Pedestrian Evacuation: Vulnerable Group Member Influence on the Group Leaders’ Decision-Making and the Impact on Evacuation Time

As pedestrian evacuations of buildings, outdoor venues, and special events occur, dynamic interactions between pedestrians and vehicles during egress are possible. To model pedestrian and vehicle evacuations, simulation models have evolved to incorporate more realistic crowd characteristics and behaviors to provide improved results. Past studies using modeling and simulation, specifically agent-based modeling, have explored pedestrian behaviors such as decision-making, navigation within a virtual environment, group formations, intra-group interactions, inter-group dynamics, crowd behaviors such as queuing and herding, and pedestrianvehicle interactions. These studies have led to relevant insights helpful to improving the accuracy of evacuation times for normal and emergency egress for preparedness and management purposes. As evacuating crowds are composed of individual pedestrians and social or familial groups, this project contributes to the study of pedestrian evacuation by exploring the incorporation of a subgroup not often considered in this area. Vulnerable individuals, such as the physically disabled, elderly, and children, can change the decision-making dynamic of a group leader while evacuating to safety. Current agent-based simulation models explore the intra- and inter- action and the effects on evacuation times; however, the vulnerable group members\u27 influence is neglected. This project presents enhancements to pedestrian evacuations with vehicle interaction using an agent-based simulation model that includes the presence of vulnerable group members and their impact on decision-making and evacuation times. This project explores how changing behaviors due to the presence of vulnerable group members can collectively cause delays and increase evacuation times. Utilizing verification and validation methods, the credibility and reliability of the simulation model and its results are increased. The results show that the group leaders\u27 decision-making differs when leading a vulnerable group versus a non-vulnerable group. Also, evacuation times increase with increased percentages of vulnerable groups within an evacuating crowd. A simulation tool can be utilized by end-users to explore specific evacuation scenarios in preparation for upcoming events and glean insight into how evacuation times may vary with differing crowd population sizes and compositions. Including vulnerable pedestrians in simulation models for evacuations would improve output accuracy and ultimately improve event training and preparation for future evacuations

Pedestrians\u27 Receptivity Toward Fully Autonomous Vehicles

Fully Autonomous Vehicles (FAVs) have the potential to provide safer vehicle operation and to enhance the overall transportation system. However, drivers and vehicles are not the only components that need to be considered. Research has shown that pedestrians are among the most unpredictable and vulnerable road users. To achieve full and successful implementation of FAVs, it is essential to understand pedestrian acceptance and intended behavior regarding FAVs. Three studies were developed to address this need: (1) development of a standardized framework to investigate pedestrians’ behaviors for the U.S. population; (2) development of a framework to evaluate their receptivity of FAVs; and (3) investigation of the influence of the external interacting interfaces of FAVs on pedestrian receptivity toward them. The pedestrian behavior questionnaire (PBQ) categorized pedestrian general behaviors into five factors: violations, errors, lapses, aggressive behaviors, and positive behaviors. The first four factors were found to be both valid and reliable; the positive behavior scale was not found to be reliable nor valid. A long (36-item) and a short (20-items) versions of the PBQ were validated by regressing scenario-based survey responses to the fiveactor PBQ subscale scores. The pedestrian receptivity questionnaire for FAVs (PRQF) consisted of three subscales: safety, interaction, and compatibility. This factor structure was verified by a confirmatory factor analysis and the reliability of each subscale was confirmed. Regression analyses showed that pedestrians’ intention to cross the road in front of a FAV was significantly predicted by both safety and interaction scores, but not by the compatibility score. On the other hand, acceptance of FAVs in the existing traffic system was predicted by all three subscale scores. Finally, an experimental study was performed to expose pedestrians to a simulated environment where they could experience a FAV. The FAV in the simulated environment was either equipped with external features (audible and/or visual) or had no external (warning) feature. The least preferred options were the FAVs with no features and those with a smiley face but no audible cue. The most preferred interface option, which instilled confidence for crossing in front of the FAV, was the walking silhouette

Walking, Crossing Streets and Choosing Pedestrian Routes: A Survey of Recent Insights from the Social/Behavioral Sciences

Walking at first appears to be a relatively simple, mundane behavior that should pose no great puzzle for the diligent researcher in the social and behavioral sciences. The review presented here of recent studies, however, demonstrates that the behavior and experiences of ordinary pedestrians are filled with opportunities for empirical investigation and intricate theory building. But, why bring these studies together for synthesis in this volume? I suggest here that there are, in fact, several reasons that argue in favor of a timely focus on the apparently simple behavior of the pedestrian. First, the deceptive simplicity of the pedestrian experience provides an excellent empirical focus for examination of a wide range of topics prominent in recent work in the emerging field of human-environment studies. Readers unfamiliar with the scope and intensity of research in this interdisciplinary enterprise would do well to consult the pages of Environment and Behavior; Man Environment Systems; Environment and Planning; the annual proceedings of the Environmental Design Research Association (EDRA); and the topical volumes in the new review series entitled Human Behavior and Environment: Advances in Theory and Research, edited by Irwin Altman, Amos Rapoport, and Joachim Wohlwill. Even summary consideration of the many topics that have become the focus of considerable investigation in the last decade reveals that empirical and conceptual work regarding territoriality, crowding, privacy, personal space, sensory overload and deprivation, approach-avoidance, navigation and orientation, mental mapping, search processes, and environmental perception, evaluation, and decision making all bear on various facets of the pedestrian experience. Empirical verification of the viability of these conceptual ideas reveals a void which the study of the pedestrian helps to fill. The inner processes and complexity of pedestrian behavior are far greater, for example, than the outward simplicity suggested by the simple geometrical representation of a pedestrian trip as a line connecting an origin and a destination. The complexity that lies behind this apparent simplicity provides a major challenge for the students of human-environment relations

An Examination of Drivers’ Responses to Take-over Requests with Different Warning Systems During Conditional Automated Driving

Today, the autonomous vehicle industry is growing at a fast pace towards Level-5 autonomous cars, based on the Society of Automotive Engineers (SAE) definition, for customers. It is expected that there will soon be SAELevel-3 automated cars in the market–which corresponds to a plethora of research works in this sector and one of them is the study of the design of takeover request warning system because failure to respond a takeover request warning may lead to fatal accidents. The objective of this study is to examine the effects of different warning types on drivers’ takeover responses while they are engaging in different non-driving tasks during conditional automated driving. This study is a simulator-based with a mixed-subjects design while participants interacting with a simulated Level-3 automation system under different conditions. A total of 24 participants were recruited and participated in the study. Each participant experienced two types of takeover request (TOR )warning systems (Auditory TOR and Multimodal TOR) under four types of non-driving task conditions with two levels of non-driving task duration. One baseline drive without any secondary task was also designed for comparison with those conditions with non-driving tasks. Three research questions are addressed in this thesis: •Will a Multimodal TOR lead to better driver responses in reaction to takeover requests than Auditory TOR? •Will the different type of non-driving tasks lead to different cognitive engagement of drivers, therefore resulting in different reactions to takeover requests? Will different duration of engagement in non-driving tasks impact on responses of drivers’ re-engagement in driving tasks? In this study, data was collected for both objective driver measures through simulator run log files and subjective driver measures through questionnaires. For analysis purposes, a Mixed-Effects Model was conducted to test the response variables, followed by the Fisher LSD Pairwise Comparison test for significant factors with more than two levels and Two-Sample t-tests for subjective measures were used. Results showed that Multimodal TOR leads to shorter brake time and steer touching time comparatively and the difference of these dependent variables between the TORs is significant as p-value<0.05. The findings also suggest that the Multimodal TOR warning system leads to a better reaction of drivers. Moreover, it was also found that the type of non-driving tasks leads to different driver responses, more specifically, drivers have a significantly slower reaction towards the takeover request if they are engaging in visual-manual non-driving tasks when compared to if they are engaging in other types of non-driving tasks (e.g., cognitive or visual tasks). However, there are no significant gender-based effects observed for Brake Time and Steer Touch Time.Master of Science in EngineeringIndustrial and Systems Engineering, College of Engineering & Computer ScienceUniversity of Michigan-Dearbornhttps://deepblue.lib.umich.edu/bitstream/2027.42/152430/1/Kanishk Bakshi Final Thesis.pdfDescription of Kanishk Bakshi Final Thesis.pdf : Thesi

Affective driver-pedestrian interaction: Exploring driver affective responses toward pedestrian crossing actions using camera and physiological sensors

Eliciting and capturing drivers' affective responses in a realistic outdoor setting with pedestrians poses a challenge when designing in-vehicle, empathic interfaces. To address this, we designed a controlled, outdoor car driving circuit where drivers (N=27) drove and encountered pedestrian confederates who performed non-verbal positive or non-positive road crossing actions towards them. Our findings reveal that drivers reported higher valence upon observing positive, non-verbal crossing actions, and higher arousal upon observing non-positive crossing actions. Drivers' heart signals (BVP, IBI and BPM), skin conductance and facial expressions (brow lowering, eyelid tightening, nose wrinkling, and lip stretching) all varied significantly when observing positive and non-positive actions. Our car driving study, by drawing on realistic driving conditions, further contributes to the development of in-vehicle empathic interfaces that leverage behavioural and physiological sensing. Through automatic inference of driver affect resulting from pedestrian actions, our work can enable novel empathic interfaces for supporting driver emotion self-regulation

From Video to Hybrid Simulator:Exploring Affective Responses toward Non-Verbal Pedestrian Crossing Actions Using Camera and Physiological Sensors

Capturing drivers’ affective responses given driving context and driver-pedestrian interactions remains a challenge for designing in-vehicle, empathic interfaces. To address this, we conducted two lab-based studies using camera and physiological sensors. Our first study collected participants’ (N = 21) emotion self-reports and physiological signals (including facial temperatures) toward non-verbal, pedestrian crossing videos from the Joint Attention for Autonomous Driving dataset. Our second study increased realism by employing a hybrid driving simulator setup to capture participants’ affective responses (N = 24) toward enacted, non-verbal pedestrian crossing actions. Key findings showed: (a) non-positive actions in videos elicited higher arousal ratings, whereas different in-video pedestrian crossing actions significantly influenced participants’ physiological signals. (b) Non-verbal pedestrian interactions in the hybrid simulator setup significantly influenced participants’ facial expressions, but not their physiological signals. We contribute to the development of in-vehicle empathic interfaces that draw on behavioral and physiological sensing to in-situ infer driver affective responses during non-verbal pedestrian interactions

Modeling Driver-Pedestrian-Infrastructure Interactions at Signalized Midblock Crosswalks

Cities and metropolitan areas are increasingly facilitating pedestrians’ movement by the provision of pedestrian walking facilities. As pedestrian traffic increases, the risk of crash involvement increases, especially at midblock locations, where pedestrians are exposed to unsafe interactions with vehicular traffic. To improve pedestrians’ safety at midblock locations, various countermeasures are provided, which include signalized crosswalks. Several studies have analyzed driver-pedestrian interactions, as well as pedestrian-infrastructure interactions at signalized midblock crosswalks. However, more in-depth studies are necessary, due to shortfalls of study assumptions, which have led to the application of improper statistical models, as seen in the literature. Improved models are crucial, as they can be used to evaluate the factors affecting the effectiveness of countermeasures at signalized midblock crosswalks. Moreover, there are several aspects of pedestrian-infrastructure interactions that have not been studied in the previous research. This study, therefore, attempts to improve the methodologies for analyzing driver-pedestrian-infrastructure interactions at signalized midblock crosswalks. Specifically, this study is aimed towards: • Developing improved modeling methodology for the yielding compliance of drivers at signalized midblock crosswalks, which considers the time taken to yield right of way, and the transition states undergone during yielding. • Analyzing the risks associated with driver-pedestrian interactions at signalized midblock crosswalks. • Developing the framework for modeling the spatial and temporal crossing compliance of pedestrians at signalized midblock crosswalks. • Evaluating the influence of various crosswalk features, such as signs and markings, traffic-related variables, and pedestrian related factors on the safe utilization of signalized midblock crosswalks; these include factors influencing drivers’ yielding compliance, pedestrians’ crossing compliance, and pedestrians’ utilization of pushbuttons. The study data were collected from a total of twenty signalized midblock crosswalks located in the Las Vegas, Nevada metropolitan area. These crosswalks have varying geometric configurations, signalizations, traffic characteristics, and pedestrian flows. Five types of signalization; Circular Flashing Beacons (CFBs), Circular Rapid Flashing Beacons (CRFBs), Rectangular Rapid Flashing Beacons (RRFBs), Pedestrian Hybrid Beacons (PHBs), and Traffic Control Signals (TCSs) were studied in this research. The observational survey method was applied for data collection, whereby video cameras were used to collect driver-pedestrian interactions. The data extraction was performed by reviewing the videos and recording the information of interest in a spreadsheet, with a total of 2638 pedestrians crossing incidents recorded for analysis. A descriptive analysis was performed, and several statistical models were developed. Multistate hazard-based models are developed to model the yielding compliance of drivers. The transitional states while drivers are yielding right of way to pedestrians are defined as non-yield, “partial-yield” events (partial-yield, scenarios in which driver(s) in one lane yield, while other driver(s) in adjacent lane(s) in the same direction do not), and full-yield. Binary-based models are developed for modeling drivers’ spatial yielding compliance, pedestrians’ spatial crossing compliance, and pedestrians’ temporal crossing compliance. Rare Events Logistic Regression (RELR) is applied to evaluate the occurrence of partial-yield events and near-miss events. In addition to binary models, ordered models and multinomial models are developed and compared to model pedestrians’ spatiotemporal crossing compliance. The results of the multistate models reveal that signal type, number of vehicles within effective crosswalk distance, yield-here sign, and crossing zone factors have similar influence for transition from non-yield to full-yield, non-yield to partial yield, and partial yield to full yield. Thus, the results of the binary models for yielding compliance are only partially comparable to one transition of the multistate model (non-yield to full yield). Through the Rare Event Logistic Regression (RELR) model, this study finds that near crash events are highly associated with a single cross stage, a high number of lanes, and night time. In addition, this study reveals that there is a strong association between partial-yield and near-miss events. Additionally, it is found that for every second that traffic continues to flow while pedestrians are waiting to cross, the probability of a partial-yield event occurring increases by 2.1%, while that of near-crash events increase by about 3%. Moreover, the influence of the crosswalk features and the distance at which drivers yield with respect to the yield line (spatial yielding) was assessed. The logistic regression results for associating drivers’ spatial yielding results shows that the odds for drivers’ spatial yielding are high if the crosswalks are equipped with Rectangular Rapid Flashing Beacons (RRFBs) at the advanced pedestrians crossing signs (APCSs), in the presence of “State Law” and “PED XING” signs. On the other hand, long distances from stripes to the yield lines, multiple cross stages, and high Annual Average Daily Traffic (AADT) are associated with decreased spatial yielding compliance. Regarding pedestrian-infrastructure interactions, the logistic regression results reveal that the arrival sequence to a crosswalk has the highest impact on warning light activation tendencies. This means that the first arriving pedestrians are eight times more likely to press pushbuttons. Moreover, males, the elderly, children, and teens are less likely to press pushbuttons. Furthermore, pedestrians who are involved in secondary activities, such as carrying/holding objects in their hands, have a relatively low odds ratio of pressing the pushbutton, while phone use is a statistically insignificant factor. Several infrastructure and traffic factors, including flash-based signal types (CRFBs, CFBs and RRFBs), a high number of lanes, residential land use, and higher oncoming vehicle speeds are associated with an increase of pushbutton pressing. Among the models applied for spatiotemporal crossing compliance, the logistic regression outperformed the multinomial logit and the ordered logit models. The logistic regression results reveal that the active WALK signal and a crossing incident involving female(s) only are the factors positively associated with pedestrians’ spatiotemporal crossing compliance. On the other hand, wait time, children, and teens, as well as people who cross while using a phone or riding a bike are negatively associated with spatiotemporal crossing compliance. Based on the study’s findings, several recommendations are provided. The findings and recommendations from this study are expected to have academic, industry, and community benefits. Planners and engineers can benefit from this study by learning which countermeasures improve safety for both pedestrians and drivers. The models can be used by academicians and other practitioners to assess the scenarios in question. Improved pedestrian safety due to the selection of appropriate countermeasures, which fit a particular location, is a benefit that directly impacts the community

Human Behavior Modeling and Human Behavior-aware Control of Automated Vehicles for Trustworthy Navigation

First and foremost, I would like to thank my advisor, Professor Dawn Tilbury, for her constant guidance and encouragement. She has been extremely helpful in developing my technical, research, and personal skills and immensely supportive of my ideas and endeavors throughout graduate school. She has been an excellent mentor and has always been there in my time of need, encouraging and boosting my confidence when I needed them the most. I would like to specially thank my committee members and collaborators, Professors Lionel Robert and Jessie Yang, for their support and encouragement, right from the start of my graduate program. The multi-disciplinary nature of the research initiated by these three Professors is what first drew me towards pursuing a Ph.D. I would also like to thank my other committee members Professors Ilya Kolmanovsky and Ram Vasudevan, for providing their support and feedback that improved the dissertation. I would like to thank the Department of Mechanical Engineering, Rackham Graduate School, and the University of Michigan for giving me the opportunity to pursue the doctoral degree and providing financial support during my time at the university. In addition, I would like to thank the Toyota Research Institute and the Automotive Research Center for providing financial assistance. I really appreciate the support I received from the MAVRIC lab members. The multi-disciplinary culture and environment that the Professors have fostered in the MAVRIC lab have deeply broadened my perspectives. Specically, I would like to thank Hebert Azevedo-Sa. He is usually the first person I discuss my ideas with and has been an excellent critique. I would also like to thank Connor Esterwood, Na Du, Qiaoning Zhang, and Huajing Zhao for the numerous discussions and help with my user studies; especially Connor, who took on a variety of roles to help with my user study|from an engineer to a tailor, to even a hidden driver. Outside of the University of Michigan, I would like to thank my undergraduate advisor, Professor Madhu M., and my internship advisor at the Indian Institute of Technology-Madras, Professor Saravanan Gurunathan. They encouraged me to pursue research and provided me with the necessary opportunities. A special thanks to Sajaysurya Ganesh, a close friend, and collaborator in my early research projects, with who I discuss ideas even now. Last but not least, I would like to thank my family and friends for supporting me during the past several years. My friends at Ann Arbor made life away from home much easier; they are like my second family. A long list of people from my Master's and Ph.D. programs at the University of Michigan has played an essential role in my graduate experience. Still, I would like to especially thank Sandipp Krishnan Ravi, Subramaniam Balakrishna, Rahasudha Kannan, and Paavai Pari for all their love and support. I will fondly remember my time at the University of Michigan and in Ann Arbor because of all of the people I encountered, the friends I made, and the experiences I had. My parents, wife, and extended family have all been incredibly supportive of the pursuit of my degree, and I am eternally grateful for their love and guidance.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169640/1/jskumaar_1.pd