Abstract
A multi-objective eco-routing is developed and implemented in a real-time dynamic routing system based on a distributed network of intelligent intersections and connected & autonomous vehicles (CAVs). The performance of the proposed eco-routing system and its impact on travel time (TT), vehicle kilometres travelled (VKT), greenhouse gas (GHG), and Nitrogen Oxide (NOx) emissions are analyzed on Downtown Toronto network in an agent-based traffic simulation platform. A comparison between estimation approaches of TT based on two levels of spatial and temporal resolution is applied. The results showed that routing to optimize TT while adopting higher level of spatial and temporal resolution is the best among the single objective routing scenarios. Multi-objective routing strategies further reduced TT, GHG, and NOx when comparing to routing solely based on TT. Estimation approach based on a higher level of disaggregation produced substantially better results due to its ability to capture traffic characteristics more efficiently.
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