Research on duration prediction of traffic accident on expressways based on similarity of event evolution graphs

doi:10.16265/j.cnki.issn1003-3033.2026.07.1372

Abstract

Abstract:

To mitigate traffic congestion resulting from expressway accidents and enhance overall operational efficiency, this paper proposes a S-EEG method for predicting expressway traffic accident duration based on the similarity of event evolution graphs. Initially, an ontology model of expressway traffic accidents was constructed using attributes, entities, events, and relationships. The event evolution graph technology was employed to integrate logical knowledge from structured data and textual data, revealing the evolution patterns of traffic accidents and characteristics of emergency response. Subsequently, a graph structure similarity evaluation model based on MCS was proposed, along with an optimization method for determining the hyperparameter K and statistical parameter. Finally, an abstract event evolution graph for expressway traffic accidents was built through knowledge extraction and event generalization, and the prediction of traffic accident duration is achieved by statistically inferring the duration of K highly similar cases. Furthermore, an empirical analysis was carried out using expressway traffic accident records from Shaanxi Province, and a corresponding event evolutionary graph for expressway traffic accidents was built, comprising 242,150 nodes and 251,596 directed edges. The results indicate that the proposed model achieves a mean absolute percentage error (MAPE) of 42.33%, reducing the prediction error by 56.68% compared to the baseline model. Additionally, it improves performance by approximately 2.31% in the classification task of accident duration.

CLC Number:

X951交通运输安全

Chen Jiaona, Zhang Jin, Mao Yiwen, Jin Yinli. Research on duration prediction of traffic accident on expressways based on similarity of event evolution graphs[J]. China Safety Science Journal, 2026, 36(7): 77-85.

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定义		示例
事件	处置机构	施救队、消防
	处置人员	救援人员、养护人员
	处置设备	拖车、救护车、消防车
	处置措施	救援清理、强制分流、摆放锥形桶
	处置效果	火已扑灭、事故已处理完毕
	任务预判	预计1 h处理完毕
	任务难度	维修难度较大、救援难度较大
	事故持续时间	预计15 min、预计1 h
关系	属性关系	<编号,属性,货车>、 <编号,属性,侧翻>
	顺承关系	<路政,顺承,正在赶往现场>; <高交,顺承,临时管制>
	因果关系	<编号,因果,有路产损失>
	包含关系	<临时管制,包含,交警>
	并列关系	<灭火,并列,交通管制>
	对比关系	<预计15 min,对比,45 min>
实体	事故类型	剐蹭、追尾、侧翻、碰撞、自燃
	事故桩号	K+500、K+580
	事故地点类型	桥梁、主线、隧道、匝道
	事故车牌号	云D44^、京AFX^
	事故车辆类型	轿车、半挂、客车、危化品车、货车
	天气	晴、雾、阴、雨、雪
	路产损失	路产不详、有路产损失、无路产
	人员伤亡情况	无人员伤亡、2人轻伤
	交通管制	临时管制、临时封闭
属性	影响车道情况	占应急车道、占超车道
	实际持续时间	14 min、40 min
	所属路段	收费站至收费站
	事发时间	2021-01-01 T8:48:00
	交通分流	未分流、待核实、分流中
	编号	—

文献	MAPE值
[7]	2~15 min: 185.7%, >15 min: 45.4%,整体: 94.7%
[10]	5~15 min: 100.9%, 16~35 min: 75%~96%, 36~200 min: 20%~50%, >120 min: 50%~76%,整体: 27.58%~61%
[17]	异构集成模型: 35.610 1%
[26]	S-SVM: 21.7%, S-KNN: 20.8%
[28]	RF:75.155%
[32]	文本相似度BiGRU模型: 43.74%
文中	S-EEG:42.33%

模型	F
Word2vec-BiLSTM-CNN^[31]	0.520
S-EEG	0.532