融合轨迹时序与行为修正的车辆冲突风险预测

doi:10.16097/j.cnki.1009-6744.2025.04.020

交通运输系统工程与信息 ›› 2025, Vol. 25 ›› Issue (4): 219-229.DOI: 10.16097/j.cnki.1009-6744.2025.04.020

融合轨迹时序与行为修正的车辆冲突风险预测

陈喜群^*a ，祝文琪^b，吕朝锋^c

浙江大学，a.建筑工程学院，智能交通研究所；b.工程师学院，智能交通研究所；c.建筑工程学院，杭州310058

收稿日期:2025-05-11 修回日期:2025-05-31 接受日期:2025-06-11 出版日期:2025-08-25 发布日期:2025-08-25
作者简介:陈喜群(1986—)，男，黑龙江人，教授，博士。
基金资助:
国家自然科学基金(72431009, 72171210)。

Vehicle Conflict Risk Prediction Integrating Trajectory Time Series and Behavior Correction

CHEN Xiqun^*a, ZHU Wenqi^b, LV Chaofeng^c

a. Institute of Intelligent Transportation Systems, College of Civil Engineering and Architecture; b. Polytechnic Institute & Institute of Intelligent Transportation Systems; c. College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China

Received:2025-05-11 Revised:2025-05-31 Accepted:2025-06-11 Online:2025-08-25 Published:2025-08-25
Supported by:
National Natural Science Foundation of China (72431009, 72171210)。

摘要/Abstract

摘要： 针对高速公路车辆冲突指标的突变性，本文提出基于轨迹数据的车辆纵向冲突风险实时预测模型，提高车辆冲突预测精度。模型采用碰撞时间(Time-to-Collision,TTC)作为纵向冲突替代安全测度指标，将不连续的指标预测转换为连续的速度参数时序预测，通过TTC实时推演模块输出预测冲突风险；应用时序Transformer实现高精度预测，针对冲突状态下驾驶员主观行为导致的偏差，融合自适应修正模块，在当前冲突指标达到阈值时激活短期加速度拟合，通过拟合的加速度修正Transformer预测值。在实测车辆轨迹数据上验证模型有效性，结果表明：本文模型在性能指标上均优于基准模型；相比基础Transformer模型，融合了自适应偏差修正模块的自适应风险调整Transformer(Adaptive RiskAdjustment Transformer, ARATransformer)模型的均方误差(MSE)降低了48.33%，均方根误差(RMSE)降低了21.33%，平均绝对误差(MAE)降低了24.10%。此外，本文所提模型具有能够适应不同驾驶员轨迹的泛化性，为冲突预警和提高辅助驾驶情形下系统风险干预的响应水平提供了有效方法。

关键词: 智能交通, 交通冲突预测, 深度时间序列预测, 高速公路车辆轨迹, 交通安全

Abstract: To address the abrupt changes in conflict indicators for vehicles on highways, this paper proposes a real-time prediction model for longitudinal conflict risk based on trajectory data to improve the accuracy of vehicle conflict prediction. The model adopts Time-to-Collision (TTC) as a surrogate safety measure for longitudinal conflict, through transforming a discontinuous indicator prediction into a continuous time-series prediction of speed parameters. A TTC real-time deduction module is used to output the predicted conflict risk values. A time-series Transformer is employed to achieve high-precision predictions, and an adaptive correction module is integrated to address error biases caused by the subjective behaviors of drivers during conflict situations. When the current conflict indicator reaches the threshold, a short-term acceleration fitting is activated to correct the predicted values of transformer using the fitted acceleration. The effectiveness of model is validated on real-world vehicle trajectory data. The results show that the proposed model outperforms benchmark models in performance metrics. Compared with the baseline Transformer model, the Adaptive Risk Adjustment Transformer model (ARA-Transformer), which incorporates an adaptive bias correction module, reduces MSE by 48.33%, RMSE by 21.33%, and MAE by 24.10% under conflict conditions. Additionally, the proposed model demonstrates generalizability across different driver trajectories by providing an effective method for conflict warning and improving system risk intervention responsiveness in assisted driving scenarios.

Key words: intelligent transportation, traffic conflict prediction, deep time-series prediction, highway vehicle trajectory, traffic safety

中图分类号:

U491

陈喜群, 祝文琪, 吕朝锋. 融合轨迹时序与行为修正的车辆冲突风险预测[J]. 交通运输系统工程与信息, 2025, 25(4): 219-229.

CHEN Xiqun, ZHU Wenqi, LV Chaofeng. Vehicle Conflict Risk Prediction Integrating Trajectory Time Series and Behavior Correction[J]. Journal of Transportation Systems Engineering and Information Technology, 2025, 25(4): 219-229.

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参考文献

[1]杨洋.考虑区域类型差异的高速公路事故风险识别与交通安全评价研究[D]. 北京: 北京交通大学,2020. [YANG Y. Research on the method of freeway crash risk identification and comprehensive traffic safety evaluation considering the regional type difference[D]. Beijing: Beijing Jiaotong University, 2020.]

[2] ABDEL-ATY M, UDDIN N, PANDE A, et al. Predicting freeway crashes from loop detector data by matched case control logistic regression[J]. Transportation Research Record, 2004, 1897(1): 88-95.

[3] CAI Q, ABDEL-ATY M, YUAN J, et al. Real-time crash prediction on expressways using deep generative models [J]. Transportation Research Part C: Emerging Technologies, 2020, 117: 102697.

[4] GLAUZ W D, MIGLETZ D J. Application of traffic conflict analysis at intersections[R]. Transportation Research Board, 1980.

[5] DAVIS G A, HOURDOS J, XIONG H, et al. Outline for a causal model of traffic conflicts and crashes[J]. Accident Analysis & Prevention, 2011, 43(6): 1907-1919.

[6] TARKO A P. Estimating the expected number of crashes with traffic conflicts and the Lomax Distribution: A theoretical and numerical exploration[J]. Accident Analysis & Prevention, 2018, 113: 63-73.

[7]戢晓峰,耿昭师.山区双车道公路货车碰撞预测的双变量冲突极值模型[J]. 交通运输系统工程与信息, 2022, 22(2): 230- 238. [JI X F, GENG Z S. Bivariate traffic conflict extreme value model of truck collision prediction on two-lane mountain highway[J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(2): 230-238.]

[8]谢济铭,秦雅琴,彭博,等.多车道交织区车辆跟驰行为风险判别与冲突预测[J].交通运输系统工程与信息, 2021, 21(3): 131-139. [XIE J M, QIN Y Q, PENG B, et al. Risk discrimination and conflict prediction of vehicle-following behavior in multi-lane weaving sections [J]. Journal of Transportation Systems Engineering and Information Technology, 2021, 21(3): 131-139.]

[9] YUAN C, LI Y, HUANG H, et al. Using traffic flow characteristics to predict real-time conflict risk: A novel method for trajectory data analysis[J]. Analytic Methods in Accident Research, 2022, 35: 100217.

[10] WANG C, XIE Y, HUANG H, et al. A review of surrogate safety measures and their applications in connected and automated vehicles safety modeling[J]. Accident Analysis & Prevention, 2021, 157: 106157.

[11] WANG T, GE Y E, WANG Y, et al. A spatio-temporal deep learning approach to simulating conflict risk propagation on freeways with trajectory data[J]. Accident Analysis & Prevention, 2024, 195: 107377.

[12] KRAJEWSKI R, BOCK J, KLOEKER L, et al. The HighD dataset: A drone dataset of naturalistic vehicle trajectories on German highways for validation of highly automated driving systems[C]//2018 21st IEEE International Conference on Intelligent Transportation Systems, 2018: 2118-2125.

[13] HAYWARD J. Near misses as a measure of safety at urban intersections[M]. University Park, PA: Pennsylvania State University, Department of Civil Engineering, 1971.

[14] VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[J]. Advances in Neural Information Processing Systems, 2017, 30: 5998-6008.

[15] VOGEL K. A comparison of headway and time to collision as safety indicators[J]. Accident Analysis & Prevention, 2003, 35(3): 427-433.

融合轨迹时序与行为修正的车辆冲突风险预测

Vehicle Conflict Risk Prediction Integrating Trajectory Time Series and Behavior Correction

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