Comfortable walking experience today and tomorrow : investigating pedestrian interactions with bicycles, cars, and self-driving vehicles

Cities are developing facilities for walking and cycling to improve safety and meet mode share goals for active transportation. People’s perceptions of safety and comfort (PSC) on those facilities mediate the influence on travel behaviour. For pedestrians, a critical component of PSC is interactions with vehicles at crosswalks. Today, pedestrians mainly interact with bicycles and cars, but they will also need to interact with self-driving vehicles (SDVs) in the future. A key gap in understanding of pedestrian PSC is how the type of interacting road user (bicycle vs. car vs. SDV) influences PSC. The goals of this dissertation are to improve understanding of PSC in intermodal interactions for a diverse and representative array of people, and to apply that knowledge to inform policy for responsible introduction of SDVs in a way that maintains walkable streets. I develop statistical models using data from two online surveys in which participants rate safety and comfort for sample videos of pedestrian interactions with bicycles, cars, and SDVs in unsignalized crosswalks. Results show that perception of yielding plays a crucial role in directly and indirectly influencing PSC, and yielding is most strongly (but not exclusively) determined by whether the pedestrian crossed before or after the interacting road user. All else equal, people perceive pedestrian interactions with SDVs as less safe and less comfortable than similar interactions with human-driven cars, which in turn are perceived as less safe and less comfortable than similar interactions with bicycles. To ensure comfort for a majority of pedestrians, SDVs must provide crossing pedestrians with more passing time than cars, and car drivers must yield more than bicycles. In terms of attitudes towards SDVs, British Columbians are almost evenly split on policies allowing SDVs, but a large majority want restrictions on SDVs on operating near pedestrian priority areas, without a human in the driver seat, or without clear identification. To ensure pedestrian-friendly introduction of SDVs, especially for older individuals, people of colour, and women, I recommend a cautious, tiered approach beginning with restricted pilot testing to allow road users to develop familiarity with SDV interactions.Applied Science, Faculty ofCivil Engineering, Department ofGraduat

Evaluation Of The Impact Of Traffic Volume On Site Ranking

This study aims to compare and quantify the impact of traffic volume on hotspot identification. The data consist of geometric and traffic features of freeways of California for the six-year period (2005–2010). Five functional roadway classifications were used for analyzing the role of traffic volume in different environments. Four hotspot identification (HSID) methods were selected for this study, namely, Empirical Bayesian count with volume (EBWT), Empirical Bayesian count without volume (EBWOT), crash rate (CR), and crash number (CF). To determine the superiority of the above methods, four evaluation tests were conducted which include Site Consistency Test (SCT), Method Consistency Test (MCT), Total Rank Difference Test (TRDT), and Total Performance Difference Test (TPDT). The safety performance functions (SPF) that include the traffic volume show a better fit to crash count than do the ones without traffic volume. The results also show that EBWT mostly comes out to be the superior method as indicated by the four tests, followed by EBWOT, CF, and the worst performer CR. The advantages associated with the inclusion of traffic volume in SPFs are also transferred to the HSID with EBWT showing the best performance in most cases

Investigation of hit-and-run crash occurrence and severity using real-time loop detector data and hierarchical Bayesian binary logit model with random effects

<p><b>Objective</b>: Most of the extensive research dedicated to identifying the influential factors of hit-and-run (HR) crashes has utilized typical maximum likelihood estimation binary logit models, and none have employed real-time traffic data. To fill this gap, this study focused on investigating factors contributing to HR crashes, as well as the severity levels of HR.</p> <p><b>Methods</b>: This study analyzed 4-year crash and real-time loop detector data by employing hierarchical Bayesian models with random effects within a sequential logit structure. In addition to evaluation of the impact of random effects on model fitness and complexity, the prediction capability of the models was examined. Stepwise incremental sensitivity and specificity were calculated and receiver operating characteristic (ROC) curves were utilized to graphically illustrate the predictive performance of the model.</p> <p><b>Results</b>: Among the real-time flow variables, the average occupancy and speed from the upstream detector were observed to be positively correlated with HR crash possibility. The average upstream speed and speed difference between upstream and downstream speeds were correlated with the occurrence of severe HR crashes. In addition to real-time factors, other variables found influential for HR and severe HR crashes were length of segment, adverse weather conditions, dark lighting conditions with malfunctioning street lights, driving under the influence of alcohol, width of inner shoulder, and nighttime.</p> <p><b>Conclusions</b>: This study suggests the potential traffic conditions of HR and severe HR occurrence, which refer to relatively congested upstream traffic conditions with high upstream speed and significant speed deviations on long segments. The above findings suggest that traffic enforcement should be directed toward mitigating risky driving under the aforementioned traffic conditions. Moreover, enforcement agencies may employ alcohol checkpoints to counter driving under the influence (DUI) at night. With regard to engineering improvements, wider inner shoulders may be constructed to potentially reduce HR cases and street lights should be installed and maintained in working condition to make roads less prone to such crashes.</p

Evaluation of the impact of traffic volume on site ranking

This study aims to compare and quantify the impact of traffic volume on hotspot identification. The data consist of geometric and traffic features of freeways of California for the six-year period (2005–2010). Five functional roadway classifications were used for analyzing the role of traffic volume in different environments. Four hotspot identification (HSID) methods were selected for this study, namely, Empirical Bayesian count with volume (EBWT), Empirical Bayesian count without volume (EBWOT), crash rate (CR), and crash number (CF). To determine the superiority of the above methods, four evaluation tests were conducted which include Site Consistency Test (SCT), Method Consistency Test (MCT), Total Rank Difference Test (TRDT), and Total Performance Difference Test (TPDT). The safety performance functions (SPF) that include the traffic volume show a better fit to crash count than do the ones without traffic volume. The results also show that EBWT mostly comes out to be the superior method as indicated by the four tests, followed by EBWOT, CF, and the worst performer CR. The advantages associated with the inclusion of traffic volume in SPFs are also transferred to the HSID with EBWT showing the best performance in most cases