The advent of intelligent vehicles that can communicate with infrastructure as well as automate the movement provides a range of new options to address key urban traffic issues such as congestion and pollution, without the need for centralized traffic control. Furthermore, the advances in the information, communication, and sensing technologies have provided access to real-time traffic and emission data. Leveraging these advancements, a dynamic multi-objective eco-routing strategy for connected & automated vehicles (CAVs) is proposed and implemented in a distributed traffic management system. It is applied to the road network of downtown Toronto in an in-house agent-based traffic simulation platform. The performance of the proposed system is compared to various single-objective optimizations. Simulation results show the significance of incorporating real-time emission and traffic state into the dynamic routing, along with considering the expected delays at the downstream intersections. The proposed multi-objective eco-routing has the potential of reducing GHG and NO_x emissions by 43% and 18.58%, respectively, while reducing average travel time by 40%.

可以与基础设施通信并自动执行动作自动化的智能车辆的出现提供了一系列新的选项，可以解决关键的城市交通问题，例如交通拥堵和污染，而无需集中交通控制。此外，信息，通信和传感技术的进步提供了对实时交通和排放数据的访问。利用这些进步，提出了一种用于互联和自动车辆（CAV）的动态多目标生态路由策略，并在分布式交通管理系统中实现了该策略。它在内部基于代理的交通模拟平台中应用于多伦多市区的道路网络。将提出的系统的性能与各种单目标优化进行比较。仿真结果表明了将实时排放和交通状态纳入动态路径的重要性，并考虑了下游交叉口的预期延误。拟议的多目标生态路由具有减少温室气体和NO的潜力_x排放分别减少了43％和18.58％，同时将平均旅行时间减少了40％。