基于高速公路事故数据的自适应事故持续时间预测模型研究

doi:10.3976/j.issn.1002-4026.20240094

Abstract

Abstract:

Freeway traffic accidents seriously affect road safety and accessibility. Accurately predicting the durations of accidents is key to improving emergency response efficiency，alleviating traffic congestion，and reducing the risk of secondary accidents. This paper proposes an adaptive parameter-optimization model based on a deep belief network （DBN） and genetic algorithm （GA） for predicting traffic accident durations. Traffic accident data from freeways in Shandong province were collected from 2020 to 2022，including 16 variables such as road，temporal attributes，and environmental attributes. The Spearman correlation coefficient and box plots were used to analyze the correlation between each variable and the accident duration，ensuring the validity and significance of the selected variables. Based on this analysis，we developed an adaptive parameter optimization-based prediction model，GADBN，using numerous traffic accident data. This model integrates the global search and optimization capabilities of the GA to notably improve the predictive accuracy of the DBN. To validate the model effectiveness，experimental comparisons were conducted with other algorithms such as support vector regression，radial basis functions，XGBoost，and DBNs，with mean absolute percentage error (δ_MAPE) and root-mean-square error (δ_RMSE) being used as evaluation metrics. The experimental results showed that the GADBN model achieved δ_MAPE and δ_RMSE values of 16.49% and 9.12，respectively，outperforming the other comparison models，thereby demonstrating its effectiveness and practicality.

Key words: traffic safety, traffic accidents, accident duration, adaptive parameter optimization, freeway

CLC Number:

U491.31

JIANG Xiaoqing, WAN Qingsong, HAO Wenbang, LI Li, CHENG Weiping. Research on an adaptive accident duration-prediction model based on freeway accident data[J].Shandong Science, 2025, 38(5): 104-114.

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References 25

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变量	变量值
事故类型	0=侧翻，1=自燃，2=追尾，3=撞护栏，4=货物洒落
天气	0=晴，1=（雨、雾、雪）
交通流状态	0=畅通，1=拥堵
是否涉及多车	0=否，1=是
交叉口/弯道	0=否，1=是
是否有抛洒物	0=否，1=是
是否工作日	0=否，1=是
是否需要倒货	0=否，1=是
是否需要拖车	0=否，1=是
发生时间	0=白天，1=夜间（20：00—06：00）
是否养护施工	0=否，1=是
小车（小客车）	0~10内取整数值
大车（大客车、货车、罐装车）	0~6内取整数值
人员伤亡数	0~5内取整数值
车道数	2、4、6、8
拥堵长度	拥堵长度取值范围为0~10 km

隐含节点	预训练迭代次数	微调迭代次数	δ_MAPE/%	δ_RMSE	耗时/s
[128，64]	100	200	19.53	11.8	585
[128，64，16]	150	300	16.49	9.12	632
[256，128，64，16]	100	200	18.19	10.39	768
[256，128，64，64，16]	200	300	20.37	12.73	920

评价指标	验证组别	预测时长/min	模型预测误差
评价指标	验证组别	预测时长/min	SVR	XGBoost	RBF	DBN	GADBN
δ_MAPE/%	组1	60	25.64	19.22	16.77	15.18	13.16
δ_MAPE/%	组2		27.33	18.12	15.88	14.78	13.36
δ_RMSE	组1		18.24	12.44	9.38	8.25	7.07
δ_RMSE	组2		20.91	13.25	10.16	7.65	6.88
δ_MAPE/%	组1	120	32.54	28.32	22.45	20.78	16.49
δ_MAPE/%	组2		35.55	29.24	23.66	21.39	17.65
${{\delta }_{R}}_{MSE}$	组1		25.87	20.38	14.33	12.39	9.12
${{\delta }_{R}}_{MSE}$	组2		28.31	19.85	14.98	11.47	10.03

Research on an adaptive accident duration-prediction model based on freeway accident data

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