交通运输系统工程与信息

交通运输系统工程与信息 ›› 2021, Vol. 21 ›› Issue (1): 190-195.

• 系统工程理论与方法 • 上一篇    下一篇

山区双车道公路机动车碰撞事故严重度致因比较分析与预测

杨文臣1，谢碧珊1, 2，房锐*1，秦雅琴2   

  1. 1. 云南省交通规划设计研究院有限公司，陆地交通气象灾害防治技术国家工程实验室，昆明 650200； 2. 昆明理工大学，交通工程学院，昆明 650504
  • 收稿日期:2020-08-18 修回日期:2020-11-19 出版日期:2021-02-25 发布日期:2021-02-25
  • 作者简介:杨文臣(1985- )，男，云南昌宁人，高级工程师，博士。
  • 基金资助:

    国家重点研发计划项目/National Key Research and Development Program of China(2017YFC0803906)；云南省基础研究计划项目/Yunnan Fundamental Research Projects(2019FB072)；国家自然科学基金/National Natural Science Foundation of China(71861016)。

Comparative Analysis and Prediction of Motor Vehicle Crash Severity on Mountainous Two-lane Highways

YANG Wen-chen1, XIE Bi-shan1, 2, FANG Rui*1, QIN Ya-qin2   

  1. 1. National Engineering Laboratory for Surface Transportation Weather Impacts Prevention, Broadvision Engineering Consdultants Co. Ltd, Kunming 650200, China; 2. Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming 650504, China
  • Received:2020-08-18 Revised:2020-11-19 Online:2021-02-25 Published:2021-02-25

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摘要：

以云南山区双车道公路1740起碰撞事故数据为基础，将事故数据分为机动车与机动车、机动车与摩托车、机动车与非机动车3种类型，事故严重度划分为仅财产损失、轻伤、重伤或死亡事故3个等级，分别用部分优势比模型和有序Logit模型建立3类机动车碰撞事故严重度分析模型，对比分析不同等级事故的显著影响因素和模型的预测准确率，分析部分优势比模型自变量的边际效应。结果表明：不同交通方式下机动车碰撞事故严重度的影响因素存在明显差异，对比有序 Logit模型，部分优势比的事故分析模型刻画了不满足比例优势假设的自变量，机动车与机动车、机动车与摩托车、机动车与非机动车碰撞事故的平均预测准确率分别为 78.29%、73.63%和 72.04%，分别提高14.54%、5.65%和3.32%。研究结果可为山区公路运营管理部门开展事故风险主动防控提供决策参考。

关键词: 交通工程, 事故严重度, 部分优势比模型, 山区双车道公路, 边际效应分析, 机动车碰撞

Abstract:

This paper developed the motor vehicle crash severity prediction models on mountainous two-lane highways using the partial proportional odds model and the ordered Logit model. Based on 1740 cases of motor vehicle crashes in Yunnan province, the crash data was classified as motor vehicle and motor vehicle crash, motor vehicle and motorcycle crash, motor and non- motorized vehicle crash. The accident severity was classified as property damage only, minor injuries, and serious injuries or fatal. The comparative analysis of the significant factors and the model prediction accuracy relevant to each severity grade was carried out. The marginal effect analysis was also performed to investigate significant variables of the partial proportional odds model. The results show that the impact factors for different accident severity are significantly different for different vehicle types. Compared to the ordered Logit model, the partial proportional odds model could be used to find the hidden variables that are not following the proportional odds assumption. The average prediction accuracy using the partial proportional odds model are respectively 78.29%, 73.63% and 72.04% for motor vehicle and motor vehicle crash, motor vehicle and motorcycle crash, and motor and non-motorized vehicle crash. The accuracy is respectively improved by 14.54%, 5.65% and 3.32% compared to the ordered Logit model. The study provides references for highway safety administrations to proactively prevent accidents and reduce risks.

Key words: traffic engineering, crash severity, partial proportional odds model, mountainous two-lane highway, marginal effect analysis, motor vehicle crash

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