Deep Learning Based Proactive Multi-Objective Eco-Routing Strategies for Connected and Automated Vehicles

TL;DR

This paper develops proactive multi-objective eco-routing strategies for connected and automated vehicles using deep learning, significantly reducing travel time, vehicle kilometers, and emissions compared to traditional methods.

Contribution

It introduces a novel proactive routing approach with deep learning-based traffic and emission prediction, outperforming myopic and single-objective strategies.

Findings

Proactive routing outperforms myopic strategies in all objectives.

Multi-objective routing reduces GHG and NOx emissions significantly.

Travel time and VKT increase slightly but lead to substantial emission reductions.

Abstract

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.