Fatal pedestrian crashes at intersections: Trend mining using association rules.

In 2018, about 6,677 pedestrians were killed on the US roadways. Around one-fourth of these crashes happened at intersections or near intersection locations. This high death toll requires careful investigation. The purpose of this study is to provide an overview of the characteristics and associated crash scenarios resulting in fatal pedestrian crashes in the US. The current study collected five years (2014-2018) of fatal crash data with additional details of pedestrian crash typing. This dataset provides specifics of scenarios associated with fatal pedestrian crashes. This study applied associated rules mining on four sub-groups, which were determined based on the highest frequencies of fatal crash scenarios. This study also developed the top 20 rules for all four sub-groups and used 'a priori' algorithm with 'lift' as a performance measure. Some of the key variable categories such as dark with lighting condition, vehicle going straight, vehicle turning, local municipality streets, pedestrian age range from 45 years and above are frequently presented in the developed rules. The patterns of the rules differ by the pedestrian's position within and outside of crosswalk area. If the pedestrian is outside the crosswalk area, no lighting at dark is associated with high number of crashes. As lift provides quantitative measures in the form of the likelihood, the rules can be transferred into data-driven decision making. The findings of the current study can be used by safety engineers and planners to improve pedestrian safety at intersections.

Temporal instability assessment of injury severities of motor vehicle drivers at give-way controlled unsignalized intersections: A random parameters approach with heterogeneity in means and variances.

Unsignalized intersections are highly susceptible to traffic crashes compared to signalized ones. By taking into account temporal stability and unobserved heterogeneity, this study investigates the determinants of the injury severity of drivers involved in crashes at unsignalized intersections controlled by give-way (yield) signs. Mixed logit models with three approaches were employed, namely random parameters, random parameters with heterogeneity in means, and random parameters with heterogeneity in means and variances. The investigation covered four years (2015-2018) of motor vehicle crashes in South Australia, and the injury severity was categorized into no injury, minor injury, and severe injury. Log-likelihood ratio tests revealed that there is a significant temporal instability in the four years of crashes. Thus, each year was considered separately to avoid any potential erroneous conclusions and unreliable countermeasures. The study found 28 indicator variables were temporally unstable over the four years of crashes, such as driver gender, time of the crash, rear-end involvement, sideswipes, right-angle crash type, vehicle movement at crash time, and crash time. Whereas several variables were stable over the same period, for example, crashes within metropolitan areas were temporally stable over four years, crashes in dry pavement condition were temporally stable over three consecutive years. Four factors have temporal stability over two consecutive years: alcohol involvement crashes, hitting fixed objects, hitting cyclists, and crashes during winter. Overall, mixed logit models with heterogeneity in means and with/without variance performed better than the standard one. It is recommended that temporal instability be considered in order to avoid any potential inconsistent countermeasures.