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Idle vehicle relocation strategy through deep learning for shared autonomous electric vehicle system optimization

Authors
Kim, SeongsinLee, UngkiLee, IkjinKang, Namwoo
Issue Date
Jan-2022
Publisher
Elsevier Ltd
Keywords
Deep learning; Demand prediction; Idle vehicle relocation; Shared autonomous electric vehicle (SAEV); System optimization
Citation
Journal of Cleaner Production, v.333, pp 1 - 16
Pages
16
Journal Title
Journal of Cleaner Production
Volume
333
Start Page
1
End Page
16
URI
https://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/152934
DOI
10.1016/j.jclepro.2021.130055
ISSN
0959-6526
1879-1786
Abstract
In optimization of a shared autonomous electric vehicle (SAEV) system, idle vehicle relocation strategies are important to reduce operation costs and customers’ wait time. However, for an on-demand service, continuous optimization for idle vehicle relocation is computationally expensive, and thus, not effective. This study proposes a deep learning-based algorithm that can instantly predict the optimal solution to idle vehicle relocation problems under various traffic conditions. The proposed relocation process comprises three steps. First, a deep learning-based passenger demand prediction model using taxi big data is built. Second, idle vehicle relocation problems are solved based on predicted demands, and optimal solution data are collected. Finally, a deep learning model using the optimal solution data is built to estimate the optimal strategy without solving relocation. In addition, the proposed idle vehicle relocation model is validated by applying it to optimize the SAEV system. We present an optimal service system including the design of SAEV vehicles and charging stations. Further, we demonstrate that the proposed strategy can drastically reduce operation costs and wait times for on-demand services. © 2021 Elsevier Ltd
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