Deep Learning Based Proactive Multi-Objective Eco-Routing Strategies for Connected and Automated Vehicles

Alfaseeh, Lama; Farooq, Bilal

doi:10.3389/ffutr.2020.594608

Cited by 2 publications

(4 citation statements)

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“…For instance, increasing throughput, which can be achieved by choosing higher speed links, may have an adverse impact on produced GHG due to the quasi-convex relationship between speed and GHG . Increasing distance travelled while minimizing travel time may also contribute to more produced GHG and NOx emissions (Alfaseeh and Farooq, 2020a).…”

Section: Literaturementioning

confidence: 99%

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Advancements in multi-objective eco-routing solutions using connectivity

Alfaseeh¹

2021

Preprint

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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.<div>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. </div>

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Section: Literaturementioning

confidence: 99%

“…For example, an improvement in the network throughput may have an adverse impact on the environmental aspect. A longer travelling distance may contribute to a lower travel time, but it may also result in more GHG emissions (Alfaseeh and Farooq, 2020a).…”

Section: Introductionmentioning

confidence: 99%