Comparison Between Familiar and Unfamiliar Driver Performance in a Multi-Lane
Roundabout: A Case Study in Athens, Ohio
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
Ashley N. Chucray
August 2013
© 2013 Ashley N. Chucray. All Rights Reserved.
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This thesis titled
Comparison Between Familiar and Unfamiliar Driver Performance in a Multi-Lane
Roundabout: A Case Study in Athens, Ohio
by
ASHLEY N. CHUCRAY
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
CHUCRAY, ASHLEY N., M.S., August 2013, Civil Engineering
Comparison Between Familiar and Unfamiliar Driver Performance in a Multi-Lane
Roundabout: A Case Study in Athens, Ohio
Director of Thesis: Deborah S. McAvoy
A study was conducted in order to compare driver performance based on driver level of familiarity. The location for the study was a multilane roundabout in Athens,
Ohio, the gateway to Ohio University’s campus, making this a unique location. Located to the east of the roundabout is a highway system that leads to most large surrounding cities. Two legs were focused on for this study, the leg that leads to the campus and the leg that leads to this highway system. Familiarity was defined by the presence of large university events such as graduation. The measures of effectiveness for this research were approaching speeds, circulating speeds, critical gap, entrance behaviors, and delay. The objective of this study was to determine what effect familiarity has on these measures of effectiveness. The results from this study showed that unfamiliar drivers had a tendency to drive slower for both circulating and approaching speeds than familiar drivers.
Familiar and unfamiliar drivers performed the same for critical cap and inappropriate driver behavior. Also, no distinctive results were extracted from the delay parameter due to the calculation of delay not incorporating driver performance. 4
DEDICATION
To my parents, Jill and Jerry Chucray, and my sister, Amanda Chucray. For believing in
me, helping me through the tough times, and supporting my academic decisions.
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ACKNOWLEDGMENTS
I would like to thank my advisor, Dr. Deborah McAvoy, for seeing my potential and helping me through every step of my college career, both graduate and undergraduate. Without all of her support, academically and emotionally, neither of my degrees would be possible.
I would also like to thank all of my committee members for their service and helpful suggestions for improving my thesis. Additionally, I would like to thank all of my fellow co-workers in the Safety and Human Factors Facility for assisting me with the collection, extraction, and analysis of my data.
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TABLE OF CONTENTS
Page
Abstract ...... 3 Dedication ...... 4 Acknowledgments...... 5 List of Tables ...... 9 List of Figures ...... 10 Chapter 1: Introduction ...... 11 1.1 Types of Roundabouts ...... 12 1.2 When Roundabouts are Ideal ...... 13 1.3 Advantages of Roundabouts ...... 15 1.3.1 Cost ...... 15 1.3.2 Pollution ...... 16 1.3.3 Delay ...... 16 1.3.4 Safety ...... 17 1.4 Rationale for This Study ...... 19 Chapter 2: Background ...... 21 2.1 Familiarity of Drivers ...... 21 2.1.1 How Familiarity is Defined ...... 21 2.1.2 Public Opinion of Roundabouts ...... 22 2.1.3 Operational Knowledge ...... 25 2.2 Capacity and Saturation Levels ...... 28 2.2.1 Traffic Volume ...... 28 2.2.2 Capacity ...... 29 2.2.3 Saturation Level ...... 32 2.3 Pedestrian Consideration ...... 34 2.3.1 Pedestrians Impacts ...... 34 2.3.2 Reduction of Pedestrian Impacts ...... 35 2.3.3 Omission of Pedestrian Effects ...... 37 2.4 Delay ...... 38 7
2.4.1 Control Delay Measurement ...... 39 2.4.2 Roundabout Effects on Delay ...... 41 2.5 Approach and Circulating Speed ...... 42 2.5.1 Speed Measurement ...... 42 2.5.2 Roundabout Speeds ...... 43 2.6 Vehicle Classification ...... 44 2.6.1 Vehicle Classification Determination ...... 44 2.6.2 Behavior Changes Based on Vehicle Types ...... 45 2.7 Gap ...... 45 2.7.1 Gap Acceptance ...... 46 2.7.2 Gap Measurement ...... 46 2.7.3 Typical Gap Lengths ...... 48 2.7.4 Gap Impacts ...... 49 2.8 Sample Size ...... 50 Chapter 3: Site Description ...... 52 Chapter 4: Methodology ...... 61 4.1 Data Collection and Equipment ...... 63 4.1.1 Manual Methods ...... 63 4.1.2 Pneumatic Tubes ...... 63 4.1.3Radar Detector ...... 68 4.1.4 Video Camera ...... 69 4.2 Sample Size Determination ...... 74 Chapter 5: Statistical Methodology ...... 79 5.1 Tests of Normality and Homogeneous Variance ...... 81 5.2 Solutions to Problems with Assumptions ...... 83 5.3 One-way ANOVA ...... 85 5.4 Post Hoc Tests ...... 87 5.5 Nonparametric ANOVA ...... 88 5.6 Chi-Squared Test ...... 89 5.7 Effect Size ...... 89 Chapter 6: Results ...... 92 8
6.1 Verifications ...... 92 6.1.1 Familiarity ...... 92 6.1.2 Sample Size ...... 93 6.2 Approaching Speeds ...... 97 6.3 Circulating Speeds ...... 101 6.4 Critical Gap ...... 106 6.5 Driver Entrance Behaviors ...... 111 6.6 Delay ...... 113 Chapter 7: Conclusions ...... 118 7.1 Approach Speeds ...... 118 7.2 Circulating Speeds ...... 119 7.3 Critical Gap ...... 120 7.4 Entrance Behaviors ...... 121 7.5 Delay ...... 121 7.6 Recommendations for Future Research ...... 123 References ...... 125
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LIST OF TABLES
Page
Table 1. Public attitude towards roundabouts ...... 24 Table 2. Level of service based on delay ...... 40 Table 3. Existing traffic volume in 1999 ...... 59 Table 4. Projected traffic volume for 2010 ...... 59 Table 5. Familiar driver initial sample size calculations (Zβ= 0.842, Zα/2=-1.96) ...... 77 Table 6. Unfamiliar driver initial sample size calculations (Zβ= 0.842, Zα/2=-1.96) ...... 78 Table 7. Average percent of Athen's county vehicles ...... 93 Table 8. Familiar driver final sample size calculations (Zβ= 0.842, Zα/2=-1.96) ...... 95 Table 9. Unfamiliar driver final sample size calculations (Zβ= 0.842, Zα/2=-1.96) ...... 96 Table 10. Approach speed descriptives ...... 98 Table 11. Results from ANOVA and Welch's tests ...... 100 Table 12. Significance values of key group comparisons...... 101 Table 13. Circulating speed descriptives ...... 102 Table 14. Circulating speed results for ANOVA and Welch's tests ...... 104 Table 15. Circulating speeds post hoc test key results ...... 105 Table 16. Critical gap descriptives ...... 106 Table 17. Entrance behaviors descriptives...... 112 Table 18. Entrance behaviors significance results ...... 112 Table 19. Non-parametric test results for key comparisons ...... 113 Table 20. Delay descriptives ...... 114 Table 21. Delay Significance Results ...... 115 Table 22. Non-parametric test result for key comparisons ...... 116
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LIST OF FIGURES
Page
Figure 1. Diagram of typical roundabout features ...... 11 Figure 2. Conflict points by intersection type ...... 18 Figure 3. Diagram of a typical splitter island ...... 36 Figure 4. Diagram of a typical slip lane ...... 42 Figure 5. Aerial view of Athens, Ohio ...... 53 Figure 6. Aerial view of the Athen's roundabout location ...... 54 Figure 7. Aerial view of Athen's roundabout lane configuration ...... 58 Figure 8. Pneumatic tube locations ...... 65 Figure 9. EZ belt road tube layout 5 for Richland Avenue ...... 67 Figure 10. D-tube road tube layout 11 for S.R. 682 ...... 67 Figure 11. Location for collection of circulating speeds ...... 69 Figure 12. Example of approach speed histogram ...... 99 Figure 13. Example of a circulating speed histogram ...... 103 Figure 14. Example of accepted gap histogram ...... 109 Figure 15. Example of rejected gap histogram ...... 110
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CHAPTER 1: INTRODUCTION
Roundabouts are intersections with a circular pattern where traffic flows in a counterclockwise direction around a center island [1, 2]. These geometric patterns cause drivers to reduce speeds when navigating through a roundabout. For most roundabouts, traffic is required to yield when entering the circulating flow. Traffic is not required to stop when entering the roundabout if there is no cross traffic present. Figure 1 shows an example roundabout with several key features labeled [3]. These features will be discussed in more detail later.
Figure 1. Diagram of typical roundabout features
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There are a few general guidelines in order to safely navigate through a roundabout. First, drivers must reduce speeds in order to merge and maneuver through the circular diversion [1]. Drivers entering the roundabout are required to yield to traffic within the roundabout as well as pedestrians and bicyclists crossing the roadway.
Pedestrian and bicyclists typically cross at a crosswalk located on a leg of the roundabout. Signs or lane markings near the roundabout indicate which lane to use, if there is more than one lane. The left lane will be used for left turning movement and the right lane will be used for right turning movements, but the through lane may vary and multiple lanes may be used for certain turning movements. Also, drivers are not supposed to change lanes within a roundabout or pass other vehicles.
1.1 Types of Roundabouts
There are five main types of roundabouts. First, conventional roundabouts allow other roadways to join by means of a circular roadway around a center island that has a diameter of at least 82 feet [4, 5]. Next, small roundabouts have a center island that is between 13 feet and 82 feet in diameter with flared approaches. Mini-roundabouts have a center island of less than 13 feet that is slightly elevated. A double roundabout contains two small or mini roundabouts in close vicinity that may be connected by a small stretch of roadway. Finally, multi-roundabouts contain three or more small or mini roundabouts which, like double roundabouts, can be continuous or connected with roadway.
Roundabouts may contain a single lane or multiple lanes for travel. As of 2008,
69 percent of roundabouts in the United States had a single lane, 26 percent had multiple lanes, and five percent were unknown [6]. Additionally, 63 percent of roundabouts in the 13
United States had four approaches. Only 30 percent of roundabouts in 2008 had three approaches, five percent had five or more approaches, and one percent were either unknown or had a different configuration. The number of approach legs and circulating lanes affect capacity. Mini roundabouts with four approach legs have a capacity of up to
15,000 vehicles per day [7]. Single lane roundabouts with the same number of legs have a capacity of up to 25,000 vehicles per day, while a multi-lane roundabout with two lanes and four approach legs can contain volumes of up to 45,000 vehicles per day. This is a major factor as to why different roundabouts are appropriate for different locations.
1.2 When Roundabouts are Ideal
Roundabouts are only appropriate under certain circumstances. With low entering volumes, roundabouts have been proven to have better performance in terms of delay than signalized intersections and stop controlled (two-way and all-way) intersections [8].
For all levels of volume, roundabouts perform better than all-way stop controlled intersections based on both delay and crash reductions. However, signalized intersections are ideal for areas with high traffic volumes because effectiveness of a roundabout reduces as number of lanes increases. Roundabouts are located in both urban and rural areas, and can be found on local and collector roads, arterial roads, and freeway exit and entrance ramps [4]. Collector roads provide access to local roads such as residential areas while arterial roadways allow traffic to travel from collector roads to freeways.
Roundabouts perform well in residential subdivisions because they are a low noise option with low maintenance [7]. As compared to stop controlled intersections, roundabouts have lower noise due to less extreme accelerating and decelerating actions. 14
Roundabouts are considered low maintenance because traffic signals are often not used and less roadway damage is caused as a result of extreme change in movements, which is typical for stop controlled intersections. Roundabouts also perform well for corridors or locations with a limit on roadway width available in between intersections because the reduction of delay allows for the use of fewer lanes. In other words, addition of lanes may not be necessary in locations that are limited such as with bridges over a freeway, for example. Either a smaller bridge could be constructed or an existing bridge could still be used if the signals or stop signs were replaced with roundabouts which would save money. Roundabouts have also been effective when replacing intersections with high crash counts and delay due to the merging pattern creating less conflicting points and allowing for drivers to travel without making a complete stop if it is not necessary.
On the other hand, there a situations where a roundabout may not be ideal. The basic problem includes areas with small right-of-way due to the larger right-of-way size needed for roundabout installation [7]. Areas with high truck volumes and oversized vehicles could also be problematic due to difficulties when navigating through the roundabout. Locations where queue may form up to and through a roundabout may not be ideal, such as railroad crossings, drawbridges, and over capacity signalized intersections due to blockage of these problematic locations. Another potential problem is locations with both heavy vehicle and pedestrian traffic because pedestrians will have problems crossing the roadway safely. While these factors can affect the performance of a roundabout, sites with one or more of these features may still be applicable for roundabout installation. 15
1.3 Advantages of Roundabouts
There are many advantages to roundabouts. They are aesthetically pleasing with a typical center island with greenery or art work, have quieter operations due to less complete stopping motions, and allow safe U-turns which are all beneficial for residential areas [1, 7]. Pedestrian safety can also be increased due to slower travel speeds and the use of splitter islands which allows pedestrians to focus on one direction of travel at a time [7]. Additionally, cost, reduction of pollution and delay, and increased safety are major advantages of roundabouts.
1.3.1 Cost
Roundabouts may also be cost efficient. Construction of a roundabout often has a similar cost to traditional intersections when they are installed [7]. However, a roundabout typically has lower maintenance costs than a signalized intersection due to the lack of need to install, maintain, and power traffic signals. Additionally, roundabouts often last longer than other intersections before needing to have major repairs because of less wear and tear on the roadway from complete stopping movements. Frequently, roundabouts use less pavement than traditional intersections because of the reduction in the necessity of lanes resulting from a reduction of delay, which will be discussed more later [1]. This may lead to other reduction in costs of widening a roadway. For example, construction of a wider bridge may not be necessary with the installation of a roundabout at a nearby intersection. 16
1.3.2 Pollution
Vehicle emissions are reduced with the use of a roundabout due to less idling time associated with traffic signal or stop controlled delay, fewer complete stops, and less acceleration required for vehicles to return to traveling speeds from a complete stop [1].
A study conducted by Nemani showed that the number of vehicles stopping and lengths of queues decreased by 15 to 37 percent among three sites when those sites were converted from a two-way stop controlled intersection to a roundabout [9]. Uddin showed that roundabouts reduce idling time by 77 percent and fuel wastage by 56 percent when compared to signalized intersections [10].
1.3.3 Delay
Roundabouts have less delay than other types of intersections due to a reduction in unnecessary stopping [7]. Nemani found that on average, delay was reduced by 10-15 percent after modifying the intersection from a two-way stop control to a roundabout [9].
The Insurance Institute for Highway Safety showed a reduction in delay by 13, 19, and
23 percent after three stop controlled and traditional intersections were converted to roundabouts [11]. The reduction in delay is the largest during non-peak traffic hours because unnecessary stopping is reduced which occurs more during non-peak hours than peak hours [7]. As a result of reduced delay, lane efficiency increased and the number of travel lanes may be able to be reduced between intersections. As stated previously, this can be beneficial for using existing bridges over a freeway as opposed to constructing a larger new bridge, which leads to a reduction in construction costs. 17
1.3.4 Safety
Safety is often viewed as one of the most important advantages to roundabouts.
Roundabouts have lower crash rates than signalized intersections. Persaud et al. studied crash data for 23 intersections that were converted to roundabouts [12]. The study estimated that all crash types would be reduced by 40 percent and 80 percent for injury related crashes. The researchers also found that incapacitating and fatal injuries would decrease by 89 percent. Additionally, Rodegerts et al. conducted a study of 55 sites including signalized, all-way stop, and two-way stop intersections that were converted to roundabouts [13]. The study found that in total all crash types were reduced by about 35 percent and injury crashes were reduced by about 76 percent.
Roundabouts increase safety because they are designed to reduce conflict points
[7]. Traditional intersections have 32 points of conflict but roundabouts only have 8 [1].
The reason for the reduction in conflict points is due to the merging pattern of the roundabout. This pattern leads to the elimination of the left-turning movements and through movements. These movements are still allowed after a right turning movement is used to merge with circulating traffic. These conflicting points can be seen in Figure 2 below [1]. 18
Figure 2. Conflict points by intersection type
The design of roundabouts eliminates the chance for both head on and right angle crashes [14]. These two are both considered dangerous types of crashes. In addition to dangerous crash types being eliminated, the severity of crashes is also reduced with roundabouts because all drivers navigating a roundabout travel at low speeds [15].
Severity of crashes is associated with the extent of injuries resulting from a crash. Unlike traditional intersections, roundabouts always have a small variation in speed between vehicles which increases safety [14]. Crashes with large speed variation tend to be more dangerous. 19
1.4 Rationale for This Study
The construction of roundabouts in the United States began in 1905 [9]. By 2000, there were about 200 roundabouts in the United States [6]. Then in 2005, about 700 roundabouts existed. Roundabouts continued to grow in popularity and in 2007 there were about 950 roundabouts in the United States. Today, there are 1731 roundabouts recorded in existence in the United States [16]. This data shows that roundabouts are becoming increasingly more common. This may be due to factors that have previously been discussed such as crash reduction and reduced delay.
As more roundabouts are built in the United States, drivers are becoming more familiar with roundabout operation. Some researchers claim that driver familiarity impacts driver performance [5, 17]. Of the studies completed examining driver familiarity in roundabouts, the vast majority examined driver familiarity through public opinion surveys and very few actually compare driver familiarity with driver behavior
[18, 19, 20]. One study conducted by Zheng et al. collected the crash types for familiar and unfamiliar drivers and found that familiar drivers were involved in more rear-end crashes while unfamiliar drivers were involved in more sideswipe crashes [17].
There are many other parameters that are commonly studied to analyze driver performance. Researchers utilize measures of effectiveness such as gap, delay, approach and circulating speed, and congestion to evaluate driver performance in a variety of situations [5, 10, 21, 22, 23, 24, 25]. However, none of these studies correlated driver performance with familiarity. 20
The purpose of this study is to determine the impact of driver familiarity on driver behavior. Approach and circulating speeds, delay, gap, and entrance behaviors are the measures of effectiveness that will be analyzed. The effects of congestion and vehicle class were also considered. This research will discuss what other studies have done, the location of the roundabout studied, the methods for data collection, and the results from the analysis.
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CHAPTER 2: BACKGROUND
Several studies pertain to familiarity of drivers and their opinion about roundabouts. However, there is limited research on how driver familiarity impacts driver performance in roundabouts. Several measures of effectiveness will be discussed in this chapter such as delay, approach and circulating speeds, and gap. Additionally, the methods that other researchers have used to define familiarity of drivers will also be addressed. This chapter will also discuss how data is typically collected and what other studies have found.
2.1 Familiarity of Drivers
2.1.1 How Familiarity is Defined
Familiarity describes the extent of being well known such as to a person, concept, or area. More specifically, driver familiarity focuses on a driver’s level of experience with a roadway or with a roadway feature including roundabouts. Many different techniques may be used to classify roundabout drivers as familiar or unfamiliar.
However, the two main ways to describe driver familiarity for roundabouts are frequency of roundabout use and lifespan of a roundabout.
The study performed by Zheng et al. is an example of classifying familiarity based on frequency of roundabout use [17]. The researchers specifically used residency to describe familiarity. Drivers who lived in the same city as the roundabout were classified as familiar drivers. This assumed that drivers who lived in the town with the roundabout used the roundabout regularly. In contrast, drivers outside of the town were assumed to be unfamiliar with the roundabout. 22
Another example of this type of driver classification was demonstrated by
McKnight et al. [19]. The researchers classified drivers based on how often they traversed a roundabout. Those participants who admitted that they drove through a roundabout less than once per month were classified as unfamiliar with roundabouts. The participants who admitted to using a roundabout at least once per month, however, were categorized as familiar with roundabouts.
Retting et al. used the second method to define familiarity [20]. Retting et al. defined familiarity by the length of time a roundabout had been in operation. After a new roundabout was constructed, all drivers were considered unfamiliar because they lacked experience with the driving patterns. As time progressed, repetitive use of the roundabout resulted in increased familiarity. Survey data was collected before the roundabout was installed, during the first year of being open, and after a year of being open.
2.1.2 Public Opinion of Roundabouts
The public has varying opinions of roundabouts with the vast majority split on roundabout acceptance. Savolainen et al. concluded that 39 percent of people were strongly opposed to roundabouts while a similar percentage, 31, strongly favored roundabouts [18]. Another study found similar findings supporting that most opinions tend to strongly favor roundabouts or strongly oppose roundabouts, with lower percentages being neutral [26]. Factors for this difference in opinion may be due to age or level of experience, for example.
Another factor that was examined in many studies was age. A study based solely on older drivers determined that roundabouts are the most difficult, of all intersection 23 types, for this age group [27]. Savolainen et al. determined that as the driver’s age increased, the negative opinion of roundabouts also increased [18]. Retting et al. concluded that typically drivers from ages 18 to 34 (younger drivers) approved roundabouts more than drivers ages 65 and over (older drivers) [20]. However, even though older drivers seemed to approve of roundabouts less, two thirds of them approved of roundabouts one or more years after construction.
Driver familiarity can affect public opinions of roundabouts. Two ways to classify drivers based on familiarity are residency and duration of a roundabout’s life, as stated previously. Residency assumes that local drivers use the roundabout frequently. The results of a study by Savolainen et al. were subdivided by how recently respondents had driven through a roundabout [18]. The drivers who had driven through a roundabout the same day as the study preferred roundabouts the most strongly. The other time periods stated when motorists last drove through a roundabout were in the past week, past month, and past year. Drivers in the respective time periods, respectively, were 8.1, 13.6, and
21.6 percent less likely to prefer roundabouts than those who drove it the same day as the survey.
Drivers may be opposed to roundabouts in areas where they are not established yet. However, the opinions of drivers tend to change once drivers understand roundabout operation. Retting et al. conducted a survey at six different locations for three different stages: before construction, immediately following construction, and several years after construction [20]. The average opinions for all six locations showed that drivers favored roundabouts 34 percent before construction, 57 percent immediately following 24 construction, and 69 percent several years after construction. As time progressed, the amount of positive opinions of roundabouts increased.
Other studies showed the same results of increased popularity over time [28].
Jacquemart conducted a study to gather public opinion information before and after roundabout construction [29]. The public opinion information was based upon the Likert
Scale, where one was a very negative opinion, three was a neutral opinion, and five was a very positive opinion. In general, the initial opinion of the public was negative before construction and became positive after construction. This indicates that people approve of roundabouts the more they traversed through them. Table 1 shows in better detail the public opinion rather than just favoring or opposing roundabouts [29].
Table 1. Public attitude towards roundabouts
Public Opinion 1 2 3 4 5 Before Construction 23 45 18 14 0 After Construction 0 0 27 41 32
In addition to public opinion, Jacquemart collected data for multiple State
Departments of Transportation (DOTs) [29]. The 80 percent of DOTs who had not built roundabouts at this time were asked what the reasoning was for this. Over one-third of the DOTs stated concerns about drivers adjusting to roundabouts. A similar percentage of
DOTs responded that they were uncertain that roundabouts would function efficiently. 25
After identifying drivers whom disliked roundabouts, Retting et al. ascertained the source of the negative opinion regarding roundabouts [20]. The majority of drivers disliked roundabouts because they were thought to be confusing or unsafe. Participants were also asked open-ended questions on how roundabouts could be improved. Thirty- one percent of the people surveyed suggested having higher quality or additional traffic signs. The most frequent suggestions, sequentially from high to low, were to provide more public education and information, improve lighting, widen roundabouts, and lower traffic speeds. Other responses were to have better pavement markings and police traffic enforcement.
Public opinion tends to vary almost equally between strongly favoring and strongly opposing roundabouts, while few people have a neutral opinion [26]. Factors that may affect public opinion include age and driver familiarity. The relationship between age and popularity was found to be inversely proportional to each other [18, 20].
Studies also showed, using both methods of familiarity classification, that the more familiar drivers are the more they tend to favor roundabouts [18, 28, 29]. When surveyed, roundabouts were thought to be unsafe and confusing to those who opposed roundabouts
[20]. The most popular suggestion for improvement was to provide more information and education to the public.
2.1.3 Operational Knowledge
Both public opinion and driver performance could improve if drivers became more informed of roundabout policies and procedures. When almost 12,000 Michigan residents were asked who had the right of way in a roundabout, 4.7 percent thought that 26 the drivers already circulating the roundabout were supposed to yield and 1.7 percent were unsure [18]. In a different study, Inman et al. tested lane choice for five different roundabout layouts [30]. The results showed that correct lane selection only occured 69 percent of the time. These results reveal that there are still drivers who need to be informed of roundabout procedures.
While there is clearly a lack of roundabout knowledge, drivers that are familiar with roundabouts are known to navigate correctly through roundabouts. McKnight et al. determined that drivers who use a roundabout at least once per month (familiar drivers) were more knowledgeable with roundabout operations than drivers who drove less than once per month (unfamiliar drivers) [19].
In the study conducted by Zheng et al., an increase in circulating or approach speeds indicated local drivers were more comfortable using roundabouts [17]. However, these speeds resulted in a greater frequency of run-off-road, rear-end, and entering/circulating crashes, which are generally related to high travel speeds. Drivers who were not familiar with the roundabout had a higher proportion of sideswipe crashes due to inappropriate lane choice. Inappropriate lane choice is inversely related to the familiarity of the driver; however, repetitive use leads to a strong understanding of the travel patterns of the roundabout.
Jacquemart conducted a survey to obtain information about current methods being used by municipalities to inform drivers of proper roundabout navigation [29]. Of the respondents for this survey, 30 percent held public information meetings for roundabout use, 30 percent published informational brochures, nine percent prepared a video or 27 announced the arrival of the roundabout on local television, and 30 percent did not make any special efforts towards spreading public information.
Another study which evaluated existing measures to improve driver knowledge for roundabouts was conducted by Savolainen et al. [31]. Roundabout information was presented in almost three-fourths of state driver’s manuals and almost two-thirds of the
DOT websites. Based upon this information, Savolainen et al concluded that there was a gap in knowledge dissemination particularly regarding lane selection and issues involved with multi-lane roundabouts. It was found that municipalities held public information and education programs at various times; 42 percent were before construction, 21 percent were during construction, 23 percent were after construction, and 37 percent of agencies did not conduct any. The percentage of agencies in this study which did not make efforts toward roundabout education was similar to Jacquemart’s results stated above.
The study performed by Savolainen et al. also included a survey of residents on the preferred delivery method for roundabout knowledge dissemination [31]. Fifty-nine percent of respondents favored television as a method for educational information, while
43 percent favored the internet. Newspaper, mail, and radio outlets ranked similarly at 20 percent favoring these methods. E-mail and social media, such as Facebook and Twitter, were favored the least with percentages in the teens. Respondents were able to select more than one method; therefore, the sum was over 100 percent. The methods of delivery which are preferred are highly important because these methods will be most effective with dispersing education information. 28
2.2 Capacity and Saturation Levels
2.2.1 Traffic Volume
Traffic volume is defined as the number of vehicles that pass a point on a roadway over a given period of time. Volume counts are often taken in shorter intervals than desired and multiplied by a factor in order to obtain a volume over a longer period of time when time for collecting data is a constraint [9]. For example, a 15 minute interval count can be recorded and multiplied by four to determine an hourly count. The smaller time interval collected the greater the chance for error. Therefore, collecting data for one hour is more accurate than for a five or 15 minute interval and multiplying it to obtain an hourly volume count. Volumes can also be collected for a long period of time and broken down to a smaller time period’s average. For example, the Average Annual Daily Traffic
(AADT) is the total number of vehicles that travel on a roadway over a year, divided by
365. This obtains a more accurate value for an average day than if data was collected for only one day because volumes may fluctuate based on day of the week, weather, or sporting events, for example.
Volume counts can be collected using many different methods such as manual counting, pneumatic tubes, loop detectors, and video cameras [32]. Manual counts may use sheets of paper and both mechanical and electrical count boards. This method is typically used for short periods of time, such as an hour, with low traffic volumes.
Automatic counters (e.g. pneumatic tubes, loop detectors) are used for long periods of time. Loop detectors are installed into the pavement and are typically used for months or 29 years. Pneumatic tubes are placed on top of the roadway and can be used for time periods longer than 24 hours but may also be used for shorter periods of time.
Another effective way of collecting volume data is utilizing a video camera to record traffic and then extracting the data manually in a laboratory setting [9, 26, 33].
This method is often used because of the convenience of being able to review the tapes whenever time is available and the ability to re-review a tape if necessary [9]. Video recordings also allow for multiple different parameters to be extracted simultaneously.
2.2.2 Capacity
Relying solely on volume counts is not a dependable way of analyzing the effectiveness of a roadway. Volume counts only state how many vehicles are on the roadway in a given period of time, not how many vehicles can travel on the roadway. The capacity of a road is defined as the maximum flow rate at which drivers can traverse a section of roadway [34]. For a roundabout, the capacity of an entry lane is calculated by using the conflicting traffic flow, or flow that conflicts a specific movement at an unsignalized intersection, wi thin the roundabout.
There are two main methods for calculating capacity in the United States. The first method is from the Federal Highway Administration’s (FHWA) Roundabout
Guidebook. This has several different formulas for various roundabout geometric layouts.
Many of these formulas account for heavy vehicles, or vehicles with more than four wheels in contact with the pavement at once, because they can have an effect on the capacity of a roadway as they require more time to safely traverse a roadway. Other key terms that will be used in the calculation of capacity are critical headway, critical lane, 30 and follow-up time. The critical headway is the minimum headway, or time in seconds between the front bumpers of two successive vehicles as they pass a reference point along a roadway, that a driver is willing to accept [34]. The critical lane is the lane with the largest entry volume which controls factors such as the capacity of a roadway. Follow-up time is the time in seconds between the departure of one vehicle on a minor roadway to the arrival of the next vehicle.
For a geometry of one entry lane and one circulating lane at all legs of the roundabout, the following equation would be used:
−3 (−1.0∗10 )푣푐,푝푐푒 푐푒,푝푐푒 = 1,130푒 [3]
Where:
푐푒,푝푐푒 = lane capacity after adjusting for heavy vehicles, veh/h
푣푐,푝푐푒 = conflicting flow, veh/h.
The equation below is for calculating capacity for a roundabout with one entry lane and opposed by two conflicting lanes:
−3 (−0.7∗10 )푣푐,푝푐푒 푐푒,푝푐푒 = 1,130푒 [3]
Where:
푐푒,푝푐푒 = lane capacity after adjusting for heavy vehicles, veh/h
푣푐,푝푐푒 = conflicting flow, veh/h.
For a roundabout with two entry lanes and two opposing lanes capacity is calculated separately for the left and the right entering lanes. The first equation listed below is for the right lane capacity, while the second equation is for the left lane capacity:
−3 (−0.7∗10 )푣푐,푝푐푒 푐푒,푅,푝푐푒 = 1,130푒 [3] 31
−3 (−0.75∗10 )푣푐,푝푐푒 푐푒,퐿,푝푐푒 = 1,130푒 [3]
Where:
푐푒,푅,푝푐푒 = right lane capacity after adjusting for heavy vehicles, veh/h
푐푒,퐿,푝푐푒 = left lane capacity after adjusting for heavy vehicles, veh/h
푣푐,푝푐푒 = conflicting flow, veh/h.
The second method is based upon the Highway Capacity Manual (HCM). HCM has two formulas to determine capacity; one for roundabouts with one entry lane and one for roundabouts with two entry lanes. For a roundabout with only one entry lane and one circulating lane the following formula is used:
−푣푐,푥푡푐/3600 푣푐,푥푒 푐푒,푥 = −푣 푡 /3600 [34] 1−푒 푐,푥 푓
Where:
푐푒,푥 = entry capacity for entry x, veh/h
푣푐,푥 = conflicting flow in front of entry x, veh/h
푡푐 = critical headway, s
푡푓 = follow − up time, s.
In the case where there are multiple entrance lanes to a roundabout the following equation would be used:
−0.0009푣푐 푐푐푟푖푡 = 1230푒 [34]
Where:
푐푐푟푖푡 = capacity of the critical lane on the approach, veh/h
푣푐 = conflicting flow veh/h. 32
The capacities for the lanes that are not critical are assumed to be the same as the critical lane as a conservative method according to the Highway Capacity Manual [34].
Conflicting flow is the main variable that affects the capacity of a roundabout.
This is created by drivers at other approaches making left turn, right turn, or through movements. The geometry of a roundabout eliminates immediate left turn and through movements, only allowing right turn movements which will help increase the capacity of the roadway. Tan found that the capacity of a roundabout varies inversely with the volume of left-turning vehicles [35]. Therefore, when the number of turning vehicles increases, vehicles spend more time in the roundabout and cross more entering legs which leads to a lower capacity.
Another study found similar results. Information about the proper range of AADT that can function at a one-lane and two-lane roundabout was provided [7]. An AADT outside of these ranges would need additional analysis to determine if a roundabout would function properly. The data showed that AADT decreases slightly as left-turning percentages increase. For example, the maximum AADT for a single lane roundabout to function properly is around 17,000 vehicles for zero percent left-turn movements, 16,000 for 20 percent left-turn movements, and 15,000 for 40 percent left-turn movements. The capacity data for the two lane roundabout decreased in a similar fashion.
2.2.3 Saturation Level
Saturation level is the proportion of roadway demand used to existing roadway capacity [26]. The level of saturation can be calculated by dividing the volume of entering vehicles by the capacity of the lane or leg as stated in the Highway Capacity 33
Manual [34]. A saturation level of 0.85 is typically used for a roundabout [3, 36, 37]. The maximum saturation level is equal to 1.0 when the volume of entering vehicles equals the capacity of the lane or leg [26]. A lower saturation level indicates lower capacities, and likewise a higher level indicates a higher capacity.
If the roadway has a saturation level above 0.85 it is considered to be congested.
The main reason for this, 40 percent of the time, is due to bottlenecking or more simply stated as when demand exceeds capacity [10]. Traffic incidents such as rear end or side- swipe collisions cause congestion 25 percent of the time. Other reasons for congestion include inclement weather, the presence of construction work zones, poor traffic signal timing, and special events.
Congestion levels can affect driver behaviors. Headway, the measurement of time in seconds between the front bumper of a leading vehicle to the front bumper of the following vehicle, is such example [34]. One study by Mahlawat delineated congestion based upon level of service [25]. This study used the Highway Capacity Manual’s method for classifying level of service. Level of service is defined as the qualitative measurement which describes operational conditional based on measures such as speed, travel time, and maneuverability freedom [33]. Level of service A and B were considered low congestion, C and D were considered medium congestion, and E and F were considered high congestion. As congestion increased, drivers were more likely to accept an unsafe headway which may lead to an increase of crashes and delay. This study, along with others, showed that as conflicting flow increased, critical headway decreased [21,
22]. The critical headway is the minimum headway, or time in seconds between the front 34 bumpers of two successive vehicles as they pass a reference point along a roadway, that a driver is willing to accept [34].
The research conducted was not able to analyze the effects of congestion, due to the proximity of a signalized intersection. On days with heavy traffic volumes, a queue, or a line of vehicles waiting to enter a roadway, would form at the intersection of
Richland Avenue and S. Shafer Street/S. Green Drive which is located approximately one thousand feet away from the roundabout [34]. The formation of a queue would cause vehicles to be at a stand-still in the roundabout and also at the location of the pneumatic road tubes on S.R. 682 which were located before entering the roundabout. Therefore approach speeds, circulating speeds, conflicting flow, and entering volume for a time period were all low values. All parameters except for vehicle classification and familiarity would be affected by this issue, hence congestion was omitted from this study.
2.3 Pedestrian Consideration
2.3.1 Pedestrians Impacts
At traditional intersections pedestrians have to worry about drivers traveling through from the opposite side of the intersection, turning left and right from the cross street, and performing all possible maneuvers from the leg where the pedestrian is crossing. For roundabouts, on the other hand, pedestrians only conflict with vehicles which are entering and exiting the roundabout. Pedestrian crosswalks are typically located a short distance away from the intersection for roundabouts as where crosswalks are typically extremely close to the intersection. Drivers must yield for pedestrians in 35 both of these situations [3]. This action may cause delay near the crosswalk in several different ways.
The first type of delay that results from yielding for pedestrians is delay on the entry leg. In this situation, drivers need to wait for a gap between pedestrians, potentially causing a queue to form at this location. The second type of delay is for vehicles exiting the roundabout. Once again, a queue may form due to drivers waiting for a gap between pedestrians. Delay may also be caused within the roundabout. This occurs after queues form from drivers exiting the roundabout. Finally, delay may be caused at a leg upstream from the crosswalk. This scenario only happens if a queue within the roundabout forms past the upstream leg therefore blocking entry to the roundabout [38].
Queue and delay resulting from pedestrian interactions are inversely proportional to the volume of vehicles using the roundabout. When there are high traffic volumes drivers will likely need to stop, regardless of pedestrians, in order to enter the roundabout
[37]. In this case, pedestrians are likely to cross when drivers are waiting to enter the roundabout. When there are low traffic volumes, additional delay for drivers due to pedestrians is more likely to happen [3].
2.3.2 Reduction of Pedestrian Impacts
Pedestrian crosswalks are always located at the legs of a roundabout, rarely through the center of the roundabout, for safety. Besides this main safety precaution, several other methods can be utilized in order to lessen the effects of pedestrians at roundabouts. One of the most common methods is implementing a splitter island at the roundabout leg. A splitter island is a small median used between the two directions of 36 travel at the leg of a roundabout. This layout allows pedestrians to cross the intersection in two different stages [3]. Figure 3 shows an example of a splitter island [37].
Figure 3. Diagram of a typical splitter island
Another more common technique used is locating crosswalks farther away from the entrance to the roundabout. This will allow the driver to have more time to focus on the pedestrian because they will have completed navigating through the roundabout.
Also, if needed there is space for a small queue can form on the leg of the roundabout rather than on the circular roadway [3]. A less common solution is to build a separate 37 walkway solely for pedestrians and bicyclists. This can be done in the form of a tunnel underground or as a bridge over the roadway. The roundabout in Athens has a crosswalk located on a leg of the roundabout, a splitter island was installed, and there is a tunnel under the roadway that serves as a pedestrian and bicycle pathway.
2.3.3 Omission of Pedestrian Effects
The Athens roundabout has one crosswalk located on the western leg of the intersection. This leg is not only an ideal location because it is two travel lanes wide, but it is also used less frequently than the other legs of the intersection. The other three legs of the intersection are frequently used to travel to and from the university and therefore have higher traffic volumes. The crosswalk includes a splitter island ten feet in length and has an average width of about six feet in between the two directions of travel. It is located
25 feet away from the entrance of the roundabout, which only leaves room for one queued vehicle [39]. However, there is also a separate pathway that runs underneath the same roadway for pedestrians and bicyclists. The underground pathway can also be used by individuals who travel to and from the university’s campus.
The tunnel provides an option for pedestrians to travel a different path, which greatly reduces the volume crossing the roadway. As stated before, the leg with the crosswalk has the lowest vehicle volumes compared to other legs. During times where there is heavy pedestrian traffic, such as graduation, there is also heavy vehicle traffic.
Again, higher traffic volumes minimize the effect of pedestrians because the drivers would likely be stopping anyway [37]. Additionally, the crosswalk is not located on one 38 of the two legs that were researched for this study. For all of these reasons, the presence of pedestrians was disregarded for this study.
2.4 Delay
According to the Highway Capacity Manual, delay is defined as excess travel time as compared to expected travel time [34]. Roundabouts typically have less delay than traditional intersections as roundabouts do not require a complete stop when conflicting traffic is not present [3, 38, 40]. Signalized intersections utilize phasing to separate right of way. The change between phasing increases delay by adding yellow and red phases as well as the additional time from driver reaction and accelerating movements. There are many different types of delay that could occur at roundabouts.
Kareem stated that there are two main reasons for why delay occurs at a roundabout [5]. The first type of delay associated with speed reduction at the entry, traveling the circular path of the roundabout, and then accelerating back to the roadway speed limit. The second reason for delay is due to other drivers that use the roundabout which will be discussed later.
An example of the first type of delay described above is geometric delay.
Geometric delay is from features that cause drivers to slow down, navigate the intersection, and regain speed [34]. This type of delay is often determined by finding the difference between the time it takes to navigate from one point to another with and without the roundabout feature, without traffic constrictions [3]. In order to determine geometric delay, time measurements are often recorded before construction and then 39 again after construction. Additionally, many roundabouts have lower speed limits than the arterial in order to navigate the roundabout safely.
The second type of delay mentioned above was delay from other drivers, which can include traffic delay and incident delay. Traffic delay is the result of slower travel speeds due to congested roadway conditions [34]. Incident delay is delay caused by a traffic incident such as a rear-end collision. These types of delays can be found by comparing free flow travel time to congested travel time or travel time after an incident.
Control delay, however, has qualities from both these categories and is caused by the use of traffic control devices such as a yield sign or traffic signal [34]. A yield sign requires drivers entering a roundabout to yield to traffic in the roundabout. Drivers tend to slow down when seeing this sign as a precautionary measure regardless of the presence of vehicles. Circulating volume causes drivers to slow, stop, and possibly wait to enter the roundabout [3]. The HCM states that control delay is the only type needed in order to determine the overall level of service of the roundabout [34].
2.4.1 Control Delay Measurement
Control delay can be found by measuring the travel time between two points without traffic and then comparing this to the same measurement with traffic present [3].
Delay data is often difficult to collect in the field because of the lack in ability to control vehicles, as such alternative methods are often used to determine delay, including the use of simulators [10].
Control delay may also be calculated using the equations from the HCM, such as that shown below [3, 34, 38]. Note that the equation uses volume and capacity of the 40 subject lane, or lane being studied. The capacity can be found using the methods described in Section 2.2.2.
3600 푣 3600 푣 푣 2 ( ) 푑 = + 900푇 [ − 1 + √( − 1) + 푐 푐] 푐 푐 푐 450푇 [34]
where: d = control delay, sec/veh; v = flow in subject lane, veh/h; c = capacity of subject lane, veh/h; and
T = time period, h (T=1 for 1-hr analysis, T=0.25 for 15-min analysis).
From this information, the lane’s level of service can be found for the roundabout using
Table 2 [34].
Table 2. Level of service based on delay
Level of Control Delay Service (s/veh) A <10 B 10-15 C 15-25 D 25-35 E 35-50 F >50
41
2.4.2 Roundabout Effects on Delay
Roundabouts have been found to have about 12 seconds less delay than traditional intersections [40]. A study performed by Retting et al. compared the difference in delay before and after a roundabout was installed in three different locations [28]. Vehicle delay was decreased at the three intersections by an average of 18.3 percent. The rationale for this is because drivers are not required to make a complete stop if cross traffic is not present [3]. If a queue forms, traffic typically moves slowly rather than coming to a complete stop. Udding conducted a simulator study that produced results consistent with these findings [10]. On average, the delay for the roadway with roundabouts was seven seconds less than the roadway with stop controlled intersections.
Additionally, slip lanes are often used at roundabout intersections. Slip lanes, a right turn only lane that bypasses the circulating flow of a roundabout, reduce delay by decreasing the volume of vehicles passing through the roundabout [24]. Al-Ghandour et al. found that a free-flow slip lane reduced delay by 24 percent, while a yielding slip lane reduced delay by 22 percent. Figure 4 shows an example of a slip lane [3]. 42
Figure 4. Diagram of a typical slip lane
2.5 Approach and Circulating Speed
2.5.1 Speed Measurement
Speed is defined as the amount of time it takes a driver to travel a specified distance in miles per hour. Based on the definition, speed can be found manually if there is a known distance along a roadway and time measurements are recorded for that section of road. However, this is not a very accurate method due to the human error that occurs when measuring time and therefore is rarely used. The most common method used for speed measurements is radar detectors [32]. Radar detectors work by sending out signals 43 toward the vehicle and then the signals are reflected back to the detector. The travel time for the signal is then converted to a speed. This method is not practical for large vehicle volumes over long periods of time because the person collecting data is not likely to collect speeds for all vehicles when they are closely packed.
Other methods of speed measurement include inductive loops and pneumatic tubes [41]. Inductive loops are embedded in the pavement and are generally installed during construction: however multiple loops are needed in order to determine speed because the loops are placed a certain distance apart and the time it takes to travel between the two loops is converted into speed. This method is acceptable and preferred for long term studies with high vehicle counts, such as an annual volume count program by the state. Pneumatic tubes, on the other hand, are placed on top of the roadway and can be used to collect volume, speed, vehicle classification, and gap. When drivers travel over the tubes, the air is compressed and sent to the data collector. Volume can be found directly from this data by simply recording the number of times the pneumatic tubes are compressed while taking into account that vehicles have several axles. With a known distance between tubes, the data from the air pressure can be transformed into speed, vehicles class, or gap. The tubes can be set up in many different layouts for different roadway types. These are best for collecting data for time periods over 24 hours with any traffic volume level. Pneumatic tubes are ideal when collecting more than one parameter.
2.5.2 Roundabout Speeds
Almost all roundabouts in the nation require yielding at the entrance of the roundabout. The design requires that drivers slow their speeds in order to navigate the 44 roundabout safely and properly [42]. The center island forces drivers to follow the circular pattern and triangular islands at roundabout approaches are used to guide entering drivers which causes them to slow their approaching speeds [43]. Approaching drivers need to slow their speeds in order to merge safely with traffic in the roundabout.
Therefore, roundabouts affect both approach speeds and circulating speeds.
A study compared the mean speed during a peak traffic hour for a stop controlled intersection and a roundabout [10]. For the stop controlled intersection with a design speed of 30 mph, the mean vehicular speed was nine mph. The mean speed was 15 mph for the roundabout, which was the designed travel speed. This shows that during high volume periods, roundabouts allow for faster travel speeds through the junction which contributes to reduced congestion and delay.
2.6 Vehicle Classification
2.6.1 Vehicle Classification Determination
Vehicles are often classified by number of axels and length of vehicle, or space between axels. The simplest way to classify small amounts of vehicles would be manually. However, for large amounts of vehicles for long periods of time this is unrealistic. Methods that would be used for classifying high traffic volumes include inductive loops, weigh-in-motion sensors, and pneumatic tubes [41]. Inductive loops, installed in the pavement, can measure the length of a vehicle but only estimate the number of axles. Weigh-in motion sensors are an accurate system for measuring vehicle class. These sensors are embedded in pavement and collect data as vehicles travel across them. Weigh-in motion sensors are sometime paired with inductive loops. 45
2.6.2 Behavior Changes Based on Vehicle Types
A study conducted by Mahlawat and Zhang analyzed driver behavior based on vehicle type [25]. Vehicles were classified based on size; starting with motorcycles and passenger cars and ending with six and seven axle multi-trailer trucks. The study determined that larger sized vehicles are more likely to adopt safe headways. Unsafe headways may lead to crashes. Headway is the time in seconds it takes the front bumper of a trailing vehicle to reach a point where the front bumper of the lead vehicle lies [34].
This result was consistent regardless of volume and speed of vehicles. Additionally,
Taylor and Mahmassani determined from their study that drivers needed a longer gap when the vehicle closing the gap was a large vehicle, such as a bus or semi, rather than a passenger vehicle [44]. This may because drivers are more timid to enter close to a large vehicle as they have less control in decelerating their vehicle.
2.7 Gap
Headway and gap are the two main ways to measure distance between vehicles.
Gap is the time in seconds between the rear bumper of one vehicle and the front bumper of a trailing vehicle [34]. The definition of headway is stated above in Section 2.6.2. The difference between these two types of measurements is that headway includes the length of the vehicle. Therefore, a semi-truck and passenger car would have different headway if the gap was equal.
Since vehicles were not classified individually for this study, gap was the most appropriate parameter to measure. The aim for this study was to determine if vehicles entering the roundabout would accept or reject gaps. An accepted gap is the gap size in 46 seconds that a driver accepts. Rejected gap is the gap size in seconds that a driver rejects.
Driver can reject more than one gap before entering the roundabout.
2.7.1 Gap Acceptance
When drivers enter a roundabout, they are faced with a choice of whether or not to accept the gap presented to them. For double lane roundabouts, vehicles in the inside lane need to consider conflicting traffic in both circulating lanes [45]. Drivers in the outside lane are often over cautious about which lane a vehicle is traveling the roundabout in and therefore drivers often reject larger gaps than they normally would in a single lane roundabout. Other factors that affect the driver’s decision on whether or not to accept a gap include the size of the gap, speed of conflicting vehicle, type of conflicting vehicle, previous waiting time of the driver entering the gap, design of the roundabout, driver characteristics, and vehicle characteristics [15].
2.7.2 Gap Measurement
Gap and headway can both be measured using the same methods. A basic method for collection of this data is the stopwatch method [46]. This involves manually starting and stopping a stopwatch at the appropriate time, depending if gap or headway is being measured, and recording the value. This is a time consuming method typically used for small vehicle counts.
Video recording is a popular method used for larger vehicle counts. Vehicles are initially recorded on tape and then data is extracted from these recordings in the laboratory [21, 46, 47]. Data is often extracted manually, sometimes with help from small devices. For example, Vasconcelos created a keyboard that linked certain keys with 47 certain actions such as “w” meaning a vehicle arriving at the stop bar in the right lane
[46].
Video cameras can be mounted over the roadway in order to obtain an aerial view or placed on the ground to obtain a view along the roadway. Video cameras placed on the ground may be problematic because vehicles or pedestrians may obstruct the view of the study area. However, mounted cameras are costly because they are often long term which means the cameras need to be properly installed and weather proof. While both camera setups have advantages and disadvantages, sometimes only one may be appropriate for a situation. Regardless of layout, the use of a video camera is beneficial because it allows tapes to be analyzed several times to achieve accurate information [47]. Videotaping can be used to analyze several parameters which were recorded at the same time.
Cheng organized the gap data by grouping numbers into ranges, such as a range plus or minus a half of a second [21]. For example, gap values from zero seconds to 0.5 seconds were grouped as a zero second gap, gaps of 0.5 seconds to 1.5 seconds were grouped in the 1 second gap, and so on. The grouping method is often used for determination of the critical gap.
From gap data that is collected, a critical gap can be obtained. Raff’s method is one of the most popular methods used for this [23]. Other methods used for critical gap includes the Cumulative Acceptance Method, Equilibrium of Probabilities, and Fit
Maximization. These methods were not chosen due to their high difficult levels, poor results, or large sample size needed. 48
Garber and Hoel discuss the use of Raff’s method to determine critical gap [48].
Two different forms of this method can be used, a graphical method or a computational method. For the graphical method, the critical gap is the intersection of two cumulative distribution curves. The curves include the comparison of gap length with both number of accepted gaps and the number of rejected gaps. The computational Raff’s method requires the use of the following formula.
∆푡(푟−푚) 푡 = 푡 + 푐 1 (푛−푝)+(푟−푚) [48] where tc = critical gap t1 =the beginning of the time range for critical gap t2 =the end of the time range for critical gap
∆t =the time range for critical gap r = number of rejected gaps greater than t1 m = number of accepted gaps less than t1 n = number of accepted gaps less than t2 p = number of rejected gaps greater than t2.
2.7.3 Typical Gap Lengths
According to Shiftan, gaps shorter than two seconds in length are not likely to be accepted while gaps longer than five seconds are typically accepted right away [15]. The recommended range for critical gap for unsignalized intersections based on HCM 2000 is between 4.1 seconds and 4.6 seconds [34]. NCHRP Report 572 stated that the critical gap range when a roundabout is under more congested situations where queues form and 49 initial gap openings are denied is between 4.2 seconds and 5.9 seconds [13]. One study found a critical gap of 5.3 seconds for a roundabout in California [22].
Gaps that are long are not meaningful because drivers are not required to make a decision when entering the gap [15]. A long gap is typically classified as a gap that is almost always accepted by drivers. Multiple studies disregarded gap sizes of 13 seconds or longer because they were classified as a long gap [21, 23]. Including long gaps is unacceptable because it skews the critical gap values. Acceptance of larger gaps does not represent real gap acceptance decision making.
2.7.4 Gap Impacts
Critical gap has an inverse relationship to capacity, meaning as critical gap decreases, the capacity of a roundabout increases [15]. A study conducted by Mensah proved that a decrease in critical gap not only increases capacity but also improves the level of service of the roadway, reduces delay, and decreases queue lengths [47].
Gap not only affects other parameters such as capacity and delay, but these parameters may affect gap. For example, if gaps are not large enough for drivers to accept, delay will increase for the approach lanes [47]. This in turn makes the drivers less patient, and smaller gaps will be accepted. Other factors may affect gap size as well.
Xu conducted a study of ten different roundabouts that analyzed the effects of circulating volumes and speeds with headway [22]. The study determined that as either circulating volumes or circulating speed increased, critical headway decreased. This study also compared the critical headway of single lane and multilane roundabouts. It was 50 found that multilane roundabouts have a slightly smaller headway than single lane roundabouts.
Mensah questioned if familiarity of the roundabout affected the size of gaps accepted [47]. Mensah’s study analyzed the critical gap of two different roundabouts in
2009 and compared these values to the critical gap in 2005 at the same roundabouts. The researchers collected data four years later and the critical gap reduced by over one second for both of the roundabouts.
Cheng tested the effects of congestion on critical gap [21]. The researchers showed that critical gaps were slightly smaller on weekdays during rush hour than after rush hour. This study also showed that the critical gap on weekends is about one second larger than on weekdays. A different study predicted that driver behavior when entering a roundabout would affect gap sizes that were accepted [49]. The study did not determine the factuality of this assumption due to a lack of sample size. An inappropriate sample size may lead to incorrect results which will be discussed later.
2.8 Sample Size
When collecting data for this study, it was important to determine the appropriate sample size in order to assure a statistically valid analysis. If a sample size is too small, results may not be correct. This can produce two types of errors, Type I and Type II [50].
Type I error (alpha) produces a difference when none actually exists. Type II error (beta) is accepting there is no significant difference when one does exist. Type I errors are typically viewed as more important to avoid. Time, money, and resources should also be considered when determining an appropriate sample size [51]. A sample size that is too 51 large can result in unnecessary spending due to the cost of collecting data, testing data, analyzing data, and materials from testing.
Four factors that affect sample size are the level of confidence, power of the test, population error variance, and the acceptable error [52]. The probability of making a type
I error is equal to the level of significance. Power of the test is one minus the probability of making a type II error and β is generally 4α. The following formula considers all four of the factors and has been used for many other studies in order to determine the appropriate sample size [32, 53,54].
(푍 −푍 )2∗휎2 푛 = 훽 훼/2 휀2 [52]
Where:
푛 = desired sample size
푍훽 =critical value associated to β, 0.842 for a power of 80%
푍훼/2 =critical value associated to α/2, -1.96 for a 95% confidence interval
휎2 =population error variance
휀 =acceptable error.
Acceptable error can be estimated by deciding the smallest interval worth identifying [50]. Typically the level of confidence is 95 or 99 percent (alpha equal to and
0.01), depending on the importance of the outcome being correct [52]. Type I errors are said to be four times more serious than type II errors which results in a power of 0.8 from
1 - 4(0.5) for a 95 percent level of confidence. Population error variance is typically estimated initially and then a portion of the data, if not all of it, is collected to verify the sample size was appropriate. 52
CHAPTER 3: SITE DESCRIPTION
Athens, Ohio is the location of Ohio University. Ohio University was established in 1804 and currently has over 26,000 students [55]. A single roundabout, located at the intersection of Richland Avenue and State Route 682 in Athens, Ohio was the subject of the field experiment. Richland Avenue serves as a collector roadway as it provides access to local roadways and the arterial roadway, S.R. 682, which provides access to the highway system. Richland Avenue has a speed limit of 25 mph while S.R. 682 has a speed limit of 50 mph. The advisory speed, or recommended speed for safe navigation, for this roundabout is 20 mph. This intersection is a gateway to Ohio University for faculty, student commuters, and visitors. The figure below shows the city of Athens while identifying key features [56].
Label 1, shown in Figure 5, is the location of the Richland Avenue/S.R. 682 roundabout that was studied. An important piece of information in this figure is displayed by label 2. This label shows the location of Ohio University’s sporting arenas which include Chessa Field, Pruitt Field, Bob Wren Stadium, Convocation Center, and Peden
Stadium. Chessa Field is used for women’s soccer [57]. Pruitt Field is used for lacrosse, field hockey, and track teams. Bob Wren Stadium is used for baseball. Convocation
Center is used for sporting events such as volleyball and basketball and also larger events such as graduation and concerts. Peden Stadium is Ohio University’s football stadium.
These arenas are noted because they often draw large crowds to the area. Additionally, label 3 shows Ohio University’s campus including student housing and University 53 buildings and classrooms. Label 4 shows the location of U.S. 33, U.S. 50, and S.R. 32 which are used for travel to most large, surrounding cities.
2 3
1
4
Figure 5. Aerial view of Athens, Ohio
Now that a general overview of Athens has been established, a closer look needs to be taken at the roundabout. Figure 6 indicates the location of the two roadways at the roundabout intersection [56]. The Richland Avenue leg that is labeled in the figure will 54 be described as the northern leg. The S.R. 682 leg that is labeled will be described as the eastern leg of the roundabout. The other two legs are described as southern and western accordingly.
Traffic signal
Richland Ave.
State Route 682
Figure 6. Aerial view of the Athen's roundabout location
55
The northern leg of the roundabout, on Richland Avenue, is the route to Ohio
University’s campus. Both students and faculty that come from surrounding areas travel this path daily for work and class. Approximately a thousand feet north of the roundabout is a signalized intersection (Richland Avenue and Shafer Street) which provides access to
Ohio University’s sporting arenas previously described. In summary, the arenas used for baseball, soccer, lacrosse, field hockey, track, basketball, volleyball, and football are all located near this intersection. The Convocation Center is also used for local student tournaments, commencement for both Ohio University’s undergraduate and graduate students, and many other activities.
The eastern leg of the roundabout, S.R. 682, is used as travel to and from U.S. 33 and 50/S.R. 32. This roadway provides access to most large cities and small towns in the surrounding areas. With cities such as Columbus which is located to the northwest and
Parkersburg to the southeast, many drivers use the eastern leg when entering and exiting the university. The western leg of S.R. 682 provides local access to The Plains and New
Marshfield.
The southern leg, Richland Avenue, is the location of several student apartment buildings and residential subdivisions. These apartment buildings provide busing for students to and from the university’s campus. Other residents in this vicinity utilize the southern leg to travel to and from campus. However, many residents walk or bike to campus from this area. There are also several businesses along this portion of the road including fast food restaurants, banking facilities, and gas stations. 56
The roundabout is ideal for studying the effects of driver familiarity. Commuters travel to campus regularly from the southern leg of Richland Avenue. On the other hand, the roundabout is also used by unfamiliar drivers generally utilizing the eastern leg along
S.R. 682. The university hosts weekend events for students and their families, which occur approximately twice per semester, which are considered to be unfamiliar drivers. In addition, events at the Convocation Center also generate unfamiliar drivers to the area including, but not limited to, graduation ceremonies or basketball tournaments.
Figure 7 depicts the general layout of the Richland Avenue & S.R. 682 roundabout [58]. The northern leg, Richland Avenue, has four lanes approximately 12 feet in width per lane with two lanes approaching and two lanes exiting the roundabout.
A triangular island is located at this leg of the roundabout which separates northbound and southbound traffic at the approach leg and guides traffic to turn right into the roundabout. Of the two approach lanes, the left lane serves as a left turn only lane while the right lane serves as a through and right turning lane. There is one lane of cross traffic in the roundabout at its intersection with North Richland Avenue.
The southern leg of the roundabout, Richland Avenue, has one exiting and two entering lanes to the roundabout. Additionally, there is a right turn slip lane allowing right turning traffic to proceed directly onto S.R. 682 without yielding. The left approach lane is used for drivers making left turning and through movements. The center lane is used solely for through movements. An island is used to separate the continuous right turn lane from the other two lanes and directs traffic to the right in the roundabout. There 57 is one lane of cross traffic in the roundabout at its intersection with South Richland
Avenue.
The western leg of the roundabout, S.R. 682, has two approach lanes and one exiting lane. The left approach lane is used for through and left turn movements. The right lane is used for right turning movements only; however, these drivers are still required to yield to traffic in the roundabout. There are two lanes of cross traffic in the roundabout at this leg of the intersection. This leg of the intersection also has a pedestrian crosswalk with a median island separating traffic in opposing directions while serving as protective refuge. Underneath the roadway, a pedestrian tunnel also provides a pedestrian pathway for those wishing to completely avoid crossing the roadway.
The eastern leg of the roundabout, S.R. 682, has two approach lanes and one exiting lane. The left approach lane is for left, through, or right turning movements. Due to the high approaching speed, these lanes are separated by a concrete barrier followed by a median island closest to the intersection. The right entrance lane serves as a right turn only lane where drivers yield to circulating traffic. There are two conflicting lanes of traffic in the roundabout at this leg of the intersection.
58
Figure 7. Aerial view of Athen's roundabout lane configuration
Burgess and Niple included a detailed vehicle count of the Richland Avenue and
S.R. 682 intersection in a study conducted in 1999 which projected volume counts for
2010 [59]. Both the actual volume counts and the projected volume counts are provided in Tables 3 and 4, respectively. Morning and afternoon peaks are presented to show the main leg of travel to and from the university [59].
59
Table 3. Existing traffic volume in 1999
Turning Northern Southern Eastern Western Movement Leg Leg Leg Leg
Left 72 92 96 109 Through 171 556 226 130 Right 28 82 628 98 AM Peak Total 271 730 950 337
Left 389 126 92 99 Through 485 281 115 209 Right 64 174 222 160 PM Peak Total 938 581 429 468
Table 4. Projected traffic volume for 2010
Turning Northern Southern Eastern Western Movement Leg Leg Leg Leg
Left 109 141 153 154 Through 302 953 316 183 Right 42 137 893 152 AM Peak Total 453 1231 1362 489
Left 562 194 149 143 Through 833 485 165 295 Right 95 274 3320 245 PM Peak Total 1490 953 634 683
During morning peak hours from 7:00 AM to 9:00 AM, the eastern leg, S.R. 682, has the highest vehicular volumes while during afternoon peak hours from 4:00 PM to
6:00 PM, the northern leg, Richland Avenue, has the highest volumes. The southern leg,
Richland Avenue, is fairly highly traveled by commuters on a daily basis. Only the main legs to and from the campus were utilized for this study due to the presence of unfamiliar 60 and familiar drivers. The eastern leg was selected for observation because both familiar drivers and unfamiliar drivers would be using this leg of the roundabout. The northern leg was also studied. As stated earlier, these legs were chosen because they are the main legs used, by both familiar and unfamiliar drivers, to enter and exit Ohio University’s campus.
These two legs will be referred to simply as S.R. 682 and Richland Avenue during this study.
61
CHAPTER 4: METHODOLOGY
The main objective of this study was to compare familiar and unfamiliar driver behavior in a roundabout. In order to complete this objective, the determination of driver familiarity was completed. A field study was conducted in order to collect driver behaviors using efficient and logical methods. Then finally, the outcome for this study was to determine if there was a statistically significant difference between familiar and unfamiliar driver behaviors.
The roundabout in Athens, Ohio was selected as the location for this study. A high percentage of the traffic volumes for the roundabout are generated by the university.
Faculty, commuters, and drivers from out of town use two legs to enter and exit Ohio
University’s campus. These two legs of the roundabout, the northern leg (Richland
Avenue) and the eastern leg (State Route 682), were the focus for this study. Burgess and
Niple’s study proved that the eastern leg of State Route 682 was heavily traveled in the morning when drivers are entering campus and the northern leg or Richland Avenue was heavily traveled in the evening when drivers are leaving campus [59].
For this study drivers were classified as familiar or unfamiliar. Familiarity was quantified by periods of times, not by individual vehicles. This was done because obtaining all the parameters for individual vehicles would have been unrealistic. If the circulating speed of a vehicle was obtained it would be extremely difficult to match it with the approach speed of the vehicle that was obtained. Additionally, the data collection method used for approaching speeds, which will be discussed later, does not 62 distinguish the lane choice for Richland Avenue. For these reasons, familiarity was defined for time periods rather than individual vehicles.
Due to the roundabout being located just outside of Ohio University’s campus, time periods could easily be classified as familiar or unfamiliar based on university activities. For example, Ohio University’s graduation attracts family and friends, many of which travel through the roundabout. These drivers were considered as unfamiliar drivers. County numbers, located on license plates of vehicles, were collected as a mean for verifying familiarity assumptions. County five was Athens County while all other counties were grouped together as not county five vehicles.
Data collected during a typical work day, Monday through Friday not including holidays, was classified as familiar drivers as no large university events were occurring and regular commuters such as faculty or students were utilizing the roundabout.
Unfamiliar drivers were classified as drivers traveling during large university event days.
These events include Mom’s Weekend, Dad’s Weekend, Sib’s Weekend, Homecoming
Weekend, Graduation, and a high school basketball tournament.
Data was collected at the key times where drivers were expected to enter and exit the campus area. For example, before graduation most drivers were expected to travel through the roundabout within two hours of the event’s start time. For Mom’s, Dad’s, and
Sib’s weekends, a two hour window was chosen on both Friday and Sunday when family members were expected to enter and leave the university. Homecoming weekend was studied similarly with additional data collection times on Saturday based on a parade and football game. Additionally, a high school basketball tournament was studied where 63 several games were played continuously. Therefore, with the limited camera availability, data was collected when drivers were arriving and leaving the games in one hour intervals, accordingly. Driver behaviors were measured during these time periods by several different methods described in the following section.
4.1 Data Collection and Equipment
4.1.1 Manual Methods
County numbers were collected manually in order to prove driver familiarity associated with different time periods. Vehicles were either classified as local/familiar
(Athens County) or nonlocal/unfamiliar. However, due to the residential nature of Ohio
University there is a chance that county numbers may incorrectly represent driver familiarity of the roundabout. Many student commuters are familiar with the roundabout; however, their county numbers represent their home town. Therefore, familiar days were expected to not have as high of a percent of county five vehicles as one may have initially expected. Unfamiliar days were expected for have a high percentage of non-county five vehicles.
4.1.2 Pneumatic Tubes
Pneumatic tubes were used to obtain approaching speeds. The pneumatic tubes were placed upstream from the roundabout on the Richland Avenue and the State Route
682 legs. The locations for the placement of the tubes were based on several factors.
First, the tubes could not be placed too closely to the roundabout in order to minimize the chance for queue, or a line of vehicles, to form on top of the tubes resulting from drivers yielding to enter the roundabout. This placement was also used as it was desired to collect 64 the speeds of vehicles before drivers slowed down to maneuver through the roundabout were desired. Additionally, the tubes are required to be placed on a straight portion of roadway so vehicles will not cross the tubes at an angle because inaccurate data would be the result of tubes at an angle. The pneumatic tubes also needed to be placed at a location where they could safely be locked to a fixed object to prevent theft of the data collector.
These four factors were used when determining the placement for the tubes. For Richland
Avenue, the tubes were placed across one lane 742 feet north of the roundabout. For State
Route 682 the tubes were placed 1246 feet east of the roundabout across two lanes of roadway traveling the same direction. The locations of the tubes can be seen in Figure 8
[58].
65
Figure 8. Pneumatic tube locations
The layouts and types of tubes utilized were determined based on the collected parameters, speed limits, and the lane layouts. The type of tubes used depends heavily on the speed and layout of the roadway. The EZ belt, a belt with two tubes at a premeasured distance apart, is generally used for low travel speeds and can only be used for one lane of travel in the same direction or two lanes of travel in different directions.
D-tubes, a type of standard road tube, can be used for both high and low travel speeds and can be used for two lanes, one in each direction of travel, or up to four lanes with the same direction of travel. The EZ belt has an easier installation process and leaves little room for experimental setup error, but has limitations on roadway layout. For these 66 reasons D-tubes were used on State Route 682 and the EZ belt was used for Richland
Avenue.
The layout type depends on the lane layout and what parameters were collected.
The desired parameters were speed, volume, and vehicle classification. Again, Richland
Avenue had one approaching lane and State Route 682 had two approaching lanes in the location where the tubes were placed. Based on the layouts from TRAX Flex HS User’s
Manual, layouts five and eleven were chosen for this study [60]. Layout five was used with the EZ belt at Richland Avenue and layout eleven was used with the D-tubes at State
Route 682. The EZ belt at Richland Avenue was placed across one lane of traffic. Two tubes were set at a premeasured distance apart, as done with all EZ belts. S.R. 682 had D- tubes running across two lanes of traffic, both in the same direction. The use of four tubes was needed for this location. Two tubes spanned across only one lane of traffic while the other two tubes spanned across both lanes of traffic. These tubes were manually measured and placed at a certain distance apart. These layouts can be seen in Figures 9 and 10, respectively, along with the distance of tubing needed [60].
67
Figure 9. EZ belt road tube layout 5 for Richland Avenue
Figure 10. D-tube road tube layout 11 for S.R. 682
Data extraction from the pneumatic tubes was fairly simple. The data collection boxes were hooked up to a computer and the TRAXPro software was run. This program could extract each parameter separately and for different time intervals including one, five, fifteen, thirty, and sixty minute intervals. The program also allowed the range of speed data to be selected with different mile per hour accuracies. For example, for a 68 range of 15 to 70 miles per hour the accuracy is a range of five mph. If the range was set to 1 to 40 the accuracy would be a three mph range.
4.1.3Radar Detector
Even though pneumatic tubes are convenient, they are not ideal for all locations.
An example of this is in the circulatory part of a roundabout. Placement of pneumatic tubes at this location would too dangerous. Therefore, circulating speeds were collected using a radar detector. A Laser Technology Inc. UltraLyte radar detector was used for this study. The data collector would stand at a location not easily seen by drivers and also aligned with the vehicles direction of travel as much as possible. If the data collector is standing at a large angle from the vehicles direction of travel it would be more difficult to obtain readings as well as incorrect results would be produced. The radar detector was then aimed towards the license plate of a vehicle and the time it takes a signal to reach the license plate and reflect back to the radar detector was converted to a speed. This speed was shown on a small screen located on the radar detector and manually transferred to a piece of paper. The radar detector used had an accuracy of one mph.
Circulating speeds were measured at two different locations in the roundabout and for both lanes of traffic. Each location was near one of the two legs that were studied, and was the portion of the roundabout with two lanes. With this information, inside lane and outside lane driver behaviors could be compared. These locations are shown in Figure 11
[58].
69
Figure 11. Location for collection of circulating speeds
4.1.4 Video Camera
The video camera was used to obtain rejected gap, accepted gap, and entrance behavior data. Gap is the time in seconds between the rear bumper of one vehicle and the front bumper of a trailing vehicle [34]. Accepted gap is the size gap that a driver accepts.
Similarly, rejected gap is the size gap that a driver rejects. A driver may reject more than one gap. Entrance behavior was measured by the action the entering driver performed (no stop, partial stop, or complete stop) depending on if there was or was not a presence of 70 cross traffic, or traffic circulating through the roundabout where the approaching vehicle enters the roundabout.
For both legs that were studied, the video camera was placed so that both entering traffic and cross traffic could be seen. The video camera was set up facing State Route
682 when vehicles were expected to enter the university’s campus and towards Richland
Avenue when vehicles were expected to leave. The videos were recorded generally about one hour in length each. The use of a video camera allowed multiple parameters to be recorded at once. It also allowed for more accurate measurements because of slow motion and rewind controls. The accuracy of the camera used for this study was one mph.
Extraction of data from the video tapes had to be completed. Due to the manual extraction methods used for this study, human error may be involved in the data extraction process. To avoid judgment errors, multiple people reviewed the video tapes.
In many cases, two people reviewed the same video tape at different times and results were compared. This overlapping check method help reduce error due to human judgment.
In order to accurately measure accepted and rejected gaps, the gap between circulating vehicles would not be sufficient. For accepted gaps, the gap sizes were taken as the time entering vehicles passed the stop bar until the time the circulating vehicle arrived at that leg of the roundabout. Rejected gaps were measured as the time entering vehicles reached the stop bar to the time the circulating vehicles reached the leg of the roundabout. If two gaps were rejected, the first gap was rejected as stated above and the second gap was the actual gap size between the two circulating vehicles. For State Route 71
682, drivers entering the inner lane of the roundabout had to consider the gaps coming from the combination of both circulating lanes. Accepted and rejected gaps were obtained for both entering lanes of both legs of the roundabout that were studied. This process also obtained circulating volumes which were later used for delay calculations.
Accepted and rejected gaps were then used to determine critical gap sizes. Sample sizes for both accepted and rejected gaps are described in Section 4.2. Once enough data was collected to meet the required sample sizes for these two parameters, a critical gap was determined for this amount of data. A short description of the method used to calculate critical gap was seen in the Background section of this paper. Raff’s method was used for this study, both graphically and computationally. As stated earlier, the critical gap is the intersection of two cumulative distribution curves. The curves include the comparison of gap length with both number of accepted gaps and the number of rejected gaps. The computational Raff’s method requires the use of the following formula.
∆푡(푟−푚) 푡 = 푡 + 푐 1 (푛−푝)+(푟−푚) [48] where tc = critical gap t1 =the beginning of the time range for critical gap t2 =the end of the time range for critical gap
∆t =the time range for critical gap r = number of rejected gaps greater than t1 m = number of accepted gaps less than t1 72 n = number of accepted gaps less than t2 p = number of rejected gaps greater than t2.
Additionally, driver entrance behaviors were extracted from the video tapes.
Driver maneuvers were divided into six different categories based on their behaviors and the presence of cross traffic. The categories consisted of entering drivers making a complete stop, partial stop (noticeably slow down but not to a complete stop), or no stop, for both a presence and no presence of cross traffic. From this information, the percent of drivers who made inappropriate maneuvers could be obtained. Inappropriate behaviors include making a complete stop with no traffic present and not stopping when traffic is present. Stopping without traffic can lead to an unnecessary increase in delay. Both of these behaviors, however, can lead to crashes such as rear-end crashes when a driver does not expect the vehicle in front to stop or a sideswipe crash or rear-end crash with the vehicle entering the roundabout and the vehicle circulating the roundabout. Entering traffic volumes by lane were also obtained from this process. The use of pneumatic tubes produced this same result for State Route 682 but not for Richland Avenue because the tubes were placed away from the roundabout where only one southbound lane existed.
The final parameter not discussed yet is delay. Initially, delay was going to be measured using a video camera. This would take reviewing the tapes several times in order to collect accurate data. It was determined that this method would not work. First, no camera angle could collect the full length of queuing due to the curvature in the approaching lanes. Also, other vehicles would often block the view from the camera and 73 data would be difficult to measure accurately. Instead, delay was calculated based on the following equation from the HCM:
3600 푣 3600 푣 푣 2 ( ) 푑 = + 900푇 [ − 1 + √( − 1) + 푐 푐] 푐 푐 푐 450푇 [34]
Where: d= control delay, sec/veh c=capacity in subject lane, veh/h
T=time period, h v=flow in subject lane, veh/h.
Flow in the subject lane was already recorded from the video tapes for collection of entrance behaviors. Time period was the length of the video tape in hours. The final variable needed to calculate control delay is capacity in the subject lane. For multilane roundabout entries, the following equation is used by the HCM for capacity of the critical lane:
(−0.0009푣푐) 퐶푐푟푖푡 = 1230푒 [34]
Where:
퐶푐푟푖푡 =capacity of the critical lane on the approach, veh/h
푣푐 =conflicting flow, veh/h.
For this equation, the conflicting flow was already extracted from the video tapes when critical gap data was collected. The critical lane capacity is then assumed for the non-critical lane. 74
4.2 Sample Size Determination
An appropriate sample size was needed to be determined in order to produce a statistically significant result at a level of confidence of 95%, or alpha equal to 0.05, and a power of 80%, or beta equal to 0.2. First, each parameter was collected for a short period of time, typically a few hours. This was done because there was a plan to determine an appropriate sample size from these few hours in order to have a general idea of the sample size needed. After, more samples were expected to be collected and a verification that the appropriate number of samples was to be conducted. From this data, a sample error variance was calculated for each parameter. Using the equation below a desired sample size was calculated.
(푍 −푍 )2∗휎2 푛 = 훽 훼/2 휀2 [52]
Where:
푛 = desired sample size
푍훽 =critical value associated to β, 0.842 for a power of 80%
푍훼/2 =critical value associated to α/2, -1.96 for a 95% confidence interval
휎2 =sample error variance
휀 = acceptable error.
Logical decisions were used to initially estimate the acceptable error. For example, the acceptable error for delay was largely based on HCM’s table for level of service based on delay (Table 2). In this table, the smallest range for any level of service was five seconds per vehicle. Half of this value, rounded down to a whole number to be conservative, was used to obtain a two second acceptable error for delay. 75
Parameters that would commonly vary without being able to notice have larger acceptable errors. These parameters included approaching speed, circulating speed, and entrance behavior. These acceptable errors were determined to be three, two, and five, respectively. Of these parameters, circulating speed had the smallest acceptable error because larger than two miles per hour was determined to be significant difference, similar to approaching speeds with an acceptable error of three miles per hour.
Circulating speed had a smaller acceptable error than approaching speed because of the circular traffic pattern which led to a smaller deviation in speeds. Entrance behaviors were less critical, with an acceptable error of five percent, because less than a five percent difference was viewed as an insignificant difference. Finally, for the gap parameters, an acceptable error of one second was used because it was the smallest measure obtainable from the camera that the data was extracted from.
Accepted and rejected gap sample sizes were calculated along with critical gap.
The reason for this is because when the critical gap parameter is compared there is a small sample size. Normality of the distributed accepted and rejected gaps will be checked for a statistical analysis by visual inspection with the use of a histogram, plotted bell curve, and the known mean value.
A sample size was needed for both familiar and unfamiliar drivers, for each leg, and in some cases for each lane. Data was not analyzed by lanes for two reasons. First, approach speed was not separated by lane for Richland Avenue. As stated earlier, the location where the pneumatic tubes were placed had only one lane in the direction of travel towards the roundabout. The second reason that was not broken down by lane was 76 driver entrance behaviors. For this parameter, lane location did not impact the parameter.
A sample size was calculated separately for each parameter. Initial sample size calculations for the quantitative parameters can be seen in Tables 5 and 6.
77
Table 5. Familiar driver initial sample size calculations (Zβ= 0.842, Zα/2=-1.96)
Required Parameter Leg Lane σ ε n Richland N/A 4.18 16 Approach Speed Ave. 3 (mph) Inside 8.94 70 S.R. 682 Outside 6.51 37 Richland Inside 2.00 8 Circulating Speed Ave. Outside 2.13 9 2 (mph) Inside 3.12 20 S.R. 682 Outside 3.07 19 Richland Inside 3.09 75
Ave. Outside 2.79 62 Accepted Gap (s) 1 Inside 2.67 56 S.R. 682 Outside 2.77 61 Richland Inside 1.33 14 Ave. Outside 1.24 13 Rejected Gap (s) 1 Inside 1.35 15 S.R. 682 Outside 3.48 96
INITIAL FAMILIAR Richland Inside 0.13 1 Ave. Outside 0.29 1 Critical Gap (s) 1 Inside 0.1 1 S.R. 682 Outside 0.14 1 Richland Inside 0.19 1 Ave. Outside 0.15 1 Delay (sec/veh) 2 Inside 0.19 1 S.R. 682 Outside 0.76 2 Richland N/A 1.38 1 Entrance Ave. 5 Behaviors (%) S.R. 682 N/A 2.40 2
78
Table 6. Unfamiliar driver initial sample size calculations (Zβ= 0.842, Zα/2=-1.96)
Required Parameter Leg Lane σ ε n Richland N/A 4.55 19 Approach Speed Ave. 3 (mph) Inside 12.78 143 S.R. 682 Outside 15.71 216 Richland Inside 2.84 16 Circulating Speed Ave. Outside 2.41 12 2 (mph) Inside 2.53 13 S.R. 682 Outside 2.81 16 Richland Inside 3.07 74 Ave. Outside 2.87 65 Accepted Gap (s) 1 Inside 2.90 67 S.R. 682 Outside 3.02 72 Richland Inside 1.91 29 Ave. Outside 1.01 9 Rejected Gap (s) 1 Inside 1.19 12 S.R. 682 Outside 1.24 13 Richland Inside 0.49 2 INITIAL UNFAMILIAR Ave. Outside 0.36 2 Critical Gap (s) 1 Inside 0.06 1 S.R. 682 Outside 0.11 1 Richland Inside 0.66 1 Ave. Outside 0.23 1 Delay (sec/veh) 2 Inside 0.023 1 S.R. 682 Outside 0.76 2 Richland N/A 1.21 1 Entrance Ave. 5 Behaviors (%) S.R. 682 N/A 6.44 14
79
CHAPTER 5: STATISTICAL METHODOLOGY
The parameters that were collected need to be statistically tested in order to determine if there is a significant difference between the groups of familiar and unfamiliar drivers. The data collected was subdivided into eight groups:
1. Familiar drivers, S.R. 682 leg, and inside lane
2. Familiar drivers, S.R. 682 leg, and outside lane
3. Familiar drivers, Richland Ave. leg, and inside lane
4. Familiar drivers, Richland Ave. leg, and outside lane
5. Unfamiliar drivers, S.R. 682 leg, and inside lane
6. Unfamiliar drivers, S.R. 682 leg, and outside lane
7. Unfamiliar drivers, Richland Ave. leg, and inside lane
8. Unfamiliar drivers, Richland Ave. leg, and outside lane
Groups one through four were control groups which unfamiliar driver data was compared against. The parameters that were included in each group were delay, critical gap, and circulating speed because analysis between inside and outside lane behavior was desired for these parameters. For approaching speeds, data was only collected for one lane on Richland Avenue. As stated earlier, this is due to the layout of the roadway where the pneumatic tubes were placed. Therefore, approaching speeds were only divided into six groups, the same as above with the exception of dividing the Richland Avenue data into inside and outside lanes. Finally, lane was disregarded for the final parameter, driver entrance behaviors. Inappropriate behaviors were not desired based on lane, just overall percentages for each leg. This parameter was separated by leg in order to determine if 80 driver behaviors leaving or entering the University’s campus varied. The entrance behaviors parameter was analyzed in the groups described below:
1. Familiar drivers, S.R. 682 leg
2. Familiar drivers, Richland Ave. leg
3. Unfamiliar drivers, S.R. 682 leg
4. Unfamiliar drivers, Richland Ave. leg
Statistical tests were used in order to determine if there is a significant difference in the driver behaviors collected based on driver roundabout familiarity. The t-test is a popular statistical test typically conducted to compare two variables [52]. If several variables need to be compared, one may initially try to do multiple t tests. However, this method would create an unacceptable probability for Type I errors [52]. This is the error of producing a difference when none actually exists. It was determined that for this study the level of confidence was 95% or alpha equal to 0.05. The chance for error would increase based on the following equation:
푇푦푝푒 퐼 푒푟푟표푟 푟푎푡푒 =1−(1−훼)푐 [52]
Where:
α = level of significance for each separate t test c = number of independent t − tests.
For example, the number of independent t-tests needed for approaching speeds would be 15. This would produce a Type I error rate of 0.54 which is considered unacceptable. 81
To compare more than two variables without increasing the Type I error rate, the one-way analysis of variance (ANOVA) test can be conducted. In order to perform this test, three main assumptions must be met. These assumptions are that the observations are random and independent, the samples are normally distributed, and the variances of the distributions in the population are equal [52].
Samples collected were random and independent. Pneumatic tubes and the video camera recorded every vehicle that passed through the roundabout during the selected times were studied regardless of age, gender, etc. Data was collected at random for both manual data collection and radar detectors. When data was collected, impacts on driver performance were minimal. Due to the circular layout of the roundabout, drivers were unlikely to see the data collection equipment or the field worker until the driver passed the location of study. Additionally, familiar drivers and unfamiliar drivers were independent since they were not drawn from the same population.
5.1 Tests of Normality and Homogeneous Variance
Tests for both normality of the data and equal population variances were conducted in order to check if the assumptions for the ANOVA test were met. Levene’s test was used to evaluate the homogeneity of variances. The null hypothesis for this test is that the difference between variances of groups is zero [61]. Therefore, a result of less than 0.05 means that the null hypothesis is false and there is a significant difference in variances. If Levene’s test produces a result of greater than 0.05 then the assumption of homogeneous variances is met. Small differences in variances can produce a significant result when sample sizes are large. Large sample sizes are often seen as values over 100. 82
Many of the parameters collected for this study had large sample sizes such as approach speeds.
The final assumption made using the ANOVA test was that the data set was normal. Two analytical tests may be used to determine the normality of the data; the
Kolmogorov-Smirnov and Shapiro-Wilk tests. These tests compare the data collected to a normally distributed set of data with the same mean and standard deviation [61]. If the test is significant than the data set varies from a normally distributed data set. Similarly to the limitation with Levene’s test, significant results can be easily produced with a small deviation in normality with a large sample size.
Values for kurtosis and skewness could also be observed as a normality check.
Skewness is the lack of symmetry and Kurtosis is pointiness of the graphed data. For both of these items, zero values represent normally distributed data [61]. Z-scores can be calculated for both of these items and compared to a standard in order to determine if the data is normally distributed. A z-score with an absolute value of 1.96 or larger is considered significant with a level of confidence of 95%. The equations for z-scores can be seen below:
푆−0 푧푠푘푒푤푛푒푠푠 = [61] 푆퐸푠푘푒푤푛푒푠푠
Where:
푧푠푘푒푤푛푒푠푠 = 푧 − 푠푐표푟푒 푓표푟 푠푘푒푤푛푒푠푠
푆 = 푠푘푒푤푛푒푠푠
푆퐸푠푘푒푤푛푒푠푠 = 푠푡푎푛푑푎푟푑 푒푟푟표푟 표푓 푠푘푒푤푛푒푠푠
퐾−0 푧푘푢푟푡표푠푖푠 = [61] 푆퐸푘푢푟푡표푠푖푠 83
Where:
푧푘푢푟푡표푠푖푠 = 푧 − 푠푐표푟푒 푓표푟 푘푢푟푡표푠푖푠
퐾 = 푘푢푟푡표푠푖푠
푆퐸푘푢푟푡표푠푖푠 = 푠푡푎푛푑푎푟푑 푒푟푟표푟 표푓 푘푢푟푡표푠푖푠.
These tests can also produce incorrect results if sample sizes are very large or small. Several parameters in this study have a very large and very small sample sizes.
The testing methods mentioned so far may not be ideal for these situations. Rather, normality can be checked by plotting a histogram and visually inspection. A bell curve could be fit to the data and the peak value could be compared to the mean of the data points.
If these assumptions are not met, the Type I error rate increases [52]. However, when data is not normal there is a minimal effect of the Type I error rate. If the variance in the population differ and sample sizes are equal, the effect on the Type I error is minimal. However, if the sample sizes are not equal, the effects may cause serious problems. In summary, ANOVA is lenient with not meeting the assumptions besides unequal variances with unequal sample sizes.
5.2 Solutions to Problems with Assumptions
There are several other tests that may be used as a method in order to obtain robust results while still using the ANOVA test if assumptions are not met. If the assumption of variance homogeneity is violated, ANOVA may still be used. Welch’s F- ratio allows for the one-way ANOVA to produce robust results even when this assumption is not met [61]. Therefore, Welch’s F-ratio was incorporated into the 84 analysis of this study. Welch’s F-ratio was then compared to the critical F-ratio calculated during the one-way ANOVA test. If the calculated F-ratio was larger than the critical F- ratio the null hypothesis is rejected meaning all groups do not have equal means.
To improve normality, outliers can be removed from a data set. Z-scores can be used to determine if an outlier exists [61]. The following equation can be used in order to determine the z-scores of a data set.
푋−푋̅ 푧 = 푠 [61]
Where:
X = the mean of all scores
X̅ = each individual score s = the standard deviation of all scores.
A z-score of 1.96 (absolute value) reduces the top and bottom of the distribution by a total of five percent [61]. Of all the z-scores for a data set, 99 percent of them lie between positive and negative 2.58. Similarly, 99.9 percent of z-scores fall between positive and negative 3.29. Therefore, a data point with the absolute value of a z-score of
3.29 and above would represent an extreme outlier.
Transforming data is another way to improve normality. This should also reduce the impact of outliers. There are three ways to transform data; log transformation, square root transformation, and reciprocal transformation [61]. Log transformation is useful for data with a positive skew. However, the log cannot be taken for zero or negative values.
The square root transformation is helpful for positive skew also. Taking the square root of a large value has a greater effect than on a small value. Also, square roots cannot be 85 taken for negative values. Finally, the reciprocal transformation would make large values very small and small values large. Reversing the values can be used to correct this. This is when each individual value can be replaced with the highest value minus each value.
Then, the reciprocal test can be performed. All of these transformations can use used to improve negative skew by reversing the scores.
5.3 One-way ANOVA
One-way ANOVA tests whether or not different group’s means vary [61]. The assumption is that the group’s means are equal. The alternative is that at least one mean varies from another group’s mean. One-way ANOVA uses the F-ratio to test the fit of a linear model. The F-ratio is found by the ratio of the model mean squares, MSM, and the residual mean squares, MSR [61]. The means squares can be found by dividing the sum of squares by the degrees of freedom. If the data had homogeneous variances, the ANOVA
F-ratio would be used. The following equations are used to calculate the F-ratio:
2 푆푆푇 = ∑(푥푖 − 푥̅푔푟푎푛푑) [61]
Where:
SST = the total sum of squares xi = each observed data point x̅grand = the grand mean
2 푆푆푀 = ∑ 푛푘(푥̅푘 − 푥̅푔푟푎푛푑) [61]
Where:
SSM = the model sum of squares nk = number of participants within group k 86 x̅k = the mean of group k x̅grand = the grand mean
푑푓푀 = 푘 − 1 [61]
Where: dfM = model degrees of freedom k = number of groups
2 푆푆푅 = ∑(푥푖푘 − 푥̅푘) [61]
Where:
SSM = the residual sum of squares xik = each observed data point within group k x̅k = the mean of group k
푑푓푅 = 푁 − 푘 [61]
Where:
푑푓푅 = residual degrees of freedom
N = total sample size k = number of groups
푆푆푀 푀푆푀 = [61] 푑푓푀
Where:
MSM = model mean square
푆푆푅 푀푆푅 = [61] 푑푓푅
Where:
MSR = residual mean square 87
푀푆 퐹 = 푀 [61] 푀푆푅
Where:
F = F ratio.
5.4 Post Hoc Tests
If the data was found to be statistically significant in the ANOVA, post hoc tests were used to compare all possible combinations of experimental data, similar to performing a t-test for each pair of groups [61]. As stated earlier, multiple t-tests can cause the Type I error rate to increase. If each of eight groups were compared to one another, the number of tests to be conducted would be 28. However, the Bonferroni
Method was utilized to correct the level of significance for each test and the Type I error rate does not exceed 0.05. The Bonferroni Method divides the Type I error, alpha, by the number of tests conducted [61]. Therefore, if 28 tests were conducted with a desired alpha of 0.05, each individual hypothesis is tested at a significance level of 0.05/28.
These tests are conducted without any specific prediction to the results that may arise.
When observing that collected data, it was obvious that sample sizes varied throughout the project. Hocheberg’s GT2, Gabriel’s, and Games-Howell tests are used when sample sizes differed [61]. It was not recommended to use Hocheberg’s GT2 test if the data sets had unequal variances. In this case, Gabriel’s test should be conducted; however, Gabriel’s should not be utilized if sample sizes are very different. The Games-
Howell test is accurate with unequal sample sizes and population variances but errors may occur when sample sizes are too small. 88
For this study, Gabriel’s test was not used due to the large variation in sample sizes. This variation was largely due to the sample size being uncontrolled for some parameters. Sample sizes also varied because many different unfamiliar days were studied in order to obtain a large variety of events, while a comparably smaller amount of familiar days were studied because ideally these days should be rather consistent. For this reason, Hocheberg’s GT2 test was performed when groups had equal variances. When variances were unequal, the Games-Howell test was executed.
5.5 Nonparametric ANOVA
If the data was not normally distributed or skewness was a major problem, the nonparametric ANOVA was conducted. The nonparametric ANOVA test, a Kruskal-
Wallis test, can be used in order to compare difference between several independent groups. This test is commonly used when data is non-normal or other assumptions are not met [61]. The null hypothesis for this test is that the means of each group are equal. If an insignificant result is produced, the null can be accepted and the means can be considered equal. If a significant result is produced, several Mann-Whitney tests need to be conducted in order to compare two groups at a time.
Again, the null hypothesis for the Mann-Whitney test is that the means of the two groups tested are equal. An insignificant result means that the null is accepted and the means of the groups are equal. A significant result means the null is rejected and the means of the groups are unequal.
Details about which parameters used which testing methods will be described in the Results section. The reason behind using the selected methods will be discussed along 89 with solutions to assumptions not being met. If any data needed altering, such as outliers being removed, this will also be discussed in the following section.
5.6 Chi-Squared Test
The chi-squared test compares the observed and expected frequencies of data in each category to test that all categories contain the same proportion of vales [61]. This test can also check that each category contains a specified proportion of values. A chi- squared statistic can be calculated using the following equation:
(푂−퐸)2 휒2 = ∑ 퐸 [61]
Where:
O = observed frequency
E = expected frequency.
This value is then compared to a critical value that is based on the degrees of freedom of the data and the level of confidence selected. Degrees of freedom is one less than the number of observations. The level of confidence used for this study was 95 percent, or alpha equal to 0.05.
5.7 Effect Size
According to Hinkle, effect size is the degree to which a phenomenon exists [61].
When sample sizes are large, a small difference of means can produce a significant result.
Similarly, if sample sizes are small, a large difference may produce insignificant results.
For this reason, effect size should be used as another method to measure importance by expressing the difference in measurement in standard deviation units. Since effect size is 90 a standardized measurement it can be compared across different studies and variables.
The effect size was calculated based on the following equations.
One-way ANOVA:
푆푆 푟 = √ 푀 [61] 푆푆푇
Where: r = effect size
SSM = model sum of squares
SST = total sum of squares.
Mann-Whitney tests:
푍 푟 = [61] √푁
Where: r = effect size
Z = z − score calculated from analysis
N = total sample size used in the analysis.
After effect size is calculated, it can be compared to the following standards [61].
These show the practical effect size that the variable has on the outcome. In other words, how familiarity of drivers affects each parameter. Pearson’s r can be compared to the following standards.
r = 0.10 – 0.29 Small effect
r = 0.30 – 0.49 Medium effect
r = 0.50 and above Large effect 91
Another method for calculating effect size is Cohen’s method [61]. Cohen’s method is used to compare two means as done with the student’s t-test. This method may also be helpful for effect size calculations for post-hoc tests after conducted a one-way
ANOVA. Cohen’s d can be calculated with the following equation.
푥̅1−푥̅2 푑 = [61] 푠
Where:
푑 = cohen’s d
푥̅1 = mean on group 1
푥̅1 = mean of group 2
푠 = pooled standard deviation.
Cohen’s d value can be evaluated by the following standards [61].
d = 0.20 – 0.49 Small effect
d = 0.50 – 0.79 Medium effect
d = 0.80 and above Large effect
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CHAPTER 6: RESULTS
The main purpose of this research was to compare the performance of familiar drivers against unfamiliar drivers in a modern roundabout and determine if there was a statistical difference with any of the parameters collected. Again, these parameters included approaching speeds, circulating speeds, critical gap, driver entrance behaviors, and delay. The location for this case study was the roundabout at the intersection of
Richland Avenue and State Route 682 in Athens, Ohio. Data from the main leg entering the city and leaving the city was collected and analyzed. This multi-lane modern roundabout has many unique features previously mentioned. Variation in weather was not considered for this study due to the consistency in weather conditions for the university events in which data was collected. This chapter will discuss the results that were found during this study.
6.1 Verifications
6.1.1 Familiarity
Classification of familiarity was based on significant university activities. It was assumed that large university events would attract unfamiliar drivers to the campus and when no event was happening drivers would be familiar with the roundabout. A check was performed in order to verify that these assumptions were correct. County numbers were collected manually into two categories, Athens County and non-Athens County. As stated earlier, it was predicted that unfamiliar days would have a high percent of unfamiliar drivers. For familiar time periods there was expected to not have a very high 93 percentage of vehicles registered in Athens County due to the large amount of student commuters traveling through the roundabout.
Table 7 shows averages for several events where county numbers were collected.
These results were consistent with the assumptions that were made. Additionally, a Chi-
Squared test was performed on that collected county numbers. The null hypothesis for this test is that the means of both groups are equal. A significance value of 0.033 was produced meaning the means of the groups are not equal. Therefore, this method for classifying familiarity of drivers was deemed as acceptable.
Table 7. Average percent of Athen's county vehicles
Athens County Event License Plates Familiar day (9/11/12) 52% Familiar day (9/20/12) 56% Graduate/PhD Graduation 35% Undergraduate Graduation 7% High School Basketball 20%
6.1.2 Sample Size
Initial sample sizes were calculated in order to determine the approximate number of data points that were needed for each parameter. Tables 5 and 6, presented in Section
4.2, show the initial sample sizes calculated. After collection of the data was complete, standard deviations were calculated and new sample sizes were updated. These calculated sample sizes were compared to the collected sample sizes. If the collected sample sizes were larger than the calculated values, then the amount of data collected would be 94 acceptable. If calculated values were larger than the collected values, then more data would be collected and the process would be reevaluated. After the initial sample size calculations, only one additional trial was performed and an acceptable amount of data was collected. The additional data collected for each parameter improved the accuracy of the final results. These values can be seen in Tables 8 and 9 for familiar and unfamiliar drivers, respectively.
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Table 8. Familiar driver final sample size calculations (Zβ= 0.842, Zα/2=-1.96)
Required Actual Parameter Leg Lane σ ε n n Richland N/A 4.08 15 52589 Approach Ave. Speed 3 Inside 11.84 123 42934 (mph) S.R. 682 Outside 17.03 254 10128 Richland Inside 2.08 9 478 Circulating Ave. Outside 2.15 10 548 Speed 2 Inside 6.07 73 496 (mph) S.R. 682 Outside 3.10 19 556 Richland Inside 3.12 77 511 Accepted Ave. Outside 2.88 65 866 1 Gap (s) Inside 2.82 63 395 S.R. 682 Outside 2.77 61 186 Richland Inside 1.59 20 240 Rejected Ave. Outside 1.63 21 334 1 Gap (s) Inside 1.64 22 372 S.R. 682 Outside 2.97 70 110 FINAL FAMILIAR FINAL Richland Inside 0.57 3 4 Critical Ave. Outside 0.44 2 11 1 Gap (s) Inside 0.47 2 5 S.R. 682 Outside 0.14 1 2 Richland Inside 0.52 1 4 Delay Ave. Outside 0.54 1 4 2 (sec/veh) Inside 0.19 1 2 S.R. 682 Outside 0.76 2 2 Richland N/A 1.41 1 8 Entrance Ave. Behaviors 5 (%) S.R. 682 N/A 4.09 6 6
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Table 9. Unfamiliar driver final sample size calculations (Zβ= 0.842, Zα/2=-1.96)
Required Actual Parameter Leg Lane σ ε n n Richland N/A 3.94 14 8729 Approach Ave. Speed 3 Inside 17.22 259 2579 (mph) S.R. 682 Outside 15.17 201 3412 Richland Inside 2.84 16 44 Circulating Ave. Outside 2.41 12 98 Speed 2 Inside 2.53 13 53 (mph) S.R. 682 Outside 2.81 16 148 Richland Inside 2.97 70 188
Accepted Ave. Outside 2.79 62 351 1 Gap (s) Inside 2.92 67 349 S.R. 682 Outside 2.95 69 438 Richland Inside 1.61 21 105 Rejected Ave. Outside 1.18 11 99 1 Gap (s) Inside 1.15 11 227 S.R. 682 Outside 1.57 20 213
FINAL UNFAMILIAR FINAL Richland Inside 0.49 2 2 Critical Ave. Outside 0.68 4 6 1 Gap (s) Inside 0.5 2 4 S.R. 682 Outside 0.52 3 5 Richland Inside 0.49 1 12 Delay Ave. Outside 0.83 2 12 2 (sec/veh) Inside 1.43 5 11 S.R. 682 Outside 1.8 7 10 Richland N/A 2.18 2 24 Entrance Ave. Behaviors 5 (%) S.R. 682 N/A 4.57 7 28
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6.2 Approaching Speeds
Approaching speeds were collected for both familiar and unfamiliar drivers.
Several different university events were studied in order to collect a variety of data. The sample sizes for approaching speeds were well over the calculated required amount, as shown in Table 8 and Table 9, generally due to the amount of data collected by the pneumatic tubes under continuous operation. Of this continuously running data, only times of day that were predetermined as familiar and unfamiliar times were used for this study. For graduation, data was collected two hours before and after the start of the event.
For Mom’s, Dad’s, and Sib’s weekends, a two hour window was chosen on both Friday and Sunday when family members were expected to enter and leave the university.
Homecoming weekend was studied similarly with additional data collection times on
Saturday based on a parade and football game. Additionally, a high school basketball tournament was studied where several games were played continuously. Therefore, with limited camera availability, data was collected when drivers were arriving and leaving the games in one hour intervals, accordingly.
When observing that data, there appeared to be time periods with large amount of vehicles in the slowest speed category for both legs of the roundabout. According to
FHWA’s Traffic Signal Timing Manual, stopped or near stopped conditions are typically defined as speeds below five miles per hour [62]. Therefore, all speeds below five miles per hour were considered to have no flow and congestion was most likely an issue.
Congestion may have been due to the formation of queue at a nearby intersection where a traffic signal controls the intersection. In order to determine how many drivers were 98 traveling under five miles per hour, the TraxPro software was rerun in order to obtain vehicle counts for lower speed limits. As stated earlier, congested times were not considered for this study, therefore, speeds below five miles per hour were disregarded.
Due to the two intersecting roads having very different speed limit, deviation from the speed limit was used in order to compare speeds between the two approaching legs. The speed limit of the roadway was subtracted from each data point. Therefore, drivers traveling above the speed limit had a positive deviation and drivers traveling below the speed limit had a negative deviation. A descriptive detail of the data is shown in Table 10.
Table 10. Approach speed descriptives
Sample Std. Std. Group Mean Size Deviation Error Fam_682_Inside 53590 -1.4 4.93 0.021 Fam_682_Outside 43379 -1.4 5.01 0.024 Fam_Richland 52589 3.6 3.79 0.017 Unfam_682_Inside 4752 -2.0 6.39 0.110 Unfam_682_Outside 5261 -3.8 8.06 0.081 Unfam_Richland 8729 3.0 3.79 0.041 Total 168300 0.3 5.37 0.013
Normality of the data was then checked by visual inspection of a histogram for each group. It was clear that all groups contained data that was distributed normally based on the visual analysis. This was determined by plotting a bell curve to fit the data.
A reference line for the median value of the data was also plotted. If the reference line 99 was near the peak of the bell curve, data was considered to be not skewed. For normal data, the shape of the data should represent the bell curve in the sense that the frequency of speeds would be large in the middle of the data and small near the edge of the data. An example of the distribution of data along with the bell curve and median value reference line can be seen for one group in Figure 12.
Figure 12. Example of approach speed histogram
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Next, a one-way ANOVA with a 95 percent level of confidence was used for comparison of this data. The null hypothesis for this test states that there is no difference in approaching speeds for familiar and unfamiliar drivers. Levene’s test was conducted in order to determine if homogeneity of variance existed. This test produced a significance of zero signifying that variances were unequal. Therefore, Welch’s F-ratio was used to correct the problems of unequal variances. This test also produced a significant result of zero meaning the groups’ means are not equal. The effect size, calculated by Pearson’s method, falls within the medium effective size range of 0.30 to 0.49. This means that somewhere within the data set there is an effect size of 0.46. The results of these tests are shown below.
Table 11. Results from ANOVA and Welch's tests
Deg. Source of Sum of Mean Calc. Crit. Param. of Result Variation Sq. Square F-ratio F-ratio Freedom Reject Model 1047740.6 5 209548 Approach Null 10729.615 9254.45 Speed ES= Residual 3813188.2 22583.283 22.643 0.46
Based on these results, the Games-Howell post hoc test was conducted and key comparisons are presented in Table 12. The results were compared to a significance value of alpha divided by the number of tests conducted in order to reduce Type I errors. For approach speeds, 15 tests were conducted and then compared to a significance of 0.05/15.
All of the key comparisons had a significance of 0.0 meaning they had unequal 101 approaching speeds. Cohen’s d was used to calculate the effect sizes for the key comparisons. All comparisons made in Table 12 had a small effect size. Therefore, familiarity had a small effect on approach speeds for each lane studied. After reviewing the descriptive values for the data set, it can be seen that familiar drivers had a tendency to have higher approaching speeds than unfamiliar drivers, showing that familiar drivers have more confidence and are prepared to navigate through the roundabout.
Table 12. Significance values of key group comparisons
Effect Group 1 Group 2 Significance Size Fam_682_Inside Unfam_682_Inside 0 0.11 Fam_682_Outside Unfam_682_Outside 0 0.45 Fam_Rich Unfam_Rich 0 0.11
6.3 Circulating Speeds
Circulating speeds were collected at two different locations within the roundabout. The locations chosen had two circulating lanes in order to compare speeds between the inside and outside lanes. The data was collected using a radar gun for both familiar and unfamiliar drivers. Because the advisory speed was the same for both locations that were studied, the original values that were collected were compared. Final sample sizes were larger than the required sample sizes calculated which can be seen in
Table 8 and Table 9. Details of each group’s sample size, mean, standard deviation, and standard error can be seen in Table 13.
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Table 13. Circulating speed descriptives
Sample Std. Std. Group Mean Size Deviation Error Fam_682_Inside 496 17.7 3.07 0.138 Fam_682_Outside 556 18.0 3.10 0.131 Fam_Richland_Inside 478 15.5 2.08 0.095 Fam_Richland_Outside 547 17.0 2.14 0.091 Unfam_682_Inside 53 16.2 2.53 0.348 Unfam_682_Outside 148 17.6 2.81 0.231 Unfam_Richland_Inside 44 14.4 2.84 0.428 Unfam_Richland_Outside 98 17.3 2.41 0.243 Total 2420 17.1 2.82 0.057
A histogram was plotted for each group shown above to examine normality. A bell curve was fitted to the data and compared to the median value of the data to examine skew. A peak for frequency is ideal with the high end and low end of the data converging to zero. Based on visual inspection, this data was distributed normally. An example histogram for one group is shown in Figure 13. 103
Figure 13. Example of a circulating speed histogram
A one-way ANOVA was performed for the analysis of circulating speeds.
Levene’s test was conducted and produced a significant result of zero. Therefore, group variances were unequal. Welch’s test was used to correct this issue. This test also resulted in a significant result indication equal to zero. The Pearson’s calculated effect size was on the lower end of the medium effect range which is between 0.30 and 0.49. This shows that somewhere within the data set lies this effect size. The results from the ANOVA and
Welch’s tests can be seen in Table 14.
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Table 14. Circulating speed results for ANOVA and Welch's tests
Sum Degrees Source of Mean Calc. Crit. Parameter of of Result Variation Square F-ratio F-ratio Sq. Freedom 2259.0 Reject Model 7 322.725 Circulating 76 Null 51.758 45.926 Speed 16949. ES= Residual 341.964 7.027 372 0.34
Post hoc tests were required in order to compare the groups individually. Since
Welch’s test produced significant results with unequal sum of squares and unequal variances, the Games-Howell post hoc test was conducted. Significant and insignificant results can be seen in Table 15. These results were compared to the Type I error divided by the number of tests conducted, or 0.05/28. Insignificant results, greater than 0.00179, mean the null hypothesis was accepted and the means of the two groups were equal. The groups with unequal means produced a significant result less than 0.00179. Only the key results can be seen in Table 15. Cohen’s method was used to calculate the effect size for these key comparisons.
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Table 15. Circulating speeds post hoc test key results
Significant Groups Insignificant Groups Effect Effect Group 1 Group 2 Sig. Group 2 Sig. Size Size Fam_682 0.632 0.12 Fam_682 Outside Inside Unfam_682 0.005 0.52 Inside Fam_682 Unfam_682 0.678 0.16 Outside Outside Fam_Rich Fam_Rich Unfam_Rich 0.000 0.53 0.200 0.40 Inside Outside Inside Fam_Rich Unfam_Rich 0.922 0.11 Outside Outside Unfam_682 Unfam_682 0.036 0.48 Inside Outside Unfam_Rich Unfam_Rich 0.000 0.96 Inside Outside
Based on Table 15, familiar and unfamiliar drivers performed the same for each lane that was studied. Additionally, driver performance based on lane choice was reviewed. Both familiar and unfamiliar drivers performed the same regardless of lane choice at S.R. 682. However, both groups of drivers performed differently at Richland
Avenue based on lane choice. Drivers had a tendency to travel faster in the outside lanes than the inside lanes for Richland Avenue. Of the two significant groups, unfamiliar drivers at Richland Avenue had a large effect size and familiar drivers at Richland
Avenue had a medium effect size. All insignificant groups had a small effect size except 106 the inside lane of S.R. 682 for familiar and unfamiliar drivers, which had a medium effect size. This may be due to the sample size not being large enough to determine if there is an effect on the data.
6.4 Critical Gap
Critical gap was computed based on both accepted and rejected gaps. The computation of critical gap, as well as the method for collecting accepted and rejected gap sizes, are discussed in the Methodology chapter. A critical gap value was computed after the required amount of accepted and rejected gaps were obtained based on the calculated sample sizes required. Although this method helps increase the sample size of overall critical gaps, this sample size is too small to visually determine if the data is distributed normally. Sample size calculations for accepted and rejected gaps were previously shown in Tables 8 and 9. Critical gap sample sizes as well as other descriptive statistics can be seen in Table 16.
Table 16. Critical gap descriptives
Sample Std. Std. Group Mean Size Deviation Error Fam_682_Inside 5 3.9 0.47 0.208 Fam_682_Outside 2 4.4 0.14 0.100 Fam_Richland_Inside 4 2.8 0.57 0.284 Fam_Richland_Outside 11 3.2 0.44 0.132 Unfam_682_Inside 4 3.1 0.50 0.250 Unfam_682_Outside 5 3.2 0.52 0.232 Unfam_Richland_Inside 2 3.6 0.49 0.345 Unfam_Richland_Outside 6 3.3 0.68 0.278 Total 39 3.3 0.60 0.096
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Since critical gap sample sizes were too small, accepted and rejected gaps were compiled and checked for normality. If both of these parameters were not distributed normally, further investigation would have been considered, and vice versa. In other words, if the accepted and rejected gaps were distributed normally then the critical gaps, which were obtained from accepted and rejected gaps, may be considered to have normal distribution. Accepted gap data was altered based on typical methods performed during other studies. A review of literature showed that gaps sizes larger than 13 seconds were typically always accepted, therefore, it is common to exclude data for accepted gaps larger than 13 seconds. For this study, accepted gaps larger than 13 seconds were considered irrelevant and were removed.
For accepted gaps 13 seconds and smaller and rejected gaps, visual inspection of the histograms was used for the initial determination of normality. A bell curve was plotted based on the data collected along with a reference line for the median value. If the median value was plotted near the top of the bell curve, the data is typically not skewed.
For normality, the frequency bars should typically follow the pattern of the bell curve.
The frequency of values should be large in the middle of the data and fade smaller towards both ends of the bell curve.
The accepted gap data appeared to be distributed normally. However, the rejected gap data was consistently skewed. This distribution of data is logical because a large amount of drivers will reject a one second gap while fewer and fewer drivers will reject gaps sizes as they become larger. In attempt to correct the skewness of the data, the three types of transformations discussed in the Methodology chapter were performed. Again, 108 these transformations included logarithm, square root, and reciprocal transformations.
The original data contained five groups out of the eight that were skewed. After the logarithm transformation was performed five groups remained skewed. The square root transformation was then conducted. This transformation only improved the data to four groups having skew and four groups not having skew. Finally, the reciprocal transformation was performed and six of the eight groups had skewed data. It was determined that the transformations were not useful in alleviating the skewness of the data, therefore, the original data was used for this study. An example of a group’s histogram for accepted and rejected gaps can be seen in Figures 14 and 15, respectively.
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Figure 14. Example of accepted gap histogram 110
Figure 15. Example of rejected gap histogram
Since only accepted gaps had normally distributed data, critical gap data could not be considered normal. Therefore, the nonparametric ANOVA analysis was conducted.
During this analysis, a Kruskal-Wallis test was performed. The null hypothesis for this test is that the means of each group are equal. An insignificant result of 0.077 was produced and the null hypothesis was accepted. Therefore, all groups have statistically equal critical gaps. No further testing needed to be conducted for this data. 111
6.5 Driver Entrance Behaviors
Behaviors of drivers entering the roundabout were recorded into six categories.
These categories included entering drivers completely stopping, partially stopping, and not stopping, all both the presence and no presence of cross traffic. These six categories were then separated into two groups, appropriate and inappropriate behaviors.
Completely stopping without the presence of cross traffic and not stopping with cross traffic present were considered to be inappropriate while the others were considered appropriate behaviors. The inappropriate behaviors were converted into a percentage in order to compare the data accurately without having an exactly equal number of vehicles for each time period. Therefore, the percent of all drivers behaving inappropriately were divided by all vehicles present for each one hour time period.
This data was separated by leg only because information based on lane choice was not desired. Data for each leg was collected in order to compare driver behaviors when entering or leaving Ohio University’s campus. Sample sizes were relatively small because approximately one hour of data was combined into one overall percentage. For this reason, normality of data did not exist. Descriptives of the data set is shown in Table
17.
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Table 17. Entrance behaviors descriptives
Sample Std. Std. Group Mean Size Deviation Error Fam_682 6 0.0805 0.05376 0.02195 Fam_Richland 8 0.0358 0.01382 0.00489 Unfam_682 28 0.1192 0.04566 0.00863 Unfam_Richland 24 0.0382 0.02122 0.00433 Total 66 0.0761 0.05294 0.00652
Due to the data being non-normal, a nonparametric ANOVA was performed. This statistical analysis also included a Kruskal-Wallis test. If this test produced an insignificant result, the null hypothesis of all groups having equal means would be accepted. For driver behavior, the Kruskal-Wallis produced a significant result equal to zero and the null hypothesis was rejected. Several Mann-Whitney tests needed to be conducted in order to compare the means of two groups at one time. The results of these tests for key comparisons can be seen in Tables 18 and 19. Pearson’s method was used to calculate sample size for these key comparisons. Both comparisons had equal means of groups and small effect sizes.
Table 18. Entrance behaviors significance results
Means of Group 1 Group 2 Significance Effect Size Groups Fam_682 Unfam_682 0.100 Equal 0.29 Fam_Richland Unfam_Richland 0.815 Equal 0.04
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Table 19. Non-parametric test results for key comparisons
S.R. 682 Richland Ave. Statistical Variable Familiar Unfamiliar Familiar Unfamiliar Kruskal-Wallis Test Sample Size, N 6 28 8 24 Mean Rank 41.33 49.93 17.44 17.73 Chi-Square 43.316 Degrees of Freedom 3.0 Significance 0.0 Mann Whitney Test N 6 28 8 24 Mean Rank 11.33 18.82 17.19 16.27 Sum of Ranks 68 527 137.5 390.5 Mann-Whitney U 47 90.5 Z -1.672 -0.24 P 0.1 0.815 Accept Null; Accept Null; Test Results Equal Means Equal Means
Tables 18 and 19 show that the percentage of inappropriate driver behaviors was the same for familiar and unfamiliar drivers at S.R. 682 and also for Richland Avenue.
The two key comparisons of familiar and unfamiliar drivers at the same leg had small effect sizes. Therefore, familiarity has a small practical effect on driver entrance behaviors for the same location.
6.6 Delay
Delay was calculated using an equation from the Highway Capacity Manual previously discussed in the Methodology chapter and using circulating and entering volumes in the calculation. Similarly to the other parameters collected by the use of a video camera, sample sizes for delay were small. This is because even though data from 114 many vehicles was collected, only one value was calculated for delay for each lane over about an hour period of time.
When the data was initially reviewed, one data point appeared to be an outlier.
The z-score of this outlier was calculated and found to be 2.62. This indicates that this value is on the outer limits of the data which is representative of 0.5 percent on each end.
This extreme value may have been caused by drivers behaving inappropriately and appears to not be representative of the data. This data point was considered an outlier and was therefore disregarded for this study. Table 20 shows the descriptive values for the data set studied.
Table 20. Delay descriptives
Sample Std. Std. Group Mean Size Deviation Error Fam_682_Inside 2 5.97 0.190 0.134 Fam_682_Outside 2 5.47 0.758 0.535 Fam_Richland_Inside 4 5.07 0.524 0.262 Fam_Richland_Outside 4 6.86 0.545 0.275 Unfam_682_Inside 11 6.30 1.435 0.433 Unfam_682_Outside 10 7.25 1.807 0.572 Unfam_Richland_Inside 12 4.83 0.498 0.144 Unfam_Richland_Outside 12 5.37 0.833 0.240 Total 57 5.87 1.379 0.183
Normality of the data was first considered. The data contained small sample sizes and therefore, the data could not be considered normal. For this reason, the nonparametric 115
ANOVA was used for this parameter. Along with the nonparametric ANOVA test, the
Kruskal-Wallis test was also performed. The null hypothesis for this test is that the means of all the groups are equal. This test produced a significant result or zero meaning the null hypothesis was rejected and the means of all groups were not equal. In order to compare the means of two groups at a time, the Mann-Whitney test was also performed for every key combination of groups. The results of the Mann-Whitney test are shown below.
Table 21. Delay Significance Results
Means of Effect Group 1 Group 2 Significance Groups Size Fam_682_Inside Unfam_682_Inside 0.769 Equal 0.11 Fam_682_Outside Unfam_682_Outside 0.121 Equal 0.50 Fam_Rich_Inside Unfam_Rich_Inside 0.521 Equal 0.18 Fam_Rich_Outside Unfam_Rich_Outside 0.020 Unequal 0.58
116
Table 22. Non-parametric test result for key comparisons
S.R. 682 Richland Ave. Statistical Inside Outside Inside Outside Variable Fam. Unfam. Fam. Unfam. Fam. Unfam. Fam. Unfam. Kruskal-Wallis Test Sample Size 2 11 2 10 4 12 4 12 Mean Rank 35.5 35.09 25.5 44.9 17.8 13.08 46 23.67 Chi-Square 29.361 Degrees of 7 Freedom Significance 0.0 Mann Whitney Test N 2 11 2 10 4 12 4 12 Mean Rank 8 6.82 2.5 7.3 10 8 13.25 6.92 Sum of 16 75 5 73 40 96 53 83 Ranks Mann- 9 2 18 5 Whitney U Z -0.395 -1.719 -0.728 -2.304 P 0.769 0.121 0.521 0.02 Reject Null; Accept Null; Accept Null; Accept Null; Test Results Unequal Equal Means Equal Means Equal Means Means
Tables 21 and 22 show that for the outside lane of Richland Avenue, familiar and unfamiliar drivers had a significantly different delay. All other lanes had equal means of groups between familiar and unfamiliar drivers in the same lane. When comparing familiar and unfamiliar drivers at the same location, the outside lanes had a large effect size and the inside lanes had a small effect size. However, for the outside lane of S.R. 682 familiar and unfamiliar drivers had equal means but a large effect size. This is a result of an insufficient sample size. Therefore, familiarity had a small effect for the inside lanes.
Familiarity had a large effect on the outside lane of Richland Avenue. The effect of 117 familiarity on the outside lane of S.R. 682 was inconclusive. Further discussion about these results will be presented in the Conclusion chapter below.
118
CHAPTER 7: CONCLUSIONS
The main objective of this study was to determine if there was a significant difference in driver behavior between familiar and unfamiliar drivers. The parameters that were studied include approaching speed, circulating speed, critical gap, entrance behaviors, and delay. A case study was performed at the multilane modern roundabout in
Athens, Ohio at the intersection of State Route 682 and Richland Avenue. This roundabout contains two sections where there are two circulating lanes and two sections with one circulating lane. The main legs used to travel to the city and leave the city were focused on during this research. Each of these legs has two lanes entering the roundabout.
Richland Avenue enters the roundabout with one cross traffic lane while S.R. 682 has two cross traffic lanes when entering the roundabout. Each parameter that was studied will be discussed in the chapter along with recommendations for future research in this field.
7.1 Approach Speeds
Pneumatic tubes were used to measure approaching speeds. The recorded measurements were converted to a deviation from the speed limit. If a driver traveled below the speed limit the deviation would be negative. Similarly, drivers traveling above the speed limit would have a positive deviation. The speed limit for Richland Avenue is
25 mph and S.R. 682 is 50 mph. The location where approach speeds were recorded allowed for two lanes of data collection at S.R. 682 but only one lane on Richland
Avenue. 119
The results showed that for all three lanes studied, unfamiliar drivers had a tendency to drive slower than familiar drivers. This is most likely because unfamiliar drivers are unsure of what they may be approaching or the layout of the roundabout. All key comparisons had a small effect size. Additionally, vehicle speeds were above the speed limit on Richland Avenue and below on S.R. 682. The speed limit for Richland
Avenue is 25 mph which made it easier for drivers to speed on this roadway. S.R. 682 has a high speed limit of 50 mph. In order for drivers to slow down to navigate that roundabout, it is necessary for drivers to travel slower than the speed limit at this leg of the roundabout.
7.2 Circulating Speeds
Circulating speeds were collected at the two locations with two circulating lanes.
This was done in order to compare driver behavior between the inside and outside lanes.
Circulating speeds were measured using a radar gun. The advisory speed for the roundabout is 20 mph. Collected speeds were compared rather than deviation from speed because the advisory speed is the same for both locations studied.
The statistical analysis of these results showed many details of the data. First, for each lane studied, familiar and unfamiliar driver performed the same. This is most likely due to the layout of the roundabout. The diameter of the center island along with the circulating motion controls the speed that drivers can safely transverse through the roundabout.
Additionally, a comparison of speed based on lane choice was also reviewed. For both familiar and unfamiliar drivers, speeds were the same for each lane at S.R. 682. 120
However, speeds were different for the inside and outside lane at Richland Avenue for both familiar and unfamiliar drivers. This result is likely due to the layout of the roundabout. At S.R. 682, which was studied when vehicles were expected to enter the university’s campus, both lanes have a similar travel pattern. Regardless of whether vehicles are entering the roundabout from S.R. 682 or the southern leg of Richland
Avenue, both lanes are able to travel to the campus. On the other hand, the two lanes when leaving the campus at Richland Avenue have different directions of travel. Vehicles in the inside lane travel through the roundabout making a motion equivalent to a left turn.
Vehicles in the outside lane travel through the roundabout and then continue straight on
Richland Avenue. As the inside lane serves as a left turning lane and the right lane serves as a through lane, the circulating speeds varied for both familiar and unfamiliar drivers.
7.3 Critical Gap
A video camera was used to record data in order to obtain critical gap. Accepted and rejected gaps were extracted from the videos. Accepted and rejected gaps were collected for each lane of each leg which was studied. Accepted gaps larger than 13 seconds were disregarded for this study because this size gap is typically always accepted. Critical gap was obtained from these accepted and rejected gaps.
For the inside lane of S.R. 682, drivers are required to cross one lane of traffic before merging into their lane of travel. An initial assumption may have been that this location would have a larger critical gap size. After initially inspecting the mean values for each group they appeared to be very similar to one another. The data was statistically analyzed in order to determine if the difference between groups means were significant. 121
The Kurskal-Wallis test which was performed showed that all groups means are equal.
Therefore, familiar and unfamiliar drivers performed the same with respect to critical gap at all locations that were studied. The reason for this may be because drivers that have to cross an extra lane of traffic in order to enter the roundabout tend to be more aggressive.
7.4 Entrance Behaviors
Entrance behaviors were recorded into six different categories; entering drivers completely stopping, partially stopping, or not stopping with and without cross traffic present. Entering drivers completely stopping with no cross traffic present and not stopping with cross traffic present were considered to be inappropriate behaviors. The percentages of inappropriate behaviors per approximate one hour periods were obtained.
Familiar and unfamiliar driver behaviors were compared for each leg traveled. The results of the statistical tests showed that at both legs for both levels of familiarity the percent of inappropriate behaviors were not statistically different. Familiarity had a small practical effect on driver entrance behaviors for the same location.
7.5 Delay
Due to limitations, delay was calculated based on formulas from the Highway
Capacity Manual. Circulating and entrance volumes are the main variables used in the delay calculation. Information that was extracted from the video cameras for other parameters was able to be used to obtain frequency of these two variables. Delay was calculated for a one hour period of time for each lane that was studied.
After statistically analyzing the data, means for familiar and unfamiliar drivers for the outside lane of Richland Avenue were unequal. All other lanes had equal means of 122 groups between familiar and unfamiliar drivers in the same lane. Familiarity had a small effect for the inside lanes. Familiarity had a large effect on the outside lane of Richland
Avenue. The descriptive of the data showed that familiar drivers at this location had a larger delay than unfamiliar drivers. This may be due to the difference in traffic volumes that travel through south on Richland Avenue. Familiar drivers consist of commuters that often travel on the outside lane of Richland Avenue while most unfamiliar drivers do not travel this leg. The effect of familiarity on the outside lane of S.R. 682 was inconclusive due to insufficient sample sizes. Calculation of delay may have caused error because the calculation is based on numbers and not how drivers perform.
Many of the results discussed above are due to the layout of the roadway, traffic volumes by leg, and vehicle turning movements. However, the main focus of this study was to compare familiar and unfamiliar drivers. For approaching speeds, unfamiliar drivers had a tendency to drive slower than familiar drivers. At all locations studied, familiar and unfamiliar drivers had equal circulating speeds. Both groups of drivers also performed the same for critical gap and driver entrance behaviors. Additionally, familiar drivers had a larger delay than unfamiliar drivers for the outside lane of Richland
Avenue. All other lanes had equal means between familiar and unfamiliar drivers.
Based on these results, the only parameters that have been proven to have a different performance based on driver familiarity are approaching speeds and delay. The deviation in speed may lead to an increase in crash risk as some drivers may travel at higher speeds while other drivers may travel at slower speeds approaching roundabout.
Delay was larger for familiar drivers at the outside lane of Richland Avenue. However, 123 delay was calculated on volumes and not on driver performance. Both familiar and unfamiliar drivers had equal means for circulating speeds, critical gap, and entrance behaviors.
7.6 Recommendations for Future Research
One of the most common recommendations is to collect more data. For this type of research it would be appropriate to not only collect a larger number of vehicles but to also collect data at different sites. For this particular roundabout, it would be ideal to collect several of the same events in order to compare these as well. For example, graduation and homecoming may produce a significantly different results and this could be taken into consideration. Also, if every parameter could be collected for each event this would also help with limiting the variation of data.
As stated earlier, only volumes were used in the calculation of delay when realistically driver performance can be a factor in delay as well. It would be recommended to collect delay manually, rather than by calculation, at a location that would allow for this or to use different equipment that would make it possible. A feature that would have been greatly helpful is a camera mounted above the roundabout in order to obtain an aerial view. With the right installation, this could be used to collect delay, driver behaviors, critical gap, and other parameters. Another recommendation would be to collect other types or parameters as well in order to obtain a stronger conclusion.
There is clearly a need for research on this topic. This study contained many limitations such as layout of the roundabout, equipment available, and a time constraint.
Conducting another study while implementing the recommendations stated above will be 124 an asset to the determination of the relationship between driver familiar and performance in modern roundabouts.
125
REFERENCES
1) Roundabouts: A Safer Choice. Publication FHWA-SA-08-006. FHWA, U.S. Department of Transportation, 2008.
2) NH’s Roundabouts. Business NH Magazine, Vol. 27, No. 4, 2010, pp. 16-18.
3) Rodegerdts, L., Bansen, J., Tiesler, C., Knudsen, J., Myers, E., Johnson, M., Moule, M., Persaud, B., Lyon, C., Hallmark, S., Isebrands, H., Crown, R.B., Guichet, B., and O’Brien, A. Roundabouts: An Informational Guide Second Edition, NCHRP Report No. 672, National Cooperative of Highway Research Program, 2010.
4) ITE Technical Council Committee 5B-17. Use of Roundabouts. ITE Journal, Vol. 62, No. 2, 1992, pp. 42-45.
5) Kareem, Y. A. A. Conventional Roundabouts: Their Effectiveness as Road Intersection Control. University of Ilorin, Nigeria. http://ilorin.info/papers/publications/CONVENTIONAL%20ROUNDABOUTS %20THEIR%20EFFECTIVENESS%20AS%20ROAD%20INTERSECTION% 20CONTROL.pdf. Accessed Sept. 29, 2011.
6) Rodegerdts, L. A., A. Cibor, A. Pochowski, and Kittelson & Associates, Inc. Status of Roundabouts in North America. Presented at the 87th Annual Meeting of the Transportation Research Board, Washington, D.C. 2008.
7) Federal Highway Administration Office of Safety. Roundabouts: Technical Summary. FHWA-SA-10-006. FHWA, U.S. Department of Transportation.
8) Florida Roundabout Guide. Florida Department of Transportation. http://www.dot.state.fl.us/trafficoperations/doc_library/pdf/roundabout_guide8_ 07.pdf. Accessed Feb. 7th, 2013.
9) Nemani, V. G. A Comparison of Operational Performance between Modern Roundabouts and Two-Way Stop Controlled Intersections. ITE Student Paper Contest, 2002.
10) Uddin, W. Performance Evaluation of Roundabouts for Traffic Delay and Crash Reductions in Oxford, MS. FHWA/MS-DOT- RD-11-213. FHWA, U.S. Department of Transportation, 2011. 126
11) Status Report. Insurance Institute for Highway Safety, Vol. 36, No. 7, July 28, 2001.
12) Persaud, B. N., R. A. Retting, P. E. Garder, and D. Lord. Observational Before- After Study of the Safety Effect of U.S. Roundabout Conversions Using the Empirical Bayes Method. Presented at the 80th Annual Meeting of the Transportation Research Board, Washington, D.C., 2001.
13) Rodegerdts, L., M. Blogg, E. Wemple, E. Myers, M. Kyte, M. Dixon, G. List, A. Flannery, R. Troutbeck, W. Brilon, N. Wu, B. Persaud, C. Lyon, D. Harkey, and D. Carter. Roundabouts in the United States, NCHRP Report No. 572, National Cooperative of Highway Research Program, 2007.
14) Roundabout Design: Safety and Capacity. https://www.aaafoundation.org/sites/default/files/Roundabouts.pdf. Accessed Jan 18th, 2013.
15) Polus, A., Shiftan, Y., and S. Shmueli-Lazar. Evaluation of the Waiting-Time Effects on Critical Gaps at Roundabouts by a Logit Model. European Journal of Transport and Infrastructure Research, Vol. 5, No. 1, 2005, pp.1-12.
16) Modern Roundabouts: The Website. Kittelson & Associates, Inc. http://roundabouts.kittelson.com/Roundabouts/Search. Accessed January 14th, 2013.
17) Zheng, D., X. Qin, and D.A. Noyce. Negotiation-Based Conflict Exposure Methodology in Roundabout Crash Pattern Analysis. Presented at the 89th Annual Meeting of the Transportation Research Board, Washington, D.C., 2010.
18) Savolainen, P. T., J. Kawa, and T. J. Gates. Examining Statewide Public Perceptions of Roundabouts Through Web-Based Survey. Presented at the 91st Annual Meeting of the Transportation Research Board, Washington, D.C., 2012.
19) McKnight, G.A., A. J. Khattak, and R. Bishu. Driver Characteristics Associated with Knowledge of Correct Roundabout Negotiation. Transportation Research Record: Journal of the Transportation Research Board, No. 2078, Transportation Research Board of the National Academies, Washington D.C., 2008, pp. 96-99. 127
20) Retting, R. A., S. Y. Kyrychenko, and A. T. McCartt. Long-Term Trends in Public Opinion Following Construction of Roundabouts. Transportation Research Record: Journal of the Transportation Research Board, No. 2019, Transportation Research Board of the National Academies, Washington D.C., 2007, pp. 219-224.
21) Jie, C., Y. Xinmiao, D. Wei, and H. Xin. Driver’s Critical Gap Calibration at Urban Roundabouts: A Case Study in China. Tsinghua Science and Technology, Vol. 13, No. 2, April 2008, pp. 237-242.
22) Xu, F. and Z. Z. Tian. Driver Behavior and Gap-Acceptance Characteristics at Roundabouts in California. Transportation Research Record: Journal of the Transportation Research Board, No. 2071, Transportation Research Board of the National Academies, Washington D.C., 2008, pp.117-124.
23) Tupper, S. M., M. A. Knodler Jr., C. Fitzpatrick, and D. S. Hurwitz. Estimating Critical Gap- A Comparison of Methodologies Using a Robust, Real-World Data Set. Presented at the 92nd Annual Meeting of the Transportation Research Board, Washington, D.C., 2013.
24) Al-Ghandour, M., B. Schroeder, W. Rasdorf, and B. Williams. Delay Analysis of Single-Lane Roundabout with a Slip Lane under Varying Exit Types, Experimental Balanced Traffic Volumes, and Pedestrians: Using Microsimulation. Presented at the 91st Annual Meeting of the Transportation Research Board, Washington, D.C., 2012.
25) Mahlawat, M. and Y. Zhang. Driver Risk Taking Behavior as a Function of Congestion Level: An Analysis Using Adopted Headways in Traffic Stream. Presented at the 87th Annual Meeting of the Transportation Research Board, Washington, D.C., 2008.
26) Retting, R.A., S. Mandavilli, A.T. McCartt, and E. R. Russell. Roundabouts, Traffic Flow and Public Opinion. Traffic Engineering and Control, Vol. 47, No. 7, 2006, pp. 268-272.
27) Dissanayake, S. and L. Perera, Highway Safety Issues of Older Drivers in Kansas. Report No. K-TRAN: KSU-07-3. Kansas Department of Transportation, 2009.
28) Retting, R.A., G. Luttrell, and E.R. Russell. Public Opinion and Traffic Flow Impacts of Newly Installed Modern Roundabouts in the United States. Institute of Transportation Engineers Journal, Vol. 72, No. 9, 2002, pp. 30-37. 128
29) Jacquemart, G. NCHRP Synthesis 264: Modern Roundabout Practice in the United States. National Academy Press, Washington, D.C., 1998.
30) Inman, V. W., B. J. Katz, and F. R. Hanscom. Navigation Signing for Roundabouts. Transportation Research Record: Journal of the Transportation Research Board, No. 1973, Transportation Research Board of the National Academies, Washington D.C., 2006, pp.18-26.
31) Savolainen, P. T., J. M. Kawa, A. J. McArthur, and T. J. Gates. A Review of Roundabout Public Information and Educational Programs and Materials. Presented at the 91st Annual Meeting of the Transportation Research Board, Washington, D.C., 2012.
32) Bowling Green Public Works. Traffic Data Collection Procedures. The City of Bowling Green, Kentucky: Municipal Government. http://www.bgky.org/publicworks/planningdesign/transportation/pdf/Traffic_Da ta_Collection_Procedures.pdf. Accessed January 23, 2002.
33) Dixon, M., Abdel-Rahim, A., Kyte, M., Rust, P., Cooley, H., and Rodegerdts, L. Field Evaluation of Roundabout Turning Movement Estimation Procedures. Journal of Transportation Engineering, Volume 133, No. 2, 2007, pp. 138-146.
34) TRB. Highway Capacity Manual: HCM 2000. CD-ROM. Transportation Research Board, National Research Council, Washington, D.C., 2000.
35) Tan, J. Comparison of Capacity Between Roundabout Design and Signalized Junction Design. Presented at the 1st Swiss Transport Research Conference, Monte Verità Asconan, 2001.
36) Akcelik, R. Roundabout Level of Service. Akcelik & Associates Pty Ltd. 2009.
37) Robinson, B. W., Rodegerts, L., Scarborough, W., Kittelson, W., Troutbeck, R., Brilon, W., Bondzio, L., Courage, K., Kyte, M., Mason, J., Flannery, A., Myers, E., Bunker, J., and Jaquemart, G. Roundabouts: An Informational Guide. Publication FHWA-RD-00-067. FHWA, U.S. Department of Transportation, 2000.
38) Hellinga, B., and Sindi, A. An Analytical Method for Estimating Delays to Vehicles Traversing Single-lane Roundabouts as a Function of Vehicle and Pedestrian Volumes. Presented at the 91st Annual Meeting of the Transportation Research Board, Washington, D.C., 2012.
39) City of Athens, Burgess & Niple, and Strand Associates Inc. Richland Avenue & S.R. 682 Intersection Improvements, 2010. 129
40) Pochowski, A. A Review of Statewide Roundabout Policies. Presented at the 90th Annual Meeting of the Transportation Research Board, Washington, D.C., 2011.
41) Ministry of Works and Transport, Roads Department. Traffic Data Collection and Analysis. 2004.
42) Gross, F., C. Lyon, B. Persaud, and R. Srinivasan. Safety Effectiveness of Converting Signalized Intersections to Roundabouts. Presented at the 91st Annual Meeting of the Transportation Research Board, Washington, D.C., 2012.
43) Status Report: Roundabouts. Insurance Institute for Highway Safety, Vol. 36, No. 7, 2001, pp. 1-8.
44) Taylor, D. B. and H. S. Mahmassani. Behavioral Models and Characteristics of Bicycle-Automobile Mixed-Traffic: Planning and Engineering Implications. SWUTC/98/60056-1, College Station, Texas. http://library.ctr.utexas.edu/digitized/SWUTC/60056-1.pdf. Accessed February 10th, 2013.
45) Mauro, R. and F. Branco. Comparative Analysis of Compaact Multilane Roundabouts and Turbo-Roundabouts. Journal of Transportation Engineering, Vol. 136, No. 4, 2010, pp. 416-322.
46) Vasconelos, L. and A.M. Seco. Estimation of Critical and Follow-up Headways at Roundabouts. Presented at the 91st Annual Meeting of the Transportation Research Board, Washington, D.C., 2012.
47) Mensah, S., Eshragh, S., and A. Faghri. A Critical Gap Analyses for Modern Roundabouts. Presented at the 89th Annual Meeting of the Transportation Research Board, Washington, D.C., 2010.
48) Garber, N. J. and L. A. Hoel. Traffic & Highway Engineering, Third Edition. Brooks/Cole, California, 2002.
49) Irvena, J. and S. Randahl. Analysis of Gap Acceptance in a Saturated Two-Lane Roundabout and Implementation of Critical Gaps in VISSIM. Traffic and Roads, Department of Technology and Society, Lund University.
50) Fox N., A. Hunn, and N. Mathers. Sampling and Sample Size Calculation. The NIHR RDS for the East Midlands / Yorkshire & the Humber, 2007. http://rds- eastmidlands.nihr.ac.uk/. Accessed February 14, 2013. 130
51) Gharaibeh, N. G., S. I. Garber, and L. Liu. Determining Optimum Sample Size for Percent-within-Limits Specifications. Presented at the 89th Annual Meeting of the Transportation Research Board, Washington, D.C., 2010.
52) Hinkle, D. E., W. Wiersma, and S. G. Jurs. Applied Statistics for the Behavioral Sciences: Fifth Edition. Houghton Mifflin Company, Boston, New York, 2003.
53) Busam, S. G. Safety Evaluation of Diamond-grade vs. High-intensity Retroreflective Sheeting on Work Zone Drums: A Field Study and Driving Simulator Validation Study, Ohio University Thesis and Dissertation Services, Athens, Ohio, 2011.
54) McAvoy, D. S. The Evaluation of Steady Burn Warning Lights Comparing a Field Experiment. Simulator Experiment, and Repeated-Use Experiment, UMI Dissertation Services, Ann Arbor, Michigan, 2007.
55) Ohio University. Enrollment and Freshman Class Information. www.ohio.edu/focus/. Accessed February 1st, 2013.
56) Google Earth. www.earth.google.com. Accessed February 1st, 2013.
57) Ohio Athletics. Facilities. http://www.ohiobobcats.com/facilities/ohio- facilities.html. Accessed February 1st, 2013.
58) Bing. http://www.bing.com/maps. Accessed February 4th, 2013.
59) Burgess and Niple, Limited. Richland Avenue Traffic Study: Athens, Ohio. April 2000.
60) TRAX Flex HS User’s Manual Volume 1.5. Jamar Technologies, Inc., Hatfield, Pa., 2008.
61) Field, A. Discovering Statistics Using SPSS. 2nd Edition. Sage Publications Ltd., London, 2005.
62) Office of Operations. Traffic Signal Timing Manual; Ch. 3. FHWA, U.S. Department of Transportation. http://ops.fhwa.dot.gov/publications/fhwahop08024/chapter3.htm. Accessed April 30, 2013.
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