发表机构
Cornell University; Shanghai Jiao Tong University（康奈尔大学; 上海交通大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种结合DCRNN预测与路由策略的框架，用于联网自动驾驶车辆在混合交通中的预测性重路由，以降低拥堵、排放和充电需求影响，实验表明其能有效改善出行效率。
AI 中文摘要
本文研究了在混合交通中考虑电动汽车充电需求的联网自动驾驶车辆（CAVs）的预测性重路由问题。我们提出了一种框架，将扩散卷积循环神经网络（DCRNN）与考虑拥堵、CO2排放、路线长度和充电需求的路由策略相结合。DCRNN利用历史网络观测数据来预测交通状况和充电需求。这些预测随后被用于评估符合条件的CAVs的可行替代路线。当替代路线保持与原始目的地的连通性并改善规定路线成本时，接受路线变更。我们在SUMO中，在五个CAV渗透率（从5%到45%）下，于受控交通中断场景中评估了所提出的框架。我们将其性能与K最短路径路由、预测密度路由、V2X主动路由以及无重路由的参考场景进行了比较。在所考虑的场景中，所提出的框架相对于参考场景将平均出行时间指数降低了约1.8%，并在主动路由方法中实现了最低的平均出行时间指数、最高的平均速度和最低的总CO2排放。在所有五个渗透率水平下，它接受了41次路线变更，而K最短路径路由为146次，V2X主动路由为153次。参考场景保持了较低的总排放量和行驶距离，说明了减少拥堵与重路由带来的额外行驶之间的权衡。
英文摘要
In this paper, we consider the problem of predictive rerouting of connected and automated vehicles (CAVs) in mixed traffic with electric vehicle charging demand. We provide a framework that combines a diffusion convolutional recurrent neural network (DCRNN) with a routing policy that accounts for congestion, CO2 emissions, route length, and charging demand. The DCRNN uses historical network observations to forecast traffic conditions and charging demand. These forecasts are then used to evaluate feasible alternative routes for eligible CAVs. A route change is accepted when the alternative preserves connectivity to the original destination and improves the prescribed route cost. We evaluate the proposed framework in SUMO under controlled traffic disruptions at five CAV penetration levels, ranging from 5% to 45%. We compare its performance with K-shortest-path routing, predictive-density routing, V2X proactive routing, and a reference scenario without rerouting. In the considered scenarios, the proposed framework reduces the average travel-time index by approximately 1.8% relative to the reference scenario and achieves the lowest average travel-time index, highest average speed, and lowest aggregate CO2 emissions among the active routing methods. Across all five penetration levels, it accepts 41 route changes, compared with 146 for K-shortest-path routing and 153 for V2X proactive routing. The reference scenario retains lower aggregate emissions and distance traveled, illustrating the tradeoff between congestion reduction and the additional travel associated with rerouting.
Comments8 Pages
