An Optimal Dynamic Lane Reversal and Traffic Control Strategy for Autonomous Vehicles
This paper studies an optimal dynamic lane reversal and traffic control (DLRTC) strategy in the presence of autonomous vehicles (AVs). A centralized controller is set to change lane directions dynamically and regulate traffic flow on a motorway network. Through vehicle to infrastructure (V2I) communication, the roadside sensors can send lane reversal information and flow control actions to the AVs which can perform lane-changing behaviors and adjust travel speed. To model the traffic dynamics under DLRTC, the authors propose a novel multi-lane cell transmission model (CTM). A logit model is used to characterize the lane-changing behaviors under uncontrolled cases. A mixed integer linear programming model (MILP) is formulated for DLRTC, and optimal control actions are implemented in a framework of model predictive control (MPC). The numerical experiments based on the Ayer Rajah Expressway (AYE) in Singapore are conducted to demonstrate the effectiveness of the proposed methods. The results show that the DLRTC strategy can effectively reduce road congestion and achieve better system performance compared to the benchmark method.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
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Supplemental Notes:
- Copyright © 2022, IEEE.
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Authors:
- Chen, Shukai
- Wang, Hua
- Meng, Qiang
- Publication Date: 2022-4
Language
- English
Media Info
- Media Type: Web
- Features: References;
- Pagination: pp 3804-3815
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Serial:
- IEEE Transactions on Intelligent Transportation Systems
- Volume: 23
- Issue Number: 4
- Publisher: Institute of Electrical and Electronics Engineers (IEEE)
- ISSN: 1524-9050
- Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979
Subject/Index Terms
- TRT Terms: Autonomous vehicles; Highway traffic control; Managed lanes; Optimization; Traffic congestion
- Geographic Terms: Singapore
- Subject Areas: Highways; Operations and Traffic Management; Planning and Forecasting; Vehicles and Equipment;
Filing Info
- Accession Number: 01849420
- Record Type: Publication
- Files: TRIS
- Created Date: Jun 24 2022 10:54AM