Abstract

In transportation, pedestrians are among the most vulnerable entities. Each year, a total of about 2,000 pedestrians are reported to be involved in traffic crashes with vehicles in North Carolina. Research efforts are needed to identify influencing factors and develop safety improvement measures for pedestrians. This study applies mixed logit (ML) model approach to exploring the potential unobserved heterogeneities across individual injury observations. Factors that significantly contribute to pedestrian injury severities resulting from pedestrian-vehicle crashes are examined under a variety of categories, including motorist, pedestrian, environmental, and roadway (etc.) characteristics. Police reported pedestrian-vehicle crash data collected from 2007 to 2014 in North Carolina are utilized. Parameter estimates and associated elasticities are used to interpret the results.

摘要

在交通运输中，行人是最脆弱的实体之一。据报道，北卡罗来纳州每年约有 2,000 名行人与车辆发生交通事故。需要研究工作以确定影响因素并制定行人安全改进措施。本研究应用混合 logit (ML) 模型方法来探索个体损伤观察中潜在的未观察到的异质性。对因行人与车辆碰撞而导致的行人伤害严重程度有显着影响的因素在各种类别下进行了检查，包括驾驶者、行人、环境和道路（等）特征。警方报告称，2007 年至 2014 年在北卡罗来纳州收集的行人车辆碰撞数据被利用。