No abstract

<div class="section abstract"><div class="htmlview paragraph">The driving safety performance of automated driving system (ADS)-equipped vehicles (AVs) must be quantified using metrics in order to be able to assess the driving safety performance and compare it to that of human-driven vehicles. In this research, driving safety performance metrics and methods for the measurement and analysis of said metrics are defined and/or developed.</div><div class="htmlview paragraph">A comprehensive literature review of metrics that have been proposed for measuring the driving safety performance of both human-driven vehicles and AVs was conducted. A list of proposed metrics, including novel contributions to the literature, that collectively, quantitatively describe the driving safety performance of an AV was then compiled, including proximal surrogate indicators, driving behaviors, and rules-of-the-road violations. These metrics, which include metrics from on- and off-board data sources, allow the driving safety performance of an AV to be measured in a variety of situations, including crashes, potential conflicts, and near misses. These measurements enable the evaluation of temporal flows and the quantification of key aspects of driving safety performance. The identification and exploration of metrics focusing explicitly on AVs as well as proposing a comprehensive set of metrics is a unique contribution to the literature. The objective is to develop a concise set of metrics that allow driving safety performance assessments to be effectively made and that align with the needs of both the ADS development and transportation engineering communities and accommodate differences in cultural/regional norms.</div><div class="htmlview paragraph">Concurrent project work includes equipping an intersection with a sensor suite of cameras, LIDAR, and RADAR to collect data requiring off-board sources and employing test AVs to collect data requiring on-board sources. Additional concurrent work includes development of artificial intelligence and computer vision-based algorithms to automatically calculate the metrics using the collected data. Future work includes using the collected data and algorithms to finalize the list of metrics and then develop a methodology that uses the metrics to provide an overall driving safety performance assessment score for an AV.</div></div>

In the past two decades, cell phone and smartphone use in the United States has increased substantially. Although mobile phones provide a convenient way for people to communicate, the distraction caused by the use of these devices has led to unintended traffic safety and operational consequences. Although it is well recognized that distracted driving is extremely dangerous for all road users (including pedestrians), the potential impacts of distracted walking have not been as comprehensively studied. Although practitioners should design facilities with the safety, efficiency, and comfort of pedestrians in mind, it is still important to investigate certain pedestrian behaviors at existing facilities to minimize the risk of pedestrian–vehicle crashes, and to reduce behaviors that may unnecessarily increase delay at signalized intersections. To gain new insights into factors associated with distracted walking, pedestrian violations, and walking speed, 3,038 pedestrians were observed across four signalized intersections in New York and Arizona using high-definition video cameras. The video data were reduced and summarized, and an ordinary least squares (OLS) regression model was estimated to analyze factors affecting walking speeds. In addition, binary logit models were estimated to analyze both pedestrian distraction and pedestrian violations. Ultimately, several site- and pedestrian-specific variables were found to be significantly associated with pedestrian distraction, violation behavior, and walking speeds. The results provide important information for researchers, practitioners, and legislators, and may be useful in planning strategies to reduce or mitigate the impacts of pedestrian behavior that may be considered unsafe or potentially inefficient.

No abstract

No abstract