Pedestrian crash causation analysis near bus stops: Insights from random parameters Negative Binomial–Lindley model
本研究利用随机参数负二项-林德利模型分析美国德克萨斯州沃斯堡596个公交车站的数据，发现高客流量、缺少人行横道等安全设施会增加行人碰撞风险，而远端车站布局、信号灯路口等则降低风险，为公交车站附近的行人安全改进提供了数据支持。
Pedestrian safety remains a pressing concern near bus stops along urban transit, where frequent pedestrian-vehicle interactions occur. While prior research has primarily focused on intersections and midblock locations, bus stops have often been treated as secondary contributors rather than as distinct sites requiring targeted safety assessments. This has left a critical gap in understanding how traffic exposure, roadway characteristics, and bus stop design features specifically influence pedestrian crash risks around bus stop locations. To address these gaps, this study develops a comprehensive framework focused on pedestrian safety in the vicinity of bus stops. The proposed approach employs a Random Parameters Negative Binomial-Lindley (RPNB-L) model to account for unobserved heterogeneity and site-specific variability. Using data from 596 bus stops in Fort Worth, Texas (2018-2022), the model identifies that higher pedestrian crash frequencies are significantly associated with increased AADT, elevated boarding activity, and the absence of key safety elements such as crosswalks, medians, and lighting. Conversely, far-side bus stop placement, signalized intersections, sidewalks, and mixed-use development are associated with lower crash risks. Roads near schools and those with speed limits of ≤35 mph show elevated crash risk. To support proactive safety management, the study integrates a Full Bayes-based Potential for Safety Improvement (PSI) metric, enabling the identification of hazardous stops and high-risk corridors. By unifying advanced count-based modeling with strategic risk prioritization, this research offers actionable, data-driven insights for improving pedestrian safety near bus stops.