  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.
