacity of this campus approach is 735. Approximately 530 vehicles are using this lane, so this measure is not unexpected with the proportion of vehicles counted to capacity. The volume of the left lane on the 682 approach is less than half of the 553 entry capacity associated with that lane. However, with the high volume on the two conflicting lanes of traffic, delay is attributing to the average LOS.

The study shows unfamiliarity of drivers has a significant impact on the operations of a roundabout. Addressing the difficulties unfamiliar drivers have in understanding lane assignment and vehicle priority rules and could alleviate the driver errors. With familiar drivers also making errors, further education on the consequences of incorrect driver behaviors should also be publicized.

7.4 Overall Conclusions

Based on familiarity and age, there is a difference in level of knowledge drivers have of roundabout navigation. Familiarity had significant impact where participants with more experience were capable of answering more questions correctly. Younger participants also exhibited more correct answers throughout the questionnaire than older generations. A decline in driver knowledge was seen as a participant’s age increased. Despite using familiar and unfamiliar descriptions of drivers, field 121 observations showed familiar drivers seem to be making errors in terms of lane assignment and priority.

There is little difference between familiar and unfamiliar drivers in terms of gap acceptance and approach and circulating speeds. Familiar drivers tend to accept shorter gaps and driver faster, but the differences were determined to be non-significant. While familiar drivers may be better at making proper driving in roundabouts and more comfortable in terms of speeds and gap acceptance, many driver errors still occur due to these drivers leading to the suggestion that even familiar drivers do not fully understand the rules and restrictions of priority and lane changes, respectively.

In applying the attributes of familiar and unfamiliar drivers to microsimulation models, the capacity constraints of delay and average queue length are significantly affected. Gap acceptance and speeds associated with unfamiliar drivers, as well as driver errors, caused significantly longer delays and average queue lengths on every approach, expect for the 33 approach where the queue length as not significantly different. The overall delay of the intersection was significantly higher when considering unfamiliar drivers in the model.

7.5 Study Limitations

The first limitation of the study pertains to the questionnaire. In any survey, researchers depend on the honesty of participants when answering questions. Error of answers could affect the results found. Over the course of conducting the survey, many participants remarked how they did not know an answer to a question, yet, “I don’t 122 know” was rarely chosen. An unwillingness to formally admit that the participant did not know a particular answer led to guessing. Marking “I don’t know” was considered incorrect, so if another incorrect answer was guessed, the results would not have been affected. However, if the correct answer was guessed, the survey results would be.

Another limitation regarding the survey is only county fair attendees were included in the study.

The field observations were collected on days when drivers in the roundabout were anticipated to consist of mainly familiar drivers or when unfamiliar drivers were expected to be among the usual daily traffic. A limitation of these observations was there was no method in the study to determine which vehicles were familiar or unfamiliar.

A limitation regarding the traffic microsimulation happened when modeling the unfamiliar scenario. An all the way around error occurred in the three hour span in which errors were taken for the model. As vehicle routes are configured in VISSIM, the shortest distance is highlighted. This aspect of VISSIM arose quite often when mapping driver errors, but the model could be manipulated by adjusting connectors attain the desired route. With the all the way around error, manipulation was not possible. Results of delay and queue length were already found to be significant, so the absence of this error does not stand between significant and non-significant results.

The error would add one more conflicting vehicle for all four approaches. 123

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APPENDIX B: RECRUITMENT SCRIPT

Questionnaire Recruitment Script

Hello, I was wondering if you would be interested in participating in a research study regarding driver behavior and knowledge of roundabouts. The questionnaire will only require approximately 10 minutes of your time. The survey will remain anonymous in the study and your performance will be identified by a time and date stamp only. Your participation in study would be greatly appreciated and may benefit society by helping to identify ways to improve the driving task and make roundabouts safer. Would you be interested in participating in this research study?

(If potential participant is not interested, continue with the following statement:)

Thank you for your time, and enjoy the rest of the day.

(If potential participant answers in the affirmative, continue with the following statement:)

Do you have a valid driver’s license, and are you at least 18 years of age?

(If the participant answers no to either of these criterion, continue with the following statement:)

I am sorry for the inconvenience. I cannot utilize your survey in the research study as a result of your response. Thank you for your time, and enjoy the rest of the day.

(If the participant answers yes, continue with the following statement:)

Great! Here is the questionnaire you can complete. (Hand participant clipboard with a questionnaire and pen.) Please read the guidelines on the front page and fill out the survey to the best of your knowledge.

(When survey has been completed and returned, continue with the following statement:)

Thank you so much for your contribution to this study. Enjoy the rest of the day.

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APPENDIX C: QUESTIONNAIRE

Analysis of Driver Behavior at Single Lane and Multi-lane Roundabouts

Ohio University is conducting a survey to investigate driver behavior at roundabouts. Benefits of the study are important to society. The results obtained by will be used to provide insight on the enhancement of roundabout education and design to improve driver navigation at roundabouts.

As a participant, you must be 18 years of age or older and have a valid driver’s license. If you do not meet both of these requirements, please do not complete the survey, and return the survey to the experimenter.

The survey contains 16 questions, and your participation in the study will last approximately 10 minutes. Should you wish to stop participation, you may exit the survey by handing the form back to the experimenter. No risks or discomforts are anticipated. Your study information will be kept confidential through the omission of questions asking for identifiers (i.e. name, etc.).

Further questions about the study being conducted may be asked at any time. Any inquiries specific to content in the questions of the survey may be asked following the return of the survey to the experimenter.

Please check the appropriate box, or boxes where applicable, in each question.

Below are images of normal single lane and multi-lane roundabout.

Single Lane Roundabout Multi-lane Roundabout Section A. General Information 1) Gender 147

 Male  Female

2) Which range does your age fall within?  18-24  45-54  25-34  55-64  35-44  65 or over

3) In what city do you reside? ______

4) Have you ever driven through a roundabout?  Yes  No  As a passenger

5) How often do you drive through roundabouts?  I never drive through roundabouts  Rarely  Once a month  Several times a month  Several times a week  Once a week  One or more times a day

6) What types of roundabouts have you driven through?  Single lane roundabouts  Multi-lane roundabouts  Both  Neither

7) Have you ever encountered educational information regarding how to navigate a roundabout?  No  Yes

If yes, what types of information? (Check all that may apply)  Brochure  Public Meeting  Newspaper Article  Video  Website  Driver’s Education Course  Television  Other ______Section B. Lane Assignment

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Use the above roundabout approach image and guide sign to answer questions 8-11. A driver is approaching a roundabout with two entrance lanes.

8) Based on the images above, which lane should the driver choose in order to continue traveling west on Route 30?  The right lane  The left lane  Either lane  I don’t know

9) Based on the images above, which lane should the driver choose in order to travel south on Route 18?  The right lane  The left lane  Either lane  I don’t know

10) Based on the images above, which lane should the driver choose in order to make a U-turn?  The right lane  The left lane  Either lane  A U-turn is not allowed  I don’t know

11) Based on the images above, which lane should the driver choose in order to travel north on Route 18?  The right lane  The left lane  Either lane  I don’t know Section C. Priority

Use the following roundabout image to answer questions 12-14.

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12) In the above scenario, what is the best action the green car can make?  The driver should always stop before entering the roundabout  The driver has the right of way  The driver cannot safely enter and should wait for the yellow car to exit  The driver can safely enter before the yellow car approaches  I don’t know

13) In the above scenario, what should the yellow car do?  Stop and allow the red car to enter into the roundabout  Continue driving in the circulating lane until reaching the driver’s exit  I don’t know

14) In the above scenario, what should the red car do?  Stop and wait for the yellow car to pass by in the circulating lane  Enter into the circulating lane  I don’t know

Use the following image to answer question 15.

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15) As the car plans to exit the roundabout, the pedestrians reach the crosswalk of the exiting lane. Does the car have the right-of-way?  Yes, the car needs to move out of the way of other vehicles in the roundabout  No, the pedestrians have priority over vehicles  I don’t know

Use the following image to answer question 16.

16) In the above image, the cyclist is circulating in the roundabout, and the car is entering the roundabout. Which road user has made a mistake?  The cyclist because he does not have the right-of-way  The driver because he does not have the right-of-way  The cyclist because he should not be in the circulating lane  I don’t know

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APPENDIX D: QUESTIONNAIRE CROSS TABULATION TABLES

Table 28: Cross Tabulation of Gender vs. Yellow Car

Table 29: Cross Tabulation of Age vs. Through Movement

Table 30: Cross Tabulation of Age vs. Left Movement

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Table 31: Cross Tabulation of Age vs. Yellow Car

Table 32: Cross Tabulation of Age vs. Red Car

Table 33: Cross Tabulation of Residence vs. Through Movement

Table 34: Cross Tabulation of Residence vs. Left Turn Movement

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Table 35: Cross Tabulation of Residence vs. Green Car

Table 36: Cross Tabulation of Residence vs. Cyclists

Table 37: Cross Tabulation of Experience vs. Left Turn Movement

Table 38: Cross Tabulation of Experience vs. Right Turn Movement

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Table 39: Cross Tabulation of Experience vs. Yellow Car

Table 40: Cross Tabulation of Experience vs. Red Car

Table 41: Cross Tabulation of Experience vs. Pedestrians

Table 42: Cross Tabulation of Experience vs. Cyclists

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Table 43: Cross Tabulation of Frequency vs. Through Movement

Table 44: Cross Tabulation of Frequency vs. Green Car

Table 45: Cross Tabulation of Frequency vs. Red Car

Table 46: Cross Tabulation of Frequency vs. Cyclists

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