Cai Jianrong, Liu Yang, Li Zhixue
School of Civil Engineering, Hunan City University, Yiyang, China.
School of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou, China.
PLoS One. 2024 Dec 9;19(12):e0314044. doi: 10.1371/journal.pone.0314044. eCollection 2024.
Rear-end collisions frequently occurred in the entrance zone of expressway tunnel, necessitating enhanced traffic safety through speed guidance. However, existing speed optimization models mainly focus on urban signal-controlled intersections or expressway weaving zones, neglecting research on speed optimization in expressway tunnel entrances. This paper addresses this gap by proposing a framework for a speed guidance model in the entrance zone of expressway tunnels under a mixed traffic environment, comprising both Connected and Autonomous Vehicles (CAVs) and Human-driven Vehicles (HVs). Firstly, a CAV speed optimization model is established based on a shooting heuristic algorithm. The model targets the minimization of the weighted sum of the speed difference between adjacent vehicles and the time taken to reach the tunnel entrance. The model's constraints incorporate safe following distances, speed, and acceleration limits. For HVs, speed trajectories are determined using the Intelligent Driver Model (IDM). The CAV speed optimization model, represented as a mixed-integer nonlinear optimization problem, is solved using A Mathematical Programming Language (AMPL) and the BONMIN solver. Safety performance is evaluated using Time-to-Collision (TTC) and speed standard deviation (SD) metrics. Case study results show a significant decrease in SD as the CAV penetration rate increases, with a 58.38% reduction from 0% to 100%. The impact on SD and mean TTC is most pronounced when the CAV penetration rate is between 0% and 40%, compared to rates above 40%. The minimum TTC values at different CAV penetration rates consistently exceed the safety threshold TTC*, confirming the effectiveness of the proposed control method in enhanced safety. Sensitivity analysis further supports these findings.
追尾碰撞事故在高速公路隧道入口区域频繁发生，因此需要通过速度引导来提高交通安全。然而，现有的速度优化模型主要集中在城市信号控制交叉口或高速公路交织区，而忽略了对高速公路隧道入口速度优化的研究。本文通过提出一种在混合交通环境下（包括联网自动驾驶车辆（CAV）和人类驾驶车辆（HV））的高速公路隧道入口区域速度引导模型框架，来弥补这一空白。首先，基于射击启发式算法建立了CAV速度优化模型。该模型旨在使相邻车辆之间的速度差与到达隧道入口所需时间的加权和最小化。模型的约束条件包括安全跟车距离、速度和加速度限制。对于HV，使用智能驾驶员模型（IDM）确定速度轨迹。将CAV速度优化模型表示为混合整数非线性优化问题，使用数学编程语言（AMPL）和BONMIN求解器进行求解。使用碰撞时间（TTC）和速度标准差（SD）指标评估安全性能。案例研究结果表明，随着CAV渗透率的增加，SD显著降低，从0%到100%降低了58.38%。与40%以上的渗透率相比，当CAV渗透率在0%至40%之间时，对SD和平均TTC的影响最为明显。不同CAV渗透率下的最小TTC值始终超过安全阈值TTC*，证实了所提出的控制方法在提高安全性方面的有效性。敏感性分析进一步支持了这些发现。
