Data_Sheet_1_Deep Learning Based Proactive Multi-Objective Eco-Routing Strategies for Connected and Automated Vehicles.pdf

This study exploited the advancements in information and communication technology (ICT), connected and automated vehicles (CAVs), and sensing to develop proactive multi-objective eco-routing strategies for travel time and Greenhouse Gas (GHG) emissions reduction on urban road networks. For a robust application, several GHG costing approaches were examined. The predictive models for link level traffic and emission states were developed using the long short-term memory (LSTM) deep network with exogenous predictors. It was found that proactive routing strategies outperformed the reactive strategies regardless of the routing objective. Whether reactive or proactive, the multi-objective routing, with travel time and GHG minimization, outperformed the single objective routing strategies. Using a proactive multi-objective (travel time and GHG) routing strategy, we observed a reduction in average travel time (17%), average vehicle kilometer traveled (22%), total GHG (18%), and total nitrogen oxide (20%) when compared with the reactive single-objective (travel time).

本研究充分利用了信息通信技术（ICT）、智能网联汽车（CAV）和感知技术的进步，旨在开发旨在减少城市道路网络中旅行时间和温室气体（GHG）排放的主动多目标生态路由策略。为了实现稳健的应用，本研究考察了多种GHG成本计算方法。通过长短期记忆（LSTM）深度网络与外生预测因子相结合，开发了路段级交通和排放状态的预测模型。研究发现，无论目标路由如何，主动路由策略均优于反应性策略。无论是反应性还是主动性，以旅行时间和GHG最小化为目标的多目标路由策略均优于单一目标路由策略。与反应性单一目标（旅行时间）策略相比，采用主动多目标（旅行时间和GHG）路由策略，平均旅行时间降低了17%，平均车辆行驶里程降低了22%，总GHG排放量降低了18%，总氮氧化物排放量降低了20%。