%A Alfaseeh,Lama %A Farooq,Bilal %D 2020 %J Frontiers in Future Transportation %C %F %G English %K Long-short term memory network (LSTM),Anticipatory routing,Network state prediction,Greenhouse gas (GHG) emissions,Multiobjactive optimization,Eco-routing %Q %R 10.3389/ffutr.2020.594608 %W %L %M %P %7 %8 2020-December-23 %9 Original Research %# %! Deep Learning Multi-Objective Eco-Routing %* %< %T Deep Learning Based Proactive Multi-Objective Eco-Routing Strategies for Connected and Automated Vehicles %U https://www.frontiersin.org/articles/10.3389/ffutr.2020.594608 %V 1 %0 JOURNAL ARTICLE %@ 2673-5210 %X 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).