I Driverless Vehicles' Potential Influence on Cyclist and Pedestrian

Driverless Vehicles’ Potential Influence on Cyclist and Pedestrian Facility Preferences

THESIS

Presented in Partial Fulfillment of the Requirements for the Degree Master of City and Regional

Planning in the Graduate School of The Ohio State University

Michael Julian Armstrong Blau, BA

Graduate Program in City and Regional Planning

The Ohio State University

2015

Master's Examination Committee:

Gulsah Akar, advisor

Jack Nasar

Jason Sudy

Michael Julian Armstrong Blau

2015

Abstract

Research in the field of autonomous vehicle technology focuses on the enhanced safety and convenience it will likely convey to vehicle occupants. This thesis seeks to establish a new and equally important line of inquiry that addresses the same implications for cyclists and pedestrians. It is well-established that motorized traffic volume and speed have a strong influence on non-motorized agents’ behavior and facility preference but whether this will continue to be the case in a driverless environment remains unknown.

A stated-preference survey was crafted asking respondents to select their preferred facility in various scenarios with and without the presence of driverless vehicles and on street types of varying motorized traffic volumes and speeds. An ordered logit model was estimated to illustrate that street type had a very strong influence on cyclists’ preferences for more separated facilities as traffic volume and speed increased. The presence of driverless vehicles significantly amplified this trend. Preferences for bike intersection features, pedestrian facilities, and pedestrian crossing behavior are also examined. Infrastructure and policy recommendations are presented as well as suggestions for future research in this nascent field of study.

Dedication

I dedicate this thesis to the countless victims who have lost their lives on the road. It is my hope that this research will contribute to a future in which we can bring the number of needless traffic

fatalities closer to zero.

iii

Acknowledgments

I would like to express my sincere gratitude to the following people

for their guidance, patience, and support during this research:

Gulsah Akar

David Blau

Sasha Gallant

Jack Nasar

Jason Sudy

Vita

2009...... B.A. Psychology, Goucher College

2012...... Transportation Intern

City of Asheville, NC

2013 to 2015...... Healthy Places Program Assistant

Columbus Public Health

2015...... Capital Projects and Planning Assistant

Central Ohio Transit Authority

2015...... Teaching Assistant

Driverless Vehicle Transportation Studio

City and Regional Planning, The Ohio State

University

Publications

Blau, M. (2009). The Straight Way: A Narrative Study of Conversion to Islam. Verge (6).

Baltimore: Goucher College.

Blau, M. (2009). The American Ummah Project: Using Ethnodramatic Social Action to De-other

the Muslim-American Community. Goucher College, Baltimore, MD.

Fields of Study

Major Field: City and Regional Planning

Table of Contents Abstract ...... ii

Dedication ...... iii

Acknowledgments...... iv

Vita ...... v

List of Tables ...... ix

List of Figures ...... x

Chapter 1: Introduction ...... 1

Historical Precedents ...... 2

Chapter 2: Literature Review ...... 9

Autonomous Vehicles: Background and State of the Technology ...... 9

Projected Timelines ...... 10

State of the Technology ...... 11

Infrastructure ...... 17

Behavior Determinants ...... 20

Cyclists ...... 27

Bike Infrastructure ...... 27

Behavior Determinants ...... 28

Chapter 3: Methodology ...... 36

Recruitment ...... 36

Response Rate ...... 38

Descriptive Statistics ...... 40

Transportation Characteristics...... 41

Survey Content ...... 43

General Results ...... 45

Ordered Logit Model ...... 52

Hypothesized Results ...... 54

Post-estimation Analysis ...... 61

Multinomial Logit Model ...... 63

Comparisons ...... 66

Limitations ...... 67

Chapter 5: Discussion ...... 73

Bike and Pedestrian Infrastructure in a Driverless World ...... 73

Bike Infrastructure ...... 75

Bike Intersection Features ...... 78

Chapter 6: Policy Recommendations ...... 76

Public Awareness Campaign ...... 78

Small-Scale Driverless Technology as an Educational Tool ...... 80

Autonomous Vehicle Operating Standards—Collision Avoidance ...... 83

The Full Picture: ...... 88

vii

Bike and Pedestrian Facilities and Autonomous Vehicle Operating Standards...... 88

I. Vehicle ...... 88

Chapter 7: Conclusion...... 92

References ...... 95

Appendix A: Survey Instrument ...... 111

Appendix B: Survey Response Rates by Question ...... 131

Appendix C: Change in Bike Faci lity Preference by Street Type, Revised ...... 133

Appendix D: Bike Facilities Generalized Ordered Logit Model ...... 134

Appendix E: Other Scenarios ...... 136

Bike Intersection Features ...... 136

Pedestrian Crossing Behavior ...... 139

Pedestrian Facilities...... 142

Appendix F: Pedestrian Facilities Models ...... 144

Appendix G: Mode Choice in Four Driverless Environments...... 147

viii

List of Tables

Table 1: Crossing Behavior Literature Summary ...... 22

Table 2: Cyclist Behavior Literature Summary ...... 29

Table 3: OSU Sample Statistics: ...... 37

Table 4: Descriptive Statistics ...... 39

Table 5: Transportation Characteristics ...... 42

Table 6: Facility Choices and Behavior Types by Scenario ...... 44

Table 7: Percent of Respondents with Same Answer for Both Conditions ...... 46

Table 8: Chi Squared Results ...... 47

Table 9: Variable Definitions...... 56

Table 10: Bike Facilities Ordered Logit Model Iterations ...... 57

Table 11: Bike Facilities Ordered Logit Model ...... 59

Table 12: Brant Test Results ...... 62

Table 13: Bike Facilities Multinomial Logit Model ...... 64

Table 14: Marginal Effects of Driverless Vehicles on Facility Choice ...... 66

Table 15: Facility Recommendations ...... 74

Table 16: Bike and Pedestrian Intersection Features ...... 78

Table 17: Bike Facilities Generalized Ordered Logit Model...... 135

Table 18: Pedestrian Facilities Ordered Logit Model ...... 144

Table 19: Pedestrian Facilities Brant Test Results ...... 145

Table 20: Pedestrian Facilities Multinomial Logit Model ...... 146

List of Figures

Figure 1: Pro- and anti-automobile campaign materials ...... 4

Figure 2: Correlation between autonomous vehicles and decreased wait times ...... 15

Figure 3: Diagonal crosswalks ...... 20

Figure 4: Example Walk Audit Ratings ...... 27

Figure 5: Change in Bike Facility Preference by Street Type ...... 48

Figure 6: Change in Preference by Facility ...... 51

Figure 7: Buffered bike lane depicted in survey ...... 70

Figure 8: Elevated parks and greenways in Paris, New York, and La Paz ...... 82

Figure 9: Floating bike roundabout in Holland (left) and rendering of proposed SkyCycle in

London ...... 83

Figure 10: Fraction of U.S. motor vehicle deaths relative to total population...... 77

Figure 11: Rendering of a driverless shuttle operating on the OSU campus ...... 82

Figure 12: The perils of socially acceptable collision avoidance ...... 87

Figure 13: Change in Bike Facility Preference by Street Type, Choices 5 & 6 Combined...... 133

Figure 14: Change in Bike Intersection Features Preference by Street Type ...... 137

Figure 15: Change in Pedestrian Crossing Behavior by Street Type ...... 141

Figure 16: Change in Pedestrian Facility Preference by Street Type ...... 143

Figure 17: Mode Choice in Four Different Driverless Environments ...... 148

Chapter 1: Introduction

The advent of driverless vehicles is fast approaching. As they proliferate their presence will drastically change society, just as automobiles did 100 years ago. We can only speculate as to how they will alter fields including transportation planning, urban design, public policy, economics, environmental studies, and public health, to name a few. The question of how society will plan for and adapt to the spread of autonomous vehicles is complex. I intend to address a very specific question within this emerging field:

How will the proliferation of autonomous vehicles affect the built environment for pedestrians and bicyclists and how might it change their perceptions, preferences, and behavior?

Research in the field focuses on the interactions between automated and autonomous vehicle technology and the human drivers or occupants of the vehicle (Hamish, Merat,

Carsten, & Lai, 2013; Le Vine, Zolfaghari, & Polak, 2015). This thesis seeks to establish a new and equally important line of inquiry that addresses the same implications for cyclists and pedestrians. Findings suggest that there will likely be a greater public demand for separated and protected bike and pedestrian infrastructure in a driverless

1 society. Planners, engineers, and policy-makers will need to address this demand in their decisions.

The thrust of my research focuses on what may occur after complete adoption of autonomous vehicles. Considering that cars built in the 1970s and ‘80s still cruise the streets, this scenario will likely occur many decades from now1 and the question of how it will impact pedestrians and cyclists must be revisited frequently as technology develops and trends change.

In current non-motorized and active transportation terminology, it is customary to refer to cyclists as vehicles, which confers on them the same rights and responsibilities as motorists. However, for the sake of convenience and clarity I use the term vehicles only when referring to motorized vehicles. Non-motorized roadway users will be specified as either pedestrians or cyclists.

The terms autonomous vehicles and driverless vehicles are used interchangeably throughout the thesis. I explain distinctions between these and other terms used to described the technology in Chapter 2.

Historical Precedents

To situate this research within a broader historical framework, I compare and contrast the automobile’s initial impact on pedestrians in the early 20th Century with potential

1 This prediction applies to cities in general. There may be special cases, such as university campuses, that see the onset of driverless vehicle technology and the disappearance of human-driven vehicles earlier than the rest of society. I discuss this possibility in Chapter 6. 2 implications for the arrival of driverless vehicles. Chapter 2 provides a synopsis of how cyclists and pedestrians function in our current transportation network, with particular attention to their facility preferences and behavior. Finally, Chapters 5 and 6 theorize how cyclists and pedestrians should function in a fully driverless environment.

There will likely be parallels between the automobile’s arrival and the advent of the autonomous vehicle, and history may give us some clues as to how this new mode of transportation will affect other road users. Before the automobile, city streets were the people’s domain. The roadways accommodated pedestrians, horses and buggies, streetcars, and a number of other modes. It was generally accepted that pedestrians had the right-of-way (Mars, 2013). Traffic moved at a slow pace, as evidenced by the fact that it was considered normal to tell one’s children to “Go outside, and play in the streets” (Mars, 2013, para. 1). Streets were not just a conveyance for various vehicles; they were a space of social activity.

The dawn of the automobile drastically changed the roadway environment. Fast, noisy, dirty cars violated the established norms of the road. With a growing number of vehicles on the road in the 1920’s, pedestrian fatalities increased as drivers pushed for higher speed limits. Between 1920 and 1923 instances of automobile drivers killing other road users rose by 47 percent (Johnson, 2013) and children bore the brunt of these fatalities. A strong backlash threatened to derail the still-nascent popularity of the automobile.

Resistance to the new technology took the form of anti-automobile clubs, which sprouted up in major cities throughout the country. These groups claimed that the automobile was

3 the “Modern Moloch,” an ancient god who supposedly received child sacrifices (Mars,

2013). The public agitated for jail sentences for reckless drivers (Mars, 2013).

The car lobby met this onslaught with a concerted campaign that painted pedestrians, not cars, as the problem (see Figure 1). A survey of newspaper articles from the 1920’s and

30’s reveals a plethora of stories describing anti-jaywalking campaigns nationwide. At the time, a “jay” was a derogatory term used to describe a person from the country who is unfamiliar with cities and their many attractions. A jaywalker, therefore, “is someone who walks around the city like a jay, gawking at all the big buildings, and who is oblivious to traffic around him” (Mars, 2013, para. 8). Proponents of the automobile redefined and popularized the term to refer to clueless pedestrians who did not follow the rules, acting as a nuisance to other roadway users.

Figure 1: Pro- and anti-automobile campaign materials Sources: Mars, 2013; Jackson Citizen Patriot, 1922

The campaign of shaming and humiliating pedestrians worked in concert with anti- jaywalking laws that empowered police to fine and arrest pedestrians who tried to cross mid-block. Major cities across the country passed these ordinances, the first instance occurring in Los Angeles in 1924 (Johnson, 2013)2. Studies such as the one conducted in

New York in 1922, claiming that out of 7,337 reported accidents the driver was to blame in only 450 cases (Jackson Citizen Patriot, 1922), further bolstered the legislation. In conjunction with the car lobby’s agenda to shape popular opinion and social norms, these efforts effectively exiled pedestrians to the roadside and car enthusiasts claimed the road for the automobile.

The fact that pedestrians seemed to be at fault in most automobile accidents is likely because for the first time in history human beings were no longer the dominant users of the road. The advent of the automobile and its effect on pedestrians is the most dramatic instance of a technological innovation in transportation supplanting one mode with another. Certain parallels can be drawn between this historic shift and the arrival of autonomous vehicles. For example, currently we can only theorize what pedestrian street-crossing behavior will look like in a driverless society, just as people at the turn of the last century never imagined that within 20 years they could be jailed for crossing the

2 Ordinances that allowed police to fine and arrest jaywalkers were passed as early as 1917 in smaller towns (Kalamazoo Gazette, 1917).

5 street at an undesignated location. But could autonomous vehicles conceivably bring another wave of oppression against other road users?

Many cities in the United States are slowly recognizing that comprehensive bike and pedestrian amenities are an essential component of a livable urban environment (Pucher,

Buehler, & Seinen, 2011). In addition to this newfound awareness on the municipal level, the federal government has incorporated bike and pedestrian policies, facilities, advocacy, and research into many of its funding initiatives3, acknowledging the strong advocacy movement focused around cyclist and pedestrian issues, led nationally by organizations such as the League of American Bicyclists.

Given the confluence of these trends, it seems unlikely that cyclists or pedestrians would be subject to as brutal an onslaught of discrimination and disempowerment under driverless vehicles as they were during the rise of the automobile. Autonomous vehicles will use existing roadway infrastructure rather than usurping space from pedestrians and cyclists, as the automobile did. As autonomous vehicles proliferate, human-driven vehicles will be the most endangered form of transportation, not pedestrians and cyclists.

And this historic mode shift should herald a safer environment for all roadway users instead of the carnage wrought by the automobile. That being said, it is difficult to predict how driverless vehicles will alter the built environment. Active transportation

3 The Manual on Uniform Traffic Control Devices now includes guidelines for bike and pedestrian facilities (Federal Highway Administration, 2015), and bike and pedestrian projects are eligible for funding through federal grants, including the Surface Transportation Improvement Program, Transportation Investment Generating Economic Recovery, Transportation Enhancement Activities, and Congestion Mitigation and Air Quality (Federal Highway Administration, 2014). 6 advocates, planners, and policy-makers must continue to safeguard cyclists’ and pedestrians’ rights to ensure that this new technology does not subvert them.

In the next chapter I present a brief description of the state of autonomous vehicle technology and projected timelines for its adoption, as well as a review of the literature on cyclist and pedestrian facility preferences. I then describe this study’s methodology and data collection, followed by an analysis of the results. The analysis relies on a web- based stated preference survey of 1,312 cyclists and pedestrians that measures behavior and facility preferences under current and driverless conditions, conducted at The Ohio

State University in 2014. Survey results were used to estimate ordered and multinomial logit models that predict what facilities non-motorized agents will prefer in a driverless society. The models measure the effects of having driverless vehicles on the road after controlling for the factors that are known to affect bicycling and walking preferences.

Despite the expected benefits of driverless technology to public health and safety— namely, a dramatic reduction in traffic fatalities (Wierwille et al, 2002; Lin, 2013;

Fleming, 2010)—survey results show, and the models confirm, that respondents overwhelmingly preferred more separated and protected facilities when driverless vehicles were present compared to preferences under current conditions. Based on these findings, recommendations on policy and infrastructure are outlined to overcome this public misperception and lay the groundwork for an informed, empirically-based approach to non-motorized infrastructure and design in a driverless society. In addition, I describe several hypothetical scenarios that illustrate how cyclists, pedestrians, vehicles,

7 and infrastructure will interact in a fully driverless transportation network. The thesis concludes with suggestions for future research.

Autonomous vehicles will change the foundation of our society and the field of planning, leaving long-held assumptions such as travel time savings by the wayside4. We can only speculate as to how this technology will affect social equity, population growth patterns, land use, and so on. Discussion of these fields is beyond this study’s purview as it focuses primarily on the changes affecting cyclists and pedestrians; however, it is important to remember that these changes will not occur in isolation—they will coincide with other historic shifts in society due to the proliferation of autonomous vehicles.

4 Planners often judge proposed transportation projects—such as toll roads, tunnels, or transit service—by the time it will save travelers. These criteria may become less relevant since longer trips may not have a negative influence if travelers can use that time for other activities, such as work, leisure, or sleep.

Chapter 2: Literature Review

Several elements of autonomous vehicle technology are described below5. The next section summarizes the literature on pedestrian and cyclist infrastructure, facility preferences, and behavior determinants, focusing on how motorized traffic influences cyclists’ and pedestrians’ perceptions of safety, route-selection, and use of infrastructure.

Autonomous Vehicles: Background and State of the Technology

In 2013 the National Highway Traffic Safety Administration issued a white paper that identified five levels of vehicle automation:

 Level 0: The driver completely controls the vehicle at all times.  Level 1: Individual vehicle controls are automated, such as electronic stability control or automatic braking.  Level 2: At least two controls can be automated in unison, such as adaptive cruise control in combination with lane keeping.  Level 3: The driver can fully cede control of all safety-critical functions in certain conditions.  Level 4: The vehicle performs all safety-critical functions for the entire trip, with the driver not expected to control the vehicle at any time. (National Highway Traffic Safety Administration, 2013)

This thesis uses the terms driverless vehicle and autonomous vehicle interchangeably.

Both of these terms refer to a Level 4 vehicle in which humans have been removed from

5 The complexity of this subject goes well beyond what is covered here but a basic understanding of the mechanics of the technology is important for the purposes of interpreting this study. 9 any decision-making processes and the vehicle is responsible for all functions at all times.6

Clearly, advances in and field-testing of technology must occur before Level 4 vehicles are commercially available, let alone widespread. With this caveat in mind, the issues this thesis seeks to examine occur in an environment where all motorized vehicles on the road are completely autonomous, meaning that level 4 functionality is ubiquitous and that additional elements, such as vehicle-to-vehicle (V2V) communications technology and smart infrastructure systems like Autonomous Intersection Management (AIM)— discussed below—are also in place.

Projected Timelines

The shift to more automated vehicles began decades ago, with the advent of automatic gear shifting, followed by power steering, and then cruise control (Vanderbilt, 2012).

Indeed, the very term “automobile” implies some basic form of autonomy on the machine’s part. Autonomous vehicles are the next logical step in the technology’s progression. One could argue that vehicles are already more automated than not. What were once fundamental driving skills—shifting gears, navigating, parking—are now becoming obsolete, and what is currently an innate part of driving—steering, braking— will soon become anachronistic as well.

6 There is a distinction to be made between these and other terms used to describe self-driving vehicles; an automated vehicle, for example, may describe a Level 2 vehicle in which partial control is ceded to the vehicle. 10

Car manufacturers and technology companies give varying dates as to when they will offer fully autonomous models: Audi plans to have one by 2017, Google by 2018, Nissan by 2020, Jaguar/Land Rover by 2024, Daimler by 2025 (Hars, 2015), although legal restrictions may bar it from functioning at full autonomy. In addition, leading universities and government organizations are conducting research and field tests; these include the Department of Defense, The Ohio State University, MIT, Carnegie Melon

University, and Cornell University, among many others. The Institute of Electrical and

Electronics Engineers predicts that autonomous vehicles will “account for up to 75 percent of cars on the road by the year 2040” (Institute of Electrical and Electronics

Engineers, 2012); IHS Automotive, an auto industry consultant, estimates that by 2035, autonomous vehicles will account for nine percent of global automobile sales (Reuters,

2013). These estimates place the proliferation of autonomous vehicles well within the standard 30 year time period used by transportation planners in forecasting and long- range planning.

State of the Technology

Driverless technology is rapidly evolving, and the platforms described below may be obsolete well before driverless vehicles dominate the roadways. With this caveat in mind, the following section explains the standard machinery and software that allow driverless vehicles to function.

Object Detection, Tracking, and Collision Avoidance

Basic driverless technology enables vehicles to navigate through a “cluttered urban environment, detecting and tracking fixed objects, moving objects, pedestrians, curbs, and roads” (Aufrère et. al, 2003). More recent developments in technology have matched, if not exceeded, the limits of human ability. Google, for example, now uses software that can simultaneously distinguish between hundreds of moving and static objects and determine the appropriate response based on algorithms and the vehicle’s previous experience. It can recognize and interpret a crossing guard holding up a stop sign, a pedestrian about to step out into the street, even a cyclist’s hand signals (Google

Blog, 2014).

Light Detection and Ranging (LIDAR)

LIDAR uses laser technology to scan the vehicle’s surroundings, illuminating everything from trees to pedestrians. These roof-mounted systems are integral to the design of all autonomus vehicles, constantly rotating as fast as 600 rpm (Iliaifar, 2013) to ensure that the vehicle’s onboard computers are fully aware of its surroundings while making decisions. More advanced technology observes specific features of the environment.

Some test vehicles are equipped with facial recognition software and processors that can tell the difference between an ambulance and a regular car, for example, or a pedestrian with or without a stroller (Sedgwick, 2015).

Smart Infrastructure: V2V, V2I, and V2X

Well before the arrival of fully autonomous vehicles, V2V (Vehicle-to-Vehicle)-equipped cars will be on the road, exchanging information about traffic conditions and other

12 environmental elements. Currently, human drivers function on a user-optimal basis; that is, they make decisions based on what will get them to their destination in the quickest and most convenient manner (Vanderbilt, 2008). The fusion of V2V and driverless technology will allow the roadway network to operate more efficiently at the macro level, or on a system-optimal basis. Due to their near-instant reaction time, vehicles will be able to travel in tightly formed platoons which, to today’s driver, would resemble reckless driving and widespread tailgating. With more vehicles operating safely and efficiently and requiring less space than human drivers, the roadway network’s capacity may effectively increase with no physical expansion required. By exchanging information amongst one another vehicles will, in effect, be able to “see” what is coming around the corner and even miles beforehand—be it a traffic jam, an ambulance, or a child in the street—and react accordingly.

The development of smart infrastructure will allow computer-controlled roadway features—from street lights to traffic control devices—to communicate with vehicles, known as V2I (Vehicle-to-Infrastructure) communication. This technology is already widely used to give signal prioritization to emergency and rapid transit vehicles. Lastly,

V2X synthesizes V2V and V2I technology into a single, coordinated, network. A V2X- enabled driverless vehicle could, for example, encounter an unknown object in the road and, unsure of how to react, send this information to a central computer that is connected to thousands of other vehicles (Sedgwick, 2015). The computer scans through its library of objects that other vehicles have documented, selects the best match to the unknown object, and in a matter of microseconds sends this information back to the vehicle, which

13 continues on if the object turns out to be a plastic bag and takes evasive action if the object is a baby7.

Autonomous Intersection Management (AIM)

Stone and Dresner (2008) created a comprehensive model for AIM using a multiagent system. A server located at an intersection would process “reservation” requests from oncoming driverless vehicles to enter the intersection and perform a given maneuver.

The server, called an intersection reservation manager, could field thousands of requests every minute and would render traffic lights obsolete (Badger, 2012; Dresner and Stone,

2008), assuming all road users were autonomous vehicles. According to Dresner and

Stone’s model (see Figure 2), when 95 percent of vehicles are autonomous, there is a major drop in delays and accidents. Once the system reaches full autonomy, there are virtually no delays and accidents are exceedingly rare.

7 The question of ethics in driverless vehicles has yet to be fully explored, but will likely lead to major controversy in the years ahead. Who is to decide what lives and what gets run over? For an in-depth discussion see Lin, 2014. 14

Figure 2: Correlation between autonomous vehicles and decreased wait times Source: Dresner and Stone, 2008

AIM will directly affect pedestrians’ crossing behavior and cyclists’ use of the roadway; yet to date, this model does not fully account for non-motorized agents. Stone proposed two options through which pedestrians and cyclists could be integrated into a multiagent approach to AIM: dedicated crossing times (via push-buttons) during which no reservations are given to cars—in other words, temporal separation of vehicles and pedestrians using the same technology that is widespread today; or, equipping pedestrians and cyclists with transponders so that they can request reservations from the intersection manager.

User-activated signals like the push buttons Stone suggests would be familiar to today’s urban pedestrian. On the other hand, wearing a transponder so that the intersection manager could detect oncoming cyclists would be a novel and potentially anxiety- inducing task. Since a cyclist will still control his or her bike, some sort of alert system would need to be installed that could inform the rider of the intersection manager’s decision, dictating the rider’s next action. This device could either be embedded in the bike itself or on the person riding the bike. Whatever the case, the responsibility to obey the intersection manager’s decision would rest with the human rider and not his or her bike, as opposed to the autonomous car that makes these decisions without human interference. This remnant of human control in an otherwise autonomous system could cause accidents. Though some have brought attention to this flaw (Alpert, 2012; Badger,

2012) no alternative or improved model has been proposed.

The Role of Cyclists and Pedestrians

There is a growing body of research that studies how people will interact with autonomous vehicles in the future. However, most of this research approaches the question from the driver’s perspective (Hamish et al, 2013; Le Vine et al, 2015). To date, modeling of autonomous vehicles’ behavior focuses almost exclusively on motorized traffic and ignores the issue of interactions with other roadway users, which will be just as important, if not more so. This reveals a number of questions that have yet to be answered: what roles will cyclists and pedestrians have in a driverless society? How will the presence of driverless vehicles change pedestrians’ and cyclists’ facility preferences and behavior? Specifically, will cyclists and pedestrians prefer more separated and

16 protected facilities under driverless vehicle conditions than they do under current conditions? Before addressing these questions, I provide a description of common pedestrian and bike infrastructure and examine the body of research on pedestrian and cyclist behavior determinants.

Pedestrians

Infrastructure

Pedestrian facilities can be sorted into two basic classes: those that are pedestrian- exclusive, such as a sidewalk, and those that are shared with other modes, such as a plaza with limited vehicle access or a woonerf.8 Pedestrian-exclusive facilities that are separated from vehicles are divided into three categories: horizontal separation, vertical separation, and temporal separation (Braun & Roddin, 1978). Sidewalks and pedestrian crossing signals are the most common forms of horizontal and temporal separation, respectively. A horizontally separated facility can be parallel to vehicular traffic or displaced from the roadway network. Most pedestrian infrastructure is located adjacent to the road for convenient access to destinations. A quadrangle on a college campus is an example of a displaced network; these facilities are less common.

The most widespread type of vertical or grade-separated facility is the pedestrian overpass or underpass. Although rarer than horizontally separated facilities, vertical separation does offer several benefits that other facilities lack: by eliminating both spatial

8 Woonerfs are streets that do not delineate separates spaces for different modes. They are typically used in dense, urban environments, particularly in Europe, where slow speeds are expected.

17 and temporal competition between motorists and pedestrians, grade-separated facilities reduce accidents and fatalities (Braun & Roddin, 1978). The use of Grade Separated

Pedestrian Systems or GSPS (Cui, Allan, & Lin, 2013) became commonplace in many cities during the 20th Century, often taking the form of Underground Pedestrian Systems

(UPS). There are a number of reasons for channeling pedestrian activity underground: protection from inclement weather, connecting various land uses, degradation or lack of street-level facilities, and linking to transit hubs (Cui et al., 2013). Skywalk systems are generally less expensive than UPS but the existence of underground transit networks such as subway systems may justify the construction of UPS instead.

The success of GSPS is mixed. Tanaboriboon and Jing (1994) found that pedestrians prefer signalized, at-grade crossings to grade-separated crossings. Räsänena, Lajunenb,

Alticafarbayc, and Aydinc (2007) uncovered several factors that positively influence pedestrian bridge use, including convenience, time-savings, and safety benefits.

However, grade-separated facilities can also be inconvenient, especially for disabled users. They require more effort to access than at-grade facilities and can also be aesthetically dissonant with their surroundings, especially in historic urban centers. The final and biggest drawback to grade-separated facilities is their high construction cost

(Braun & Roddin, 1978).

Horizontally separated infrastructure offers less protection than its vertical counterpart but allows more convenient access to adjacent land uses (Braun & Roddin, 1978).

Although they can provide some level of separation between vehicles and pedestrians,

18 poorly designed facilities or ones that are diminished for the benefit of motorized traffic can be unpleasant, and in some cases, dangerous. In addition, horizontally separated facilities must compete for a finite amount of space with the vehicular roadway network in the public right-of-way.

Temporal separation is common in dense urban areas with high potential for conflict between pedestrians and vehicles. It generally offers the least protection because it regulates space that is used by both modes, often at the same time. Signalization tends to force pedestrian crossing movements simultaneously with parallel traffic flow, which causes potential for conflicts with turning vehicles. Diagonal crosswalks (Figure 3) combined with universal red lights could solve this problem but are rarely used because they degrade vehicle level of service with longer wait times (Richard, 2008).

Figure 3: Diagonal crosswalks Source: Richard, 2008

Behavior Determinants

The literature on pedestrian behavior models is divided into two categories: route choice and crossing behavior (Papadimitriou, Yannis, & Golias, 2009). I begin here with a survey of crossing behavior before examining determinants of pedestrian facility choice, which spans both categories. Route choice studies a number of factors influencing pedestrian behavior, including “crowd and evacuation dynamics…congestion, bi- directional flow and bottleneck situations” (Papadimitriou et al., 2009, p. 242). Crossing behavior models generally use gap acceptance theory or utility theory to explain pedestrians’ decision-making. In gap acceptance theory, each pedestrian has a critical gap in which to cross the street between vehicular traffic. Preferred gap distance depends on the individual, which fluctuates based on a number of factors including distance from

20 oncoming vehicles, traffic speed and volume (Hankey et al., 2012; Yagil, 2000; Yang,

Den, Wang, & Wang, 2005), waiting time (Li & Fernie, 2010; Tiwari, Bangdiwala,

Saraswat, & Gauray, 2007; Van Houten, Ellis, & Kim, 2007), reaction time, confidence level, time of day, and presence of other pedestrians (Rosenbloom, 2009; Zhuang & Wu,

2011). In addition, risk tolerance varies greatly between individuals and has a major influence on decision to cross.

Utility theory treats relative cost, time and effort as the primary factors influencing decision-making (Heinen, Maat, & van Wee, 2010); time has long been considered a

“fundamental unit of cost in transport activities” (Keegan & O’Mahony, 2003, p. 891).

Applied to crossing behavior, every crossing decision has a number of spatial or temporal alternatives such as delaying crossing or using a different location. Each of these alternatives is treated as a latent random variable (Papadimitriou et al., 2009) whose effect depends on individual preferences and the relative benefits and drawbacks of the alternative (such as increased or decreased wait time, more or less effort involved, and greater or lower level of risk). Table 1, partially compiled from Brosseau, Zangenehpour,

Saunier, & Miranda-Moreno (2013) provides a partial summary of the literature on crossing behavior.

Table 1: Crossing Behavior Literature Summary

Authors Topic Findings Bell, Miri, Chan, & Mount, 2012; Motorized Higher levels increase perception McAndrews, Flórez, & Deakin, traffic of danger 2006; National Highway Traffic volume Safety Administration, 2008 and speed

Bernhoft & Carstensen, 2008; Guo, Age and Elderly and females more risk Gao, Yang, Jiang, gender averse 2011; Rosenbloom, 2009; Tiwari et al., 2007; Yagil, 2000; Zhuang & Wu, 2011 Evans & Norman, 1998; Moyano Personal Young people have a more Dı́az, 2002; Zhou, Horrey, & Yu, attitudes positive attitude towards 2009 committing violations Brosseau, Zangenehpour, Saunier, Group Larger groups increase perceived & Miranda-Moreno, 2013; size and actual safety Rosenbloom, 2009; Zhuang & Wu, 2011 Guo et al., 2011Li & Fernie, 2010; Waiting Shorter waiting times reduces Tiwari et al., 2007; Van Houten et time dangerous behaviors al., 2007 Lavalette et al., 2009; Cinnamon, Traffic Violation rates depend on street Schuurman, & Hameed, 2011; violation width and presence of signals Sisiopiku & Akin, 2003; Yang et al., 2005 Li & Fernie, 2010; Yang et al., Weather Inclement weather increases risky 2005 conditions behavior Cinnamon et al., 2011; Zhuang & Land use Connectivity and convenience are Wu, 2011 important

A number of complex decision-making processes occur every time a pedestrian approaches a crossing. Researchers have developed models using the Theory of Planned

Behavior (TPB) to explain different facets of pedestrian behavior before and during road crossings: Evans and Norman (1998) applied TPB to crossing intentions and constructed

22 a hierarchical regression model showing that social and psychological variables and perceived control predicted the manner in which people cross; Yagil (2000) used a multivariate regression model to reveal gender differences in risky crossing behavior and found that normative motives are only predictive of male behavior; and Moyano Dı́az

(2002) constructed a structural equations model based on TPB that explains risky behavior as a function of attitude, perceived control, and intentions, finding that young people and men are more likely to commit traffic violations.

Turning to more practical explanations of crossing behavior, individual attributes and environmental factors are found to be significant. Brousseau et al. (2013) found that group size increases perceived and actual safety and that countdown displays reduce risky behavior. In agreement with other research, Brosseau et al. (2013) and Mfinanga (2014) found that gender and age influence pedestrians’ preferences in crossing facilities: women generally wait longer than men to cross (Tiwari et al., 2007), are less likely to jaywalk than men, and perceive more risk when doing so (Holland & Hill, 2007). Young people and men tend to commit more traffic violations than adults and women, respectively (Moyano Dı́az, 2002; Rosenbloom et al., 2004). Regardless of gender, a pedestrian’s risk tolerance increases with waiting time (Tiwari et al., 2007). Older adults are more likely to cite safety reasons, respect for the law, and lack of trust in their own cognitive and physical abilities as determinants in their pedestrian behavior than are younger adults (Bernhoft & Carstensen, 2008).

Environmental determinants of pedestrian behavior include the presence and character of motorized traffic. Bell, Miri, Chan, & Mount (2012) looked at seven factors that influence pedestrians’ perception of a shared space environment9, including the intensity of motorized and pedestrian traffic. They concluded that a high volume of motorized traffic is usually associated with an increased perception of danger to pedestrians. This finding is confirmed by earlier research (McAndrews, Flórez, & Deakin, 2006).

Sisiopiku & Akin (2003) used survey and observational data to study how the placement of crosswalks affected pedestrians’ compliance rate (i.e. the percentage of people who cross at designated areas). The researchers recorded the crossing compliance rate with respect to crosswalks placed midblock and at intersections. They found that midblock crosswalks were more attractive to pedestrians than signalized crosswalks at intersections and concluded that most users will cross at a crosswalk rather than at the most convenient location. Although it is perceived to be riskier (Rouphail, 1984), it is statistically safer to cross mid-block rather than at an intersection, where turning motorists are not always watching for crossing pedestrians that are parallel to their travel paths (Vanderbilt, 2008).

The research on pedestrian facility preference has established that lack of pedestrian facilities, along with traffic volume and speed, are the main sources of perceived risk

(Todd & Walker, 1980). The National Survey of Bicyclist and Pedestrian Attitudes and

9 Shared space is a concept developed by Dutch traffic engineer Hans Monderman that rejects conventional roadway design—which emphasizes segregation of modes and prioritizes vehicles over pedestrians—and encourages mingling of different road users, resulting in more uncertainty and therefore more awareness of one’s surroundings (Vanderbilt, 2008). 24

Behavior found that 62 percent of pedestrians cited speeding motorists as the primary reason they felt threatened (National Highway Traffic Safety Administration, 2008). The survey also found that the most common reason for not using sidewalks or paths was lack of convenience; in other words, facilities were unavailable or existing facilities did not provide efficient access to destinations. Bernhoft and Carstensen (2008) found that 40 to

60 percent of pedestrians consider the availability of sidewalks an important factor in route choice, and Replogle (1995) found that their presence strongly predicts commute mode choice. By contrast, Rodríguez, Aytur, Forsyth, Oakes, and Clifton (2008) concluded that sidewalks and traffic did not affect walking levels in their sample and that the availability of car parking was a much more important factor, suggesting that the presence of pedestrian facilities is not always correlated with higher rates of walking.

When using crossing facilities, pedestrians prefer raised crosswalks or traffic islands

(Hine & Russell, 1996). Concerning preferences for on- or off-road facilities, they prefer paths with some separation but which are still connected to the street network rather than completely displaced in order to gain convenient access to destinations (Nuworsoo,

Cooper, Cushing, & Jud, 2014), which corroborates findings from the national survey.

There is a growing literature around the concept of “walkability” and what factors contribute to a walkable environment (Ewing, 1999; Parsons, Brinckerhoff, Quade, &

Douglas, 1993; Pikora et al., 2002; Sarkar, 1993). To establish a normative definition of walkability, researchers have developed various pedestrian level of service (LOS) measures (Clifton, Rodriguez, & Livi Smith, 2007; Sarkar, 1993). Important factors in pedestrian LOS include safety, convenience, connectivity, aesthetics, and facility

25 condition. In practice, municipal governments frequently use these findings to guide their decisions about pedestrian infrastructure. Many cities and planning organizations conduct sidewalk inventories to assess coverage and connectivity of pedestrian facilities.

On the neighborhood level, cities use walk audits or walkability assessments to measure pedestrian LOS, determine where facilities are lacking or in disrepair, and promote areas that are found to be highly walkable. City staff and residents assess sidewalk conditions, width, connectivity, Americans with Disabilities Act (ADA) compliance, crosswalks, street furniture, and landscaping. Walk audits also consider roadway attributes such as traffic volume and speed, presence of parked cars, transit stops, bike facilities, and type of land use that the road serves (Robb, 2015). Figure 4 shows an example walk audit rating scale. The data gathered during a walk audit can be used to justify pedestrian infrastructure improvements and prioritize funding. In reality, facility options are usually limited to different sidewalk configurations and attributes such as landscaping and width; pedestrian bridges, tunnels, and separated walking paths are rare. Although walk audits typically do not cover all of the facilities discussed in this thesis, their practical application to facility preference offers an instructive example of pedestrian-friendly planning and street design.

Sidewalk conditions Street and furniture conditions

Figure 4: Example Walk Audit Ratings Source: Robb, 2015

Cyclists

Bike Infrastructure

Bike facilities are commonly divided into two categories. Class I facilities are separated from motorized traffic, providing a distinct network that is accessible only to cyclists and other non-motorized modes. Off-road cycle tracks, bike paths, and shared use paths comprise Class I facilities. Class II facilities are on-street, meaning they use the same infrastructure as motorized traffic, and are reserved for the exclusive use of cyclists (Dill

& Carr, 2003). The bike lane is the most common Class II facility, typically separated from vehicles by striping, signage, or physical barriers such as bollards. According to

27 some studies it is also the most popular facility overall, probably because it provides some protection from vehicular traffic and is more convenient than bike paths (Nuworsoo et al., 2014)10. Other on-street facilities include sharrows, bike boulevards, and signed bike routes, although cyclists must share these facilities with motorized traffic. Class I facilities are typically more expensive and require more space, and thus may be easier to construct in low-density areas. Although less cost-prohibitive, it is also difficult to find space for Class II facilities, as typically they must fit within existing right-of-way, sharing space with traffic lanes, parked cars, sidewalks, and utilities.

Behavior Determinants11

Many studies have examined the factors that affect cycling behavior and attitudes. There is substantial disagreement over determinants of cyclists’ facility preferences.

Fernández-Heredia, Monzón, & Jara-Díaz (2014) divide the psycho-social factors that influence cyclist behavior into three categories: socio-demographic characteristics, cyclist choice factors, and latent variables. In addition to these, other variables that have been found to influence cycling behavior include demographics (family size, age, income, gender), trip characteristics (destination, length, duration), and external factors

(weather, topography, built environment). Table 2, partially compiled from Hunt and

Abraham (2006) provides a partial summary of the literature.

10 This contradicts other findings (Schoner et al., 2014; Dill, 2009; Caulfield et al., 2012; McClintock & Cleary, 1996), discussed below. 11 For a comprehensive review of the literature on influences of bicycle use, see Hunt and Abraham (2006). 28

Table 2: Cyclist Behavior Literature Summary

Authors Topic Findings User-friendliness of roadways shape Caulfield et al., 2012; Dill, attitudes; some 2009; Hankey et al., 2012; cyclists exhibit McClintock & Cleary, 1996; Personal attitudes cavalier attitudes Nuworsoo et al., 2014; towards pedestrians Schoner, Lindsey, & and deferential Levinson, 2014 attitudes towards motorists

Mixed: separated Antonakos, 1994; Axhausen facilities lose & Smith, 1986; Broach et importance as cyclists al., 2012; McClintock & Experience/confidence gain experience; Cleary, 1996; Nuworsoo et levels cyclists of all al., 2014; Sorton & Walsh, confidence levels 1994; Taylor & Mahmassani, prefer Class I 1996 facilities

Females exhibit a lower risk tolerance Akar et al., 2013; Byrnes, than males; female Miller, & Schafer, 1999; commuters prefer DeGruyter, 2003 ; Dill & routes with maximum Voros, 2007; Garrard, Rose, separation from Gender, income, and & Lo, 2008; Krizek, motorized traffic; age Johnson, & Tilahun, 2005; higher incomes Moudon et al., 2005; Pucher correlate with greater & Buehler, 2008; Tilahun, odds of choosing Levinson, & Krizek, 2007 more attractive facilities; cycling rates decline with age

Continued

Table 2 continued

Abraham, McMillan, Brownlee, & Hunt, 2004; Akar & Clifton, 2009; Antonakos, 1994; Axhausen & Smith, 1986; Bradley & Bovy, 1984; Calgary, 1993; Chataway, Kaplan, Alexander, Nielsen, & Reductions in traffic Giacomo Prato, 2014; Davis, volume and speed 1995; Epperson, 1994; increase people’s Fernández Heredia et al., 2014; likelihood to bike; Garrard et al., 2007; Hankey et stress levels increase in Motorized traffic al., 2012; Hopkinson & conjunction with traffic volume and speed Wardman, 1996; Jacobsen, speed; inverse Racioppi, & Rutter, 2009; correlation between Landis & Vattikuti, 1996; Mars traffic volumes and & Kyriakides, 1986; speeds and levels of McClintock & Cleary, 1996; cycling; Providelo & da Penha Sanches, 2011; Sener, Eluru, & Bhat, 2009; Sorton, 1995; Sorton & Walsh, 1994; Stinson & Bhat, Tilahun et al., 2007; Hoedl, Titze, & Oja, 2010

Fear of accidents with motorized traffic is the Antonakos, 1994; Kroll & primary deterrent for Ramey, 1977; Kroll & would-be cyclists; Sommer, 1976; Lott, Tardiff, Safety concerns presence of bike & Lott, 1978; McClintock, facilities increases 1996; Nuworsoo e al., 2014 streets’ perceived safety

Motorized traffic speed and volume strongly influence cycling behavior. Davis (1995) finds broadly that these are the most important factors governing cyclists’ behavior.

Hopkinson and Wardman (1996) found that reductions in traffic volume and speed were among the top incentives that would increase people’s likelihood to bike. Sener et al.

(2009) determined that cyclists will travel more than ten minutes to avoid motorized traffic speeds of 20-35 mph and more than 20 minutes to avoid speeds in excess of 35 mph. Their findings indicate that avoiding motorized traffic is more important than time savings. Sorton and Walsh (1994) add that cyclists’ stress levels increase in conjunction with traffic speed. Traffic travelling at 25 mph induces medium stress levels and traffic travelling at 45 mph induces high stress levels. Together, these two studies provide strong evidence that motorized vehicle speed has a monotonic, negative impact on cyclists’ willingness to travel in mixed traffic.

In addition to speed, Providelo and da Penha Sanches (2011) found that lane width was among the most important roadway attributes influencing cycling behavior. McClintock and Cleary (1996) concluded that roadway design, such as wide lanes that encourage speeding and accommodate large volumes of motorized traffic, was the most significant influence on cyclist behavior. Fear of traffic amongst cyclists is connected to gender, car frequency use, and cycling frequency (Chataway et al., 2014). Females exhibit a lower risk tolerance than males and are more likely to be afraid riding in mixed traffic (Akar et al. 2013; Byrnes et al., 1999; Garrard et al., 2006; Krizek et al., 2005; Tilahun et al.,

2007; DeGruyter, 2003). Garrard et al. (2007) confirm this finding, concluding that

female cyclists base their route choice primarily on “maximum separation from motorized traffic” (p. 55).

In agreement with the above findings, a number of studies (Tilahun et al., 2007;

Hopkinson and Wardman, 1996; Abraham et al.; 2004) using stated preference surveys found that cyclists would pay more for off-road facilities even with an accompanying increase in trip time. These results illustrate the interaction between the presence of motorized traffic, the type of existing bike infrastructure, and their combined influence on cycling behavior.

Others have found that preference for separated facilities loses importance as cyclists gain experience (Hunt and Abraham, 2006; Taylor and Mahmassani, 1996) and that preference for bike lanes (ie. less separated facilities) rises with experience. In terms of street type, Stintson and Bhat (2003) concluded that respondents preferred residential streets, most likely because of lighter traffic volumes and slower speeds. Their respondents also preferred Class II facilities such as bike lanes more than Class I facilities such as bike paths.

Tilahun et al. (2007) caution that these results may be due to sample populations, many of whom were self-selected cyclists. The preference for bike lanes in some studies could indicate that in certain cases connectivity and convenience are more important than safety and complete separation from motorized traffic. In addition, experienced riders may

value travel time more than perceived safety and opt for in-traffic facilities, even if they prefer separated facilities when time is not an issue.

The use of bike facilities is directly related to perceived risk, as protected facilities are generally thought to be safer than in-traffic facilities (Schoner et al., 2014; Dill, 2009;

Caulfield et al., 2012; McClintock & Cleary, 1996). Others have found that a well- designed bike network encourages bicycle use (Broach et al., 2012; Taylor and

Mahmassani, 1996) whereas even a comprehensive network will fail if real or perceived safety concerns arise (de Dios Ortuzar, Iacobelli, & Valeze, 1999). Fernández-Heredia et al. (2014) concluded that convenience, such as the availability of bike facilities, are more predictive of cyclist behavior than other variables. The National Survey of Bicyclist and

Pedestrian Attitudes and Behavior found that the majority of respondents who did not use bike paths (58 percent) or bike lanes (51 percent) cited lack of convenience as the primary reason. A significant number of respondents (20 percent) also cited safety as a reason for avoiding bike lanes (National Highway Traffic Safety Administration, 2008).

Although there is some disagreement, many studies have shown that cyclists of all confidence levels prefer Class I facilities (Schoner et al., 2014; Dill, 2009; Caulfield et al., 2012;

McClintock, 1996). For example, Wardman, Tight, & Page (2007) found that cyclists viewed the introduction of a segregated cycleway as a substantial benefit. In terms of route choice, Bernhoft and Carstensen (2008) found that 45 to 60 percent of their respondents consider the presence of an off-road bike path more important than time-

savings. Krizek (2006) substantiates these results, finding that cyclists are willing to spend 5.2 minutes of additional travel time to reach a bike path. However, Krizek also found that the presence of bike lanes increases cyclists’ acceptance of longer travel times by 16.3 minutes during a 20 minute trip, three times greater than the effect of off-road facilities. Tilahun et al.’s (2007) theory that connectivity is more important than safety could explain this finding.

Some question the safety value of separated facilities for cyclists (Bracher, 1991), but general public perception is that the further removed from motorized traffic, the better

(Tilahun et al., 2007). There is broad consensus that fear of accidents with motorized traffic is the primary deterrent for would-be cyclists and a concern for cyclists of all confidence levels (McClintock, 1996; Nuworsoo e al., 2014). The models estimated in this study that show cyclists’ facility preferences around driverless vehicles corroborate these findings.12

In the present study, gender, age, race, education, mode choice, and confidence level are used as explanatory variables. In light of the well-documented influence that motorized traffic speed and volume have on cyclists and pedestrians, these two factors are used as controlling variables to determine whether they have a stronger influence on cyclists’ and

12 However, in Chapter 6 I discuss how, in a world of complete motorized autonomy where all cars on the road require no human control, cyclists may actually find it more convenient to use Class II facilities, or even forego dedicated bike facilities altogether.

pedestrians’ facility preferences when driverless vehicles are present. Survey content and design are described in detail below.

Chapter 3: Methodology

The use of stated preference surveys is common in cyclist and pedestrian facility preference studies (Krizek, 2006; Mfinanga, 2014; Perdomo, Miranda-Moreno, Saunier,

Patterson, & Rezaei, 2014; Tilahun et al., 2007). I used the same format in this study for its proven reliability and for purposes of comparability.

Recruitment

A stated preference web-based survey13 was crafted and participants were recruited by a number of means. The primary method of recruitment targeted The Ohio State

University student, faculty and staff populations. Based on response rates from previous campus transportation surveys, I determined that sending the survey link to 13,600 OSU emails would ensure an adequate sample size at the 95 percent confidence level with a four percent margin of error. This method produced 767 responses, or 58 percent of total survey respondents. Table 3 presents the number of OSU respondents in detail.

13 A copy of the survey instrument is in Appendix A. 36

Table 3: OSU Sample Statistics:

Survey % of OSU Autumn 2014 Population+ Respondents Respondents Undergraduates 44,741 117 16 Graduates 10,389 52 7 Professional Students 3,192 N/A++ N/A Faculty and Staff 33,175 556 74 Total 91,497 725+++ +Source: Ohio State University, 2014 ++Graduate and Professional student responses were counted as one category in the survey +++Not all 767 OSU respondents revealed their status (student, staff, etc.)

The survey link was also posted on social media and news websites such as Facebook,

LinkedIn, Reddit, and Columbus Underground. Emails were sent out to City of

Columbus employees and Columbus Public Health Department clients, affiliates, and partners. Other organizations such as civic associations also assisted in distributing the survey.

The survey was active for one month, from October 13, 2014, to November 13, 2014.

Due to the sample’s heavy reliance on OSU respondents, I distributed the survey in the middle of autumn semester while classes were in session to ensure an adequate response rate.

Response Rate

2,343 surveys were started and 1,225 were finished, a 52 percent completion rate. Of the

2,343 respondents, 44 percent dropped out of the survey shortly after beginning, providing little or no usable data. The actual number of respondents is 1,312; the entire usable dataset therefore includes 1,312 partial responses. Only 16 percent of respondents completed the entire survey. In addition, the survey used skip logic to present bike- related questions only to those respondents who self-identified as cyclists14. For this reason, and because respondents were not required to answer every question, each question has a different sample size, ranging from 1,310 to 75015.

14 Respondents who answered “I never use a bicycle” in the first section skipped all subsequent bike-related questions. 15 Sample sizes for each survey question are listed in Appendix B.

Table 4: Descriptive Statistics

Population Population Gender N % Education N % US, % US, %

Below Female 489 59 51 bachelor's 238 23 71 degree Bachelor's Male 326 39 49 degree or 778 77 29 above Race Income Less than White 849 84 74 51 5% 7.2 $10,000 $10,000 - Other 150 15 27 46 5% 11 $19,999 $20,000 - Age 66 7% 15 $34,999 $35,000 - Under 30 307 30 ~40 113 12% 13 $49,999 $50,000 - 31-45 294 29 ~20 190 20% 18 $74,999 $75,000 - 16% 46-60 326 32 21 152 12 $99,999 Continued

Table 4 continued

$100,000- Over 60 89 16 19 185 19% 13 $149,999 Greater OSU affiliation than 152 16% 10 $150,000 Faculty/ 557 74 N/A Children Staff Student 169 22 N/A 0 0 75 N/A Alumnus 29 4 N/A 1 118 12 N/A

Marital Status 2 89 9 N/A

More Single 337 34 32 45 5 N/A than 2 Married 511 51 48 Mean 1.45 0.81 Unmarried 70 7 2.3 Living Situation partner Divorced 66 7 11 1, Just me 179 18 N/A Widowed 11 1 6 2 401 4 N/A More 423 41 N/A than 2 Mean 2.6 2.55

Descriptive Statistics

Table 4 presents descriptive statistics for the sample population. I compared the sample for this study against the 2013 American Community Survey (ACS) five year population estimates for the United States (US Census, n.d.).

 Gender: About 60 percent of respondents were female, as opposed to roughly 50

percent of the general population.

 Race: 84 percent of respondents were white, ten points higher than the general

population.

 Age: 30 percent of respondents were under the age of 30, compared to 40 percent

in the general population. 31-45 year olds and 46-60 year olds were both overly

represented in the sample by about ten percentage points; 60 year olds and over

were slightly underrepresented in the sample by ten points.

 Family Composition: Just over half of the respondents were married, roughly the

same number as the general population. Roughly one quarter of the sample

population had children living in their households, a slightly lower percentage

than the general population, at 30 percent. Less than one fifth of respondents

lived alone, as opposed to over one quarter in the general population.

 Education: Over three quarters of the sample population attained a Bachelor’s

degree or above, a much higher percentage than within the general population (18

percent). This discrepancy is likely due to the sample’s reliance on OSU

participants, who accounted for more than half of respondents.

 Income: Sample population income brackets were typically higher than that of the

general population, with the exception of the less than $10,000; $10,000 -

$19,000; $20,000 - $34,999; and $35,000 - $49,999 brackets, which were

underrepresented in the sample population.

Transportation Characteristics

The survey asked respondents to share information about their mode choice, general travel patterns, and experience levels as cyclists and pedestrians. Given the sample

demographics—mainly working professionals and students—I assumed that most trips are work- or school-related and therefore comparable to the ACS commuter data.

Approximately 80 percent of respondents use a car or other private vehicle as their primary mode of transportation for most trips. This number is slightly lower than the general population, of whom roughly 85 percent commute to work in a private vehicle

(US Census, 2013). At 7 percent, respondents were slightly more likely to walk as their primary mode choice than the general population, at 3 percent, and they were equally likely to use public transit. At 6 percent, a much higher percentage of respondents used a bicycle as their primary mode choice than the general population, at 0.06 percent.

Table 5: Transportation Characteristics

Mode Choice N % Car or other private vehicle 1,032 79 Bus or other public transit 55 4 Bicycle 80 6 Walk 88 7 Pedestrian Confidence strong pedestrian confidence 847 65 medium pedestrian confidence 371 28 weak pedestrian confidence 88 7 Cyclist Confidence strong bike confidence 201 15 medium bike confidence 330 25 weak bike confidence 387 30 I never use a bicycle 392 30

Survey Content

The survey asked respondents what bike facilities they prefer; what intersection features they prefer as cyclists; what pedestrian facilities they prefer; and how they normally cross the street as pedestrians. They were also asked the exact same questions for a hypothetical driverless vehicle environment. Respondents selected their preferred pedestrian or bike facility for three street types in each of these eight scenarios, for a total of 24 possible iterations: 12 for cyclists and 12 for pedestrians. Three street crossing methods and 17 facility options were available: six pedestrian facilities, six bike facilities, and five bike intersection facilities. Refer to Table 6 for a description of each choice.

Street types were described in terms of motorized vehicle speed, volume, and street width. Descriptions were created based on Congress of New Urbanism (CNU) roadway classifications that use terms such as “boulevard” and “avenue” instead of “arterial” or

“subcollector.” CNU terminology is more descriptive and accessible to the general public than conventional transportation planning roadway classifications (Institute of

Transportation Engineers, 1999). Street types were classified as follows:

 Street Type 1: A quiet, 2 lane residential street with slow traffic and few vehicles  Street Type 2: A moderately busy, 3 to 4 lane avenue with a mix of local and through traffic, and speeds under 35 miles per hour  Street Type 3: A major boulevard with more than 4 lanes and lots of traffic travelling over 35 miles per hour.

Table 6: Facility Choices and Behavior Types by Scenario

Scenario Facility Type No bike facilities, cyclists share the Wide shoulder, no Bike lane directly travel lane with bike facilities next to traffic vehicles Bike Facilities Buffered bike lane Cycle track or bike with barrier or Network of cycle path completely pavement markings tracks elevated above separated from between vehicles and the roadway* vehicular traffic cyclists Sidewalk with trees Sidewalk directly or other landscaping No sidewalks or adjacent to vehicles that buffers pedestrian facilities with no separation pedestrians from vehicles Pedestrian Facilities Sidewalk that is Network of separated from traffic Elevated network of pedestrian subways or by fencing or other sky walks with tunnels with complete impassable barriers, complete separation separation from except at designated from vehicles vehicles crossing locations Signalized Unsignalized Signalized intersection with intersection where intersection where designated bike bikes and vehicles use bikes and vehicles use through- and turn- the same lanes the same lanes lanes Bike Intersection Features Intersection equipped Intersection equipped with AIM technology with AIM technology that recognizes any user (cyclist, pedestrian, that only recognizes etc) with a smart phone or other GPS-enabled driverless vehicles* device*

Behavior Type Walk two or three Walk two or three Wait at the most blocks to a designated blocks to a designated Pedestrian Crossing convenient place to crossing facility, such crossing facility and Methods cross until there is no as a crosswalk or cross the street in traffic coming and pedestrian signal, and front of oncoming then cross the road wait for my turn to traffic cross

Chapter 4: Analysis

General Results

Given the primary interest in determining whether the presence of driverless vehicles influences facility preference and behavior, looking at how many respondents selected the same facility for both conditions16 gives us a good starting point (see Table 7). On average, for all street types, just over half of respondents preferred the same bike facility in both current and driverless conditions; 40 percent of respondents preferred the same intersection features in both conditions; three quarters of respondents preferred the same pedestrian facility in both conditions; and roughly 80 percent of respondents preferred the same crossing behavior in both conditions. For all questions except pedestrian crossing, the percent of respondents who selected the same answer in both current and driverless conditions decreased as motorized traffic volume, speed and street width increased.17

16 For the purposes of this analysis, condition refers to the driverless vehicle variable: current conditions or driverless vehicle conditions. Scenario refers to the facilities or behavior in question: bike facilities, bike intersection features, pedestrian facilities, and pedestrian crossing behavior. 17 Not all respondents opted to answer both condition questions in a given scenario. This analysis uses the subset of respondents who selected facilities for both conditions in a given scenario. 45

Table 7: Percent of Respondents with Same Answer for Both Conditions

Scenario Street Type % 1 (residential) 65 2 (subcollector/avenue) 54 Bike Facilities 3 (arterial/boulevard) 43 Average 54

1 53 2 34 Bike Intersection 3 29 Average 39

1 81 2 75 Pedestrian Facilities 3 72 Average 76

1 88 2 76 Pedestrian Crossing 3 84 Average 83

The remainder of the analysis focuses on bike facilities; bike intersection features, pedestrian facilities, and pedestrian crossing behavior are discussed in Appendix E.

Pearson’s Chi Squared tests the hypothesis that the rows and columns in a two-way table are independent. In this case, the rows are conditions (either current or driverless) and the columns are facility preferences. If pr < 0.05, results are significant at the 95 percent

level. Pr = 0 for all results listed below, meaning that the results for current and driverless conditions are not independent of one another. Chi Squared test results are presented in Table 8.

Table 8: Chi Squared Results

Condition Facility Cycle No Wide bike Buffered track/ Total facilities shoulder lane bike lane bike path Current 416 98 297 479 916 2,206 Driverless 318 103 339 438 1,004 2,202 Total 734 201 636 917 1,920 4,408

Pearson chi2(4) = 21.85 Pr = 0

I then looked at the change in responses within each condition by street type (Figure 5).

Although the primary interest was determining if there was any difference in preferences between conditions, examining changes in response by street type was instructive. It provided an overall picture of the results before looking at each facility individually.

Current Conditions Driverless Conditions

2.49% 3.41% 38.93% 77.03% 33.16% 14.17% Street Type 3 Street Type 3 15.73% 2.23% 5.50% 1.97% 1.57%

2.10% 1.70%

1.83% 2.62% 11.52% 29.88% 28.93% 39.71% Street Type 2 Street Type 2 29.45% 20.45% 19.76% 5.64% 5.50% 2.49% 2.23%

6.79% 5.87% 4.96% 13.19% 13.97% 8.88% Street Type1 Street Type1 12.14% 16.19% 19.06% 5.22% 6.40%

49.74% 37.60%

Figure 5: Change in Bike Facility Preference by Street Type 48

A clear trend emerges under current conditions: respondents prefer more protected facilities as motorized traffic speed, volume, and street width increase. No bike facilities was the most preferred choice for Street Type 1. For Street Type 2, buffered bike lane and cycle track or bike path were the most popular choices. Finally, for Street Type 3, cycle track or bike path was by far the most preferred facility.

Facility preferences under driverless conditions follow a similar trend but the change in preference between street types is less pronounced. A new facility choice was added under driverless conditions: network of elevated cycle tracks (shown in gray in Figure 5).

Due to the scarcity of such infrastructure today, this option was not included in current conditions18. No bike facilities is still the most preferred choice for Street Type 1 under driverless conditions. For Street Type 2, the moderately protected facilities including both bike lane types and cycle track or bike path are the preferred choices. Elevated cycle track and at-grade cycle track or bike path are the most popular choices for Street

Type 3.

From these findings, it is clear that the preference for separated bike facilities grows with increases in motorized traffic speed, volume, and street width. Preference for protected, horizontally separated facilities increases with each Street Type in current conditions, and preferences for protected, horizontally and vertically separated facilities increases with

18 Rare and new forms of bike infrastructure are discussed in Chapter 5. 49

each Street Type in driverless vehicle conditions. General analysis concludes with an examination of the changes in preference between conditions for each individual facility.

1. No Bike Facilities 49.74% 37.60%

2.49% 2.23% 2.10% 1.70%