Using pedestrian-vehicle crash data in North Carolina from 2007 to 2018, this study explores the potential variation in the influence of factors affecting pedestrian injury severity in different time periods (weekday/weekend and three-year period). To capture unobserved heterogeneity, random parameters logit models with heterogeneity in means and variances are employed. In developing the model, several categories of factors are considered, including characteristics of the pedestrian, driver, crash, locality and roadway, time and environment, traffic control, and work zone. Transferability tests are conducted to examine the possible temporal instability of the estimation results between different time periods. According to the results, factors such as “ambulance rescue” and “curved roadway” produce temporally stable effects on pedestrian injury severity. However, strong temporal instabilities in effects on pedestrian injury severity are found for most factors across the three-year period and the weekday/weekend. In regard to structure, the model offers more insights by accounting for possible heterogeneity in the means and variances of the random parameters. Detailed policy-related recommendations are provided based on the analysis results. The findings of this work should be helpful to policymakers in future planning on safety improvements for pedestrians within the transportation system.

利用2007年至2018年北卡罗来纳州的行人车辆碰撞数据，本研究探讨了在不同时间段（工作日/周末和三年期间）中影响行人伤害严重性的因素的影响的潜在变化。为了捕获未观察到的异质性，采用均值和方差具有异质性的随机参数logit模型。在开发模型时，要考虑几类因素，包括行人，驾驶员，碰撞，地点和道路，时间和环境，交通控制和工作区域的特征。进行可传递性测试以检查不同时间段之间估计结果可能存在的时间不稳定。根据结果​​，“救护车救援”和“弯曲道路”等因素对行人伤害的严重程度产生了暂时稳定的影响。然而，在三年期间和工作日/周末，大多数因素都发现对行人伤害严重程度的强烈时间不稳定。关于结构，该模型通过考虑随机参数的均值和方差中可能存在的异质性，从而提供了更多的见解。根据分析结果提供与政策相关的详细建议。这项工作的发现应有助于政策制定者在未来计划中改善运输系统内行人的安全。通过考虑随机参数的均值和方差中可能存在的异质性，该模型提供了更多的见解。根据分析结果提供与政策相关的详细建议。这项工作的发现应有助于政策制定者在未来计划中改善运输系统内行人的安全。通过考虑随机参数的均值和方差中可能存在的异质性，该模型提供了更多的见解。根据分析结果提供与政策相关的详细建议。这项工作的发现应有助于政策制定者在未来计划中改善运输系统内行人的安全。