Improving driver yielding to pedestrians at crosswalks may help prevent pedestrian fatalities, which have increased over the last decade in the United States. The level of assertiveness exhibited by pedestrians when they arrive at a crosswalk may have a significant impact on driver yielding behavior, but assertiveness is not defined clearly or studied thoroughly in the literature. This study defined three levels of pedestrian assertiveness and collected naturalistic video data at two uncontrolled crosswalks in Madison and Milwaukee, Wisconsin to explore the relationship between pedestrian assertiveness and driver yielding behavior. Driver yielding rates were 71% for pedestrians exhibiting Level 1 (high), 30% for Level 2 (moderate), and 3% for Level 3 (low) assertiveness. The pedestrian assertiveness definitions were also used to assess the potential impact of a high-visibility enforcement (HVE) program in the communities where the study took place. Observations taken after the HVE program showed a significantly higher rate of driver yielding to pedestrians exhibiting a moderate level of assertiveness. This result is promising, since a moderate level of assertiveness may be reasonable for pedestrians to adopt, especially if supported by educational messages for pedestrians to clearly indicate their intent to cross within a crosswalk. This exploratory study provides a framework for future analysis and highlights the need for additional research on the relationship between pedestrian assertiveness and driver yielding behavior.

Emerging data sources such as Safety Pilot Model Deployment (SPMD) provide a great opportunity to gain a better understanding of collision mechanisms and to develop novel safety metrics. The SPMD program was a comprehensive data collection effort under real-world conditions in Ann Arbor, Michigan, covering over 73 lane-miles and including approximately 3,000 pieces of onboard vehicle equipment and 30 pieces of roadside equipment. In-vehicle data (e.g., speed, location) collected by the SPMD program can potentially be an important supplement to traditional crash data-oriented safety analysis. The goal of this study was to assess roadway link-level surrogate safety measures using the vehicle trajectory data from SPMD. The study’s objectives included: 1) developing a framework to process the SPMD dataset using big-data analytics; 2) converting raw vehicle motion data from SPMD to surrogate safety measures; and 3) analyzing the statistical relationship between crash records and the calculated safety index. The statistical models showed that modified time to collision (MTTC) outperforms time to collision (TTC) and deceleration rate to avoid collision (DRAC) with respect to its goodness of fit. The findings are promising in that augmenting safety analysis with surrogate measures and vehicle performance (e.g., speed and brake duration from connected vehicles) improves the overall model performance. Such information is vital for safety analysis, especially in the absence of detailed roadway and traffic data.

One of the most common circumstances contributing to pedestrian crashes is drivers failing to yield to pedestrians in crosswalks. A better understanding of driver yielding behavior can help identify optimal safety treatments to improve driver yielding and prevent pedestrian injuries and fatalities. Recognizing this need, this study observed driver yielding behavior at 20 uncontrolled intersections along two-lane arterial and collector roadways with posted speed limits of 25 or 30 miles per hour in Milwaukee, Wisconsin during weekday afternoon peak travel periods in fall 2016. The naturalistic observations showed that drivers yielded 60 times out of 364 opportunities when the pedestrian wished to cross (16% driver yielding rate). Yielding rates differed between intersections, ranging from a high of 60% to a low of 0%. A binary logistic model showed that drivers were more likely to yield to pedestrians when the major roadway had a lower speed limit or less traffic; when the intersection had a shorter crossing distance or a bus stop; and when the pedestrian was White, standing in the street, or acting assertively. Finally, all else equal, intersections with no reported pedestrian crashes in the last 5 years had higher driver yielding rates than intersections with at least two reported pedestrian crashes. While this exploratory study is based on a small sample of observations, it supports several engineering, education, and enforcement strategies and provides suggestions for future studies of driver yielding behavior.

No abstract

Unintentional mortality rates attributed to disease, fertility, and motor vehicle crashes are higher in rural areas than in urban areas because of the more limited nature of emergency medical services (EMS), hospitals, and the highway network connecting them. For rural states with long travel distances that result from the sparsely distributed population, it is important to gain a reliable assessment of EMS demand and an unbiased evaluation of service performance within the current highway system. The goal of this research was to conduct a needs assessment for rural EMS and identify issues related to the delivery of quality services. The data set was from the National EMS Information System and consisted of 50,396 EMS responses in 2012 in South Dakota. Spatial analysis focused on the visual presentation and cluster analysis of service demand and performance on a county level. Temporal analysis was performed to magnify the service demand by month, day of week, and time of day. Descriptive statistics and two-tailed t-tests were applied to describe and compare the variables of interest. The findings not only offered a comprehensive view of EMS from geographic and temporal perspectives but also stressed key time-and distance-dependent factors, such as response time, en route time, on-scene time, and transporting time. The authors call for continued efforts to improve EMS data quality and recommend linkage between EMS data and crash outcomes to establish specific, data-driven, and performance-based measures.