论文标题
基于深度学习的积极主体多目标生态路由策略，用于连接和自动化的车辆
Deep Learning Based Proactive Multi-Objective Eco-Routing Strategies for Connected and Automated Vehicles
论文作者
论文摘要
这项研究利用了信息和通信技术（ICT），连接和自动化车辆（CAVS）的进步，以及感知，以开发积极的多目标生态路由策略。对于强大的应用，检查了几种温室气成本核算方法。链路流量和发射状态的预测模型是使用带有外源预测因子的长期短期内存深网开发的。发现主动路由策略的表现超过了近视策略，而不管路由目标如何。无论是近视还是主动的，多目标路由，旅行时间和温室气体作为目标的最小化，都超过了单个目标路由策略，导致平均旅行时间（TT）减少，平均车辆千米（VKT），总温室气和总NOX和NOX总NOX，分别为17％，21％，18％和20％。最后，网络中车辆经历的额外的TT和VKT对网络中产生的GHG和NOX的数量有不利的贡献。
This study exploits the advancements in information and communication technology (ICT), connected and automated vehicles (CAVs), and sensing, to develop proactive multi-objective eco-routing strategies. For a robust application, several GHG costing approaches are examined. The predictive models for the link level traffic and emission states are developed using long short term memory deep network with exogenous predictors. It is found that proactive routing strategies outperformed the myopic strategies, regardless of the routing objective. Whether myopic or proactive, the multi-objective routing, with travel time and GHG minimization as objectives, outperformed the single objective routing strategies, causing a reduction in the average travel time (TT), average vehicle kilometre travelled (VKT), total GHG and total NOx by 17%, 21%, 18%, and 20%, respectively. Finally, the additional TT and VKT experienced by the vehicles in the network contributed adversely to the amount of GHG and NOx produced in the network.
