Deep Learning Based Proactive Multi-Objective Eco-Routing Strategies for Connected and Automated Vehicles
This addresses eco-friendly routing for connected and automated vehicles, offering incremental improvements in reducing emissions and travel time.
The study developed proactive multi-objective eco-routing strategies for connected and automated vehicles, using LSTM deep networks to predict traffic and emissions, and found that these strategies reduced average travel time by 17%, vehicle kilometers traveled by 21%, total GHG by 18%, and total NOx by 20% compared to myopic approaches.
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.