基于动态交通流模型的车路协同网络中使总出行时间最小化的路侧单元放置
Roadside units placement for total travel time minimization in I2X-enabled road networks based on a dynamic traffic flow model
浏览论文内容
中文总结 AI 辅助
研究车联网中RSU放置问题，提出基于动态交通流模型的启发式方法，采用嵌套优化方案确定最优数量和放置配置，经实验测试，该方法能有效减少总出行时间，提高网络性能。
中文摘要 AI 辅助
这项工作旨在解决车联网中路边单元（RSU）的放置问题，以提高整体交通效率。我们提出一种启发式方法来确定RSU在道路网络上的放置位置，车辆动力学通过微观跟驰交互建模，即驾驶员根据与前车的距离不断调整速度。RSU之间共享收集的信息以协同估计网络状态，再将其传达给车辆，使其能计算最快路径。我们还假设并非所有驾驶员都遵守建议路线，只有一部分用户遵循建议路线。为在最小化部署单元数量的同时提高网络性能，该方法采用嵌套优化方案：外层模块探索RSU的最优数量k，内层模块为每个k确定最佳放置配置。为评估该方法的有效性和可扩展性，我们测试了两个不同拓扑和规模的网络。结果表明，与无RSU的基线场景相比，较小网络的总出行时间（TTT）最多可减少36%，较大网络最多可减少20%。我们还考虑了随机的车辆起终点对。
英文摘要
This work addresses the problem of locating Road Side Units (RSUs) in vehicular networks with the aim of improving overall traffic efficiency. We propose a heuristic approach for determining the placement of RSUs on a road network, while vehicle dynamics are modeled through a microscopic follow-the-leader interaction in which drivers continuously adapt their speed according to the distance from the vehicle ahead. The RSUs share the collected information among themselves to cooperatively estimate the network state, which is then communicated to vehicles, allowing them to compute their fastest paths. We also assume that not all drivers comply with the provided recommendations, meaning that only a fraction of users follow the suggested routes. Since the aim is to improve network performance while minimizing the number of deployed units, the methodology adopts a nested optimization scheme: an outer module explores the optimal number k of RSUs, while an inner module identifies the best placement configuration for each k. To evaluate the effectiveness and the scalability of the proposed method, we tested two networks with different topologies and sizes. The results show a decrease in Total Travel Time (TTT) compared to the baseline scenario (i.e., without RSUs) of up to 36% on the smaller network and up to 20% on the larger one. Stochastic vehicle origin-destination pairs have also been considered.
