Data_Sheet_1_Deep Learning Based Proactive Multi-Objective Eco-Routing Strategies for Connected and Automated Vehicles.pdf (2.53 MB)
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Data_Sheet_1_Deep Learning Based Proactive Multi-Objective Eco-Routing Strategies for Connected and Automated Vehicles.pdf

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posted on 23.12.2020, 05:41 authored by Lama Alfaseeh, Bilal Farooq

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).

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