Book Your Green Wave: Exploiting Navigation Information for Intelligent Traffic Signal Control
Traffic congestion alleviation around intersections has been a growing challenge, and a competent traffic signal control scheme plays a pivotal role in intelligent transportation systems. Recent studies using deep reinforcement learning techniques have shown promising results for traffic signal control, but they only focus on extracting features from traffic conditions of isolated or adjacent intersections. In this work, the authors employed navigation information for traffic signal control, greatly enriching the features for traffic signal control with deep reinforcement learning. In addition, the authors are the first to propose a novel scheme DeepNavi to exploit the temporal-spatial relations from numerous navigation routes and extract dynamic real-time and future traffic features. The authors tested the authors' scheme on a challenging real-world traffic dataset with 16 intersections in a residential district of Hangzhou, China. Extensive experiments were conducted and the results demonstrated that the authors' DeepNavi scheme achieves superior performance over five popular and state-of-the-art baseline methods on different metrics, including queue length, speed, travel time and accumulative waiting time. In addition, with the authors' method, vehicles suffer the least red lights and enjoy the most green waves, which further validates that the authors' scheme greatly relieves the congestion and provides excellent experience for drivers. Simulations with different penetration levels of navigation routes showed that even with only part of navigation routes available in the traffic network, the authors' scheme can obtain superior performance, further demonstrating the effectiveness and feasibility of DeepNavi.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/00189545
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Supplemental Notes:
- Copyright © 2022, IEEE.
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Authors:
- Cao, Miaomiao
- Li, Victor O K
- Shuai, Qiqi
- Publication Date: 2022-8
Language
- English
Media Info
- Media Type: Web
- Features: References;
- Pagination: pp 8225-8236
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Serial:
- IEEE Transactions on Vehicular Technology
- Volume: 71
- Issue Number: 8
- Publisher: Institute of Electrical and Electronics Engineers (IEEE)
- ISSN: 0018-9545
- Serial URL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=25
Subject/Index Terms
- TRT Terms: Intelligent control systems; Machine learning; Navigation; Traffic signal control systems
- Subject Areas: Highways; Operations and Traffic Management; Vehicles and Equipment;
Filing Info
- Accession Number: 01855496
- Record Type: Publication
- Files: TRIS
- Created Date: Aug 23 2022 9:10AM