Zhang Ting, Chen Zheng, Chen Feng, You Kesi
The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, Shanghai, China.
Shandong High Speed Construction Management Group Co., Ltd., Jinan, Shandong, China.
Traffic Inj Prev. 2025;26(1):83-91. doi: 10.1080/15389588.2024.2390086. Epub 2024 Dec 9.
In the freeway tunnel approach section, lane-changing behaviors and transitions in the driving environment exacerbate traffic flow disruptions, increase driving risks, and lead to a higher accident rate. To this end, this study presents a method to explore the risk evolution process of lane-changing in these sections and evaluate its impact on traffic flow operations surrounding lane-changing vehicles.
First, a driving risk potential field model based on the field theory, which consists of a vehicle kinetic potential field and a tunnel illumination potential field, is proposed to evaluate the driving risk. Furthermore, a "vehicle group" risk graph was constructed based on graph theory, incorporating both a node-coupling driving risk model and a topological potential entropy model. Finally, trajectory datasets were collected through naturalistic driving tests to analyze the evolution of lane-changing risk and the stability of the vehicle group.
From the analysis of coupling driving risk evolution, we found that in the [100, 500) m, [500, 1000) m, and [1000, 1500) m freeway tunnel approach sections, the coupled driving risk of lane-changing vehicle (LCV) was higher than that of the other vehicles in the vehicle group. In different tunnel approach sections, LCVs that received the highest risk were from different vehicles in vehicle group. LCVs received the highest field strength from the risk potential fields of the lateral vehicle (LV), front lateral vehicle (FLV), and front vehicle (FV) in the [100, 500) m, [500, 1000) m, [1000, 1500) m tunnel approach sections, respectively. Based on the absolute value of the vehicle group topological potential entropy, we observed the resilience of the vehicle group system improved with increasing distance from the tunnel entrance. Traffic flow regained stability more quickly after lane-changing disturbances in sections farther from the tunnel entrance.
This study highlights that the section closer to the freeway tunnel entrance significantly impact lane-changing risk, and it takes longer for the vehicle group to recover its stability after a lane-changing disturbance. The research results offer a theoretical and methodological foundation for enhancing traffic safety measures and developing microscopic driving behavior models for freeway tunnel approach sections.
在高速公路隧道引道段，换道行为以及驾驶环境的变化会加剧交通流的干扰，增加驾驶风险，并导致更高的事故率。为此，本研究提出一种方法，以探索这些路段换道的风险演变过程，并评估其对换道车辆周围交通流运行的影响。
首先，基于场论提出一种驾驶风险势场模型，该模型由车辆动力学势场和隧道照明势场组成，用于评估驾驶风险。此外，基于图论构建了一个“车辆组”风险图，纳入了节点耦合驾驶风险模型和拓扑势熵模型。最后，通过自然驾驶测试收集轨迹数据集，以分析换道风险的演变和车辆组的稳定性。
通过对耦合驾驶风险演变的分析，我们发现，在高速公路隧道引道段的[100, 500)米、[500, 1000)米和[1000, 1500)米区间，换道车辆（LCV）的耦合驾驶风险高于车辆组中的其他车辆。在不同的隧道引道段，风险最高的换道车辆来自车辆组中的不同车辆。在[100, 500)米、[500, 1000)米、[1000, 1500)米隧道引道段，换道车辆分别从侧向车辆（LV）、前侧向车辆（FLV）和前车（FV）的风险势场中获得最高场强。基于车辆组拓扑势熵的绝对值，我们观察到车辆组系统的恢复能力随着离隧道入口距离的增加而提高。在离隧道入口较远的路段，换道干扰后交通流恢复稳定的速度更快。
本研究强调，靠近高速公路隧道入口的路段对换道风险有显著影响，并且车辆组在换道干扰后恢复稳定所需的时间更长。研究结果为加强交通安全措施以及开发高速公路隧道引道段的微观驾驶行为模型提供了理论和方法基础。
