This study exploits the advancements in information and communication technology (ICT), connected and automated vehicles (CAVs), and sensing, to develop anticipatory 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 anticipatory routing strategies outperformed the myopic strategies, regardless of the routing objective. Whether myopic or anticipatory, 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.

中文翻译：

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基于深度学习的互联和自动车辆的预期多目标生态路由策略

本研究利用信息和通信技术 (ICT)、联网和自动驾驶汽车 (CAV) 以及传感方面的进步来制定预期的多目标生态路由策略。对于稳健的应用程序，检查了几种温室气体成本计算方法。链路级流量和排放状态的预测模型是使用具有外源预测因子的长短期记忆深度网络开发的。发现无论路由目标如何，预期路由策略都优于近视策略。无论是短视的还是预期的，以出行时间和温室气体最小化为目标的多目标路由策略都优于单目标路由策略，导致平均出行时间（TT）、平均车辆行驶里程（VKT）、总温室气体和总氮氧化物 17%, 21%, 18%, 和 20%，分别。最后，网络中车辆所经历的额外 TT 和 VKT 对网络中产生的 GHG 和 NOx 产生了不利影响。