Deep Learning Based Proactive Multi-Objective Eco-Routing Strategies for Connected and Automated Vehicles - Alfaseeh, Lama; Farooq, Bilal (Frontiers in Future Transportation, 2020)
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 proactive multi-objective (travel time and GHG) routing strategy, we observed a reduction in average travel time (17%), average vehicle kilometre travelled (22%), total GHG (18%), and total nitrogen oxide (20%), when compared with the reactive single-objective (travel time).