考虑应激避让行为的自行车轨迹预测

doi:10.16097/j.cnki.1009-6744.2024.02.015

交通运输系统工程与信息 ›› 2024, Vol. 24 ›› Issue (2): 149-156.DOI: 10.16097/j.cnki.1009-6744.2024.02.015

考虑应激避让行为的自行车轨迹预测

李岩¹，梁淑娟¹，刘林建²，邵进¹，汪帆^*1,3

1. 长安大学，运输工程学院，西安710064；2.江苏省常州市规划设计院，江苏常州213000； 3. 中交第一公路勘察设计研究院有限公司，西安710075

收稿日期:2023-11-19 修回日期:2024-01-03 接受日期:2024-01-09 出版日期:2024-04-25 发布日期:2024-04-25
作者简介:李岩(1983- )，男，河北衡水人，教授。
基金资助:
国家自然科学基金(51408049)；陕西省自然科学基础研究计划项目(2020JM-237)。

Prediction of Bicycle Trajectory Considering Stressful Avoidance Behaviors

LI Yan¹,LIANG Shujuan¹,LIU Linjian²,SHAO Jin¹,WANG Fan^*1,3

1. School of Transportation Engineering, Chang'an University, Xi'an 710064, China; 2. Planning and Design Institute of Changzhou City, Jiangsu Province, Changzhou 213000, Jiangsu, China; 3. China Communications Construction Company First Highway Consultants Co LTD, Xi'an 710075, China

Received:2023-11-19 Revised:2024-01-03 Accepted:2024-01-09 Online:2024-04-25 Published:2024-04-25
Supported by:
NationalNaturalScienceFoundation of China (51408049)； Natural Science Basic Research Plan in Shaanxi Province (2020JM-237)。

摘要/Abstract

摘要： 非机动车道空间受限时，常规自行车被超车场景下骑行者为确保自身安全会产生应激避让行为。为明确其在被超车时的应激反应，并根据行为特性设计非机动车道，构建一种面向应激行为分类的自行车轨迹预测模型。该模型从频域角度分解自行车被超车时的动力学特性，依据踏频值范围将避让行为分为匀速、加速和减速行为，利用鲸鱼算法改进长短期记忆神经网络模型，分别对分类后的骑行轨迹进行预测。应用所构建预测模型对西安市2415次超车事件的分析结果显示，发生冲突时选择上述3种避让行为的骑行者占比分别为11.3%、38.3%和50.4%。匀速避让的预测轨迹全程波动较小，平均横向位移为0.15m；加速避让轨迹表现为横向位移较大，平均达0.83 m；减速行为预测轨迹平缓度介于两者之间，横向位移为0.47m。3种预测情况下的均方根误差分别为0.0619、0.0513和0.0587，拟合优度值分别为0.9589、0.9774和0.9687。与未考虑应激行为分类的结果相比，所构建模型的预测精度分别提升了11.07%、13.22%和12.21%。

关键词: 城市交通, 轨迹预测, 频域分析法, 改进LSTM, 常规自行车, 应激避让行为

Abstract: When the space in non-motorized lanes is restricted, cyclists in an overtaken scenario will generate stressful avoidance behaviors to ensure their own safety. In order to clarify their stress reaction when being overtaken, and to design the non-motorized lane according to their behavioral characteristics, a bicycle trajectory prediction model for stress behavior classification is proposed. The model decomposes the dynamic characteristics of bicycles from the frequency domain perspective, classifies the stressful avoidance behaviors into uniform speed, acceleration, and deceleration behaviors based on the cadence range, and uses the whale algorithm to improve the long and short-term memory neural network model to predict the classified cycling trajectories. The proposed method was tested using the 2415 overtaken events obtained from Xi'an City. The results indicate that the proportions of the three avoidance behaviors are 11.3%, 38.3%, and 50.4%, respectively. The predicted trajectory of uniform speed avoidance has a small fluctuation throughout the whole process, with an average lateral displacement of 0.15 m. The predicted trajectory of accelerated avoidance shows larger lateral displacement, with an average of 0.83 m. The predicted trajectory of decelerated behavior has a lateral displacement of 0.47 m. The root mean square errors of these three behaviors are 0.0619, 0.0513, and 0.0587, with their goodness of fit as 0.9589, 0.9774, and 0.9687, respectively. Compared to the results of the model without the consideration of stress-based behaviors, the proposed model improves the prediction accuracy by 11.07%, 13.22%, and 12.21%, respectively.

Key words: urban traffic, trajectory prediction, frequency domain analysis method, improved LSTM, conventional bicycles, stress avoidance behavior

中图分类号:

U491

李岩, 梁淑娟, 刘林建, 邵进, 汪帆. 考虑应激避让行为的自行车轨迹预测[J]. 交通运输系统工程与信息, 2024, 24(2): 149-156.

LI Yan, LIANG Shujuan, LIU Linjian, SHAO Jin, WANG Fan. Prediction of Bicycle Trajectory Considering Stressful Avoidance Behaviors[J]. Journal of Transportation Systems Engineering and Information Technology, 2024, 24(2): 149-156.

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考虑应激避让行为的自行车轨迹预测

Prediction of Bicycle Trajectory Considering Stressful Avoidance Behaviors

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