posted on 2021-11-15, 16:46authored byLama Alfaseeh
Due to the significant adverse impact of transportation systems on the environment, topics related to alleviating greenhouse gas (GHG) emissions are gaining more attention. As
potential solutions to mitigate GHG emissions, several approaches have been proposed to
better control traffic and manage transportation systems. The employment of Intelligent
Transportation System (ITS), which adopts the advancements in Information and Communication Technology (ICT), has been proposed as the most favourable approach to alleviate
the undesirable impact of transportation systems on the environment. ITS can control several aspects of a network, such as speed, traffic signals, and route guidance. For the purpose
of routing, this research aims to exploit the advancements in ICT by including connected
and automated vehicles (CAVs) and sensing technology in an urban congested network.
Anticipatory multi-objective eco-routing in a distributed routing framework was proposed and compared to myopic routing with a large case study on a congested network.
The End-to-End Connected Autonomous Vehicles (E2ECAV) dynamic distributed routing
framework was examined, and encouraging results were found based on the traffic and environmental perspectives. The impact of different market penetration rates (MPRs) of CAVs
was examined for various traffic conditions. E2ECAV was adopted for both the myopic and
anticipatory routing strategies in this dissertation. The best GHG costing approach was defined and was among the elements tackled in this research. For a robust anticipatory routing
application, predictive models were developed based on Long-Short Term Memory (LSTM),
a deep learning approach, while considering a high level of spatial (link level) and temporal
(one minute) resolution. With regards to the LSTM predictive models, the impact was illustrated of using a deeper LSTM network and systematically tuning its hyper-parameters.
The anticipatory routing strategy significantly outperformed the myopic routing strategy
based on the the traffic and environmental perspectives. This research shows that ITS can
help significantly reduce GHG emissions produced by transportation systems. The developed predictive models can be used while real-time data are collected from sensors within an
urban network. Furthermore, the proposed anticipatory routing framework can be applied
in a real-time situation.