Modelling severity of pedestrian-injury in pedestrian-vehicle crashes with latent class clustering and partial proportional odds model: A case study of North Carolina.
Modelling severity of pedestrian-injury in pedestrian-vehicle crashes with latent class clustering and partial proportional odds model: A case study of North Carolina.
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DOI：
10.1016/j.aap.2019.07.008
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发表时间：
2019-10
期刊：
影响因子：
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通讯作者：
Yang Li;W. Fan
中科院分区：
文献类型：
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作者：
Yang Li;W. Fan
据报道，北卡罗来纳州每年有超过 2000 名行人与车辆发生交通事故。其中 10%–20% 死亡或重伤。需要进行研究，以确定影响因素并制定对策，以提高行人的安全。然而，由于碰撞数据固有的异质性，这些数据是由执法机构未报告和/或无法从州碰撞记录中收集的不可观察因素引起的，因此识别和评估影响此类碰撞中行人受伤严重程度的因素并不容易。通过利用潜在类别聚类（LCC），本研究首先应用LCC方法来识别潜在类别，并对具有不同分布特征的行人-车辆碰撞事故影响因素的碰撞事故进行分类。通过考虑交通事故严重程度数据的固有有序性，开发并利用部分比例赔率（PPO）模型来探索对之前在 LCC 中获得的每个潜在类别的行人车辆碰撞事故造成的行人伤害严重程度有显着影响的主要因素。这项研究使用了警方报告的 2007 年至 2014 年北卡罗来纳州收集的行人碰撞数据，包含驾车者、行人、环境、道路特征的各种特征。参数估计和相关边际效应主要用于解释模型并评估每个自变量的显着性。最后提出了政策建议并给出了未来的研究方向。
There are more than 2000 pedestrians reported to be involved in traffic crashes with vehicles in North Carolina every year. 10%–20% of them are killed or severely injured. Research studies need to be conducted in order to identify the contributing factors and develop countermeasures to improve safety for pedestrians. However, due to the heterogeneity inherent in crash data, which arises from unobservable factors that are not reported by law enforcement agencies and/or cannot be collected from state crash records, it is not easy to identify and evaluate factors that affect the injury severity of pedestrians in such crashes. By taking advantage of the latent class clustering (LCC), this research firstly applies the LCC approach to identify the latent classes and classify the crashes with different distribution characteristics of contributing factors to the pedestrian-vehicle crashes. By considering the inherent ordered nature of the traffic crash severity data, a partial proportional odds (PPO) model is then developed and utilized to explore the major factors that significantly affect the pedestrian injury severities resulting from pedestrian-vehicle crashes for each latent class previously obtained in the LCC. This study uses police reported pedestrian crash data collected from 2007 to 2014 in North Carolina, containing a variety of features of motorist, pedestrian, environmental, roadway characteristics. Parameter estimates and associated marginal effects are mainly used to interpret the models and evaluate the significance of each independent variable. Lastly, policy recommendations are made and future research directions are also given.