International Journal of Computational Intelligence Systems

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Volume 14, Issue 1, 2021, Pages 1714 - 1727

A Distributed Urban Traffic Congestion Prevention Mechanism for Mixed Flow of Human-Driven and Autonomous Electric Vehicles

Authors
Chenn-Jung Huang1, 2, *, Kai-Wen Hu2, Hsing Yi Ho1, Bing Zhen Xie1, Chien-Chih Feng1, Hung-Wen Chuang1
1Department of Computer Science and Information Engineering, National Dong Hwa University, Hualien, 974301, Taiwan
2Department of Electrical Engineering, National Dong Hwa University, Hualien, 974301, Taiwan
*Corresponding author. Email: cjhuang@gms.ndhu.edu.tw
Corresponding Author
Chenn-Jung Huang
Received 14 January 2021, Accepted 31 May 2021, Available Online 12 June 2021.
DOI
10.2991/ijcis.d.210608.001How to use a DOI?
Keywords
Support vector regressions; Optimization; Intelligent transportation systems; Autonomous mobility-on-demand; Congestion control; Machine learning
Abstract

Traffic congestion in urban areas has become a critical problem that municipal governments cannot overlook. Meanwhile, mixed traffic systems containing both autonomous and human-driven electric vehicles ramp up the challenge for traffic management in urban areas. Although numerous researchers have proposed traffic control heuristics to alleviate traffic congestion problems in the recent literature, scant research has addressed the joint problems of route and charging strategies for electric vehicles along with urban traffic congestion prevention. Accordingly, this work tackles the complex task of traffic management in urban areas during peak periods by using practical congestion prevention strategies that consider the characteristics of mixed traffic flows and the charging demands of electric vehicle users. Notably, we apply support vector regressions to compute the charging time at each charging point and the traverse time of an electric vehicle at each road segment/intersection, based on historical traffic data. The simulation results reveal that the proposed algorithms are feasible because they can avoid possible occurrences of traffic congestion during rush hours and provide the routes and charging options that are chosen by electric vehicle users.

Copyright
© 2021 The Authors. Published by Atlantis Press B.V.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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<Previous Article In Issue
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Journal
International Journal of Computational Intelligence Systems
Volume-Issue
14 - 1
Pages
1714 - 1727
Publication Date
2021/06/12
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
10.2991/ijcis.d.210608.001How to use a DOI?
Copyright
© 2021 The Authors. Published by Atlantis Press B.V.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

risenwbib
TY  - JOUR
AU  - Chenn-Jung Huang
AU  - Kai-Wen Hu
AU  - Hsing Yi Ho
AU  - Bing Zhen Xie
AU  - Chien-Chih Feng
AU  - Hung-Wen Chuang
PY  - 2021
DA  - 2021/06/12
TI  - A Distributed Urban Traffic Congestion Prevention Mechanism for Mixed Flow of Human-Driven and Autonomous Electric Vehicles
JO  - International Journal of Computational Intelligence Systems
SP  - 1714
EP  - 1727
VL  - 14
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.210608.001
DO  - 10.2991/ijcis.d.210608.001
ID  - Huang2021
ER  -