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Title: Network-Wide Traffic Signal Control Using Bilinear System Modeling and Adaptive Optimization

Abstract

This study proposes a new multi-input multi-output optimal bilinear signal control method in which a bilinear dynamic model approximation is used to capture the nonlinear dynamics of the urban traffic networks. With signal green time splits as the control input and traffic delay changes as the output for each intersections in the network, a bilinear system model was developed, which, on the basis of linear system modeling, takes interactions among traffic delays and signal timing splits into consideration. Based on the bilinear system modeling framework, we conducted two steps in each time interval to derive traffic control strategies: (1) we used the normalized least-squared algorithm to estimate system parameters; and (2) we solved an online optimization problem to obtain the updated traffic control inputs for the signal timing that minimizes future traffic delays. We evaluated the proposed method in a microscopic traffic simulation environment (VISSIM) with a 35-intersection network of Bellevue city in Washington. Two different traffic demand patterns: (1) normal traffic demands; and (2) time-varying traffic demands were simulated to compare the performance of different control strategies. Experimental results show that (1) the proposed bilinear system model can better describe traffic system dynamics than linear-model based methods, such asmore » our previously developed linear-quadratic regulator control; and (2) the proposed method outperforms the state-of-the-art signal control strategies, namely the max-pressure and the self-organizing traffic light control methods. We have also shown that the proposed method is applicable to all other possible network layouts and signal controller phasing structures.« less

Authors:
 [1];  [2]; ORCiD logo [3]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [4]; ORCiD logo [5]
  1. Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
  2. Hong Kong University of Science and Technology (HKUST) (Hong Kong)
  3. Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
  4. Univ. of Virginia, Charlottesville, VA (United States)
  5. Univ. of Washington, Seattle, WA (United States)
Publication Date:
Research Org.:
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1897010
Grant/Contract Number:  
AC05-00OR22725
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
IEEE Transactions on Intelligent Transportation Systems
Additional Journal Information:
Journal Volume: 24; Journal Issue: 1; Journal ID: ISSN 1524-9050
Publisher:
IEEE
Country of Publication:
United States
Language:
English
Subject:
42 ENGINEERING; urban traffic network; traffic signal; bilinear control; multi-input multi-output (MIMO) system; VISSIM

Citation Formats

Wang, Hong, Zhu, Meixin, Hong, Wanshi, Wang, Chieh, Li, Wan, Tao, Gang, and Wang, Yinhai. Network-Wide Traffic Signal Control Using Bilinear System Modeling and Adaptive Optimization. United States: N. p., 2023. Web. doi:10.1109/tits.2022.3215537.
Wang, Hong, Zhu, Meixin, Hong, Wanshi, Wang, Chieh, Li, Wan, Tao, Gang, & Wang, Yinhai. Network-Wide Traffic Signal Control Using Bilinear System Modeling and Adaptive Optimization. United States. https://doi.org/10.1109/tits.2022.3215537
Wang, Hong, Zhu, Meixin, Hong, Wanshi, Wang, Chieh, Li, Wan, Tao, Gang, and Wang, Yinhai. 2023. "Network-Wide Traffic Signal Control Using Bilinear System Modeling and Adaptive Optimization". United States. https://doi.org/10.1109/tits.2022.3215537. https://www.osti.gov/servlets/purl/1897010.
@article{osti_1897010,
title = {Network-Wide Traffic Signal Control Using Bilinear System Modeling and Adaptive Optimization},
author = {Wang, Hong and Zhu, Meixin and Hong, Wanshi and Wang, Chieh and Li, Wan and Tao, Gang and Wang, Yinhai},
abstractNote = {This study proposes a new multi-input multi-output optimal bilinear signal control method in which a bilinear dynamic model approximation is used to capture the nonlinear dynamics of the urban traffic networks. With signal green time splits as the control input and traffic delay changes as the output for each intersections in the network, a bilinear system model was developed, which, on the basis of linear system modeling, takes interactions among traffic delays and signal timing splits into consideration. Based on the bilinear system modeling framework, we conducted two steps in each time interval to derive traffic control strategies: (1) we used the normalized least-squared algorithm to estimate system parameters; and (2) we solved an online optimization problem to obtain the updated traffic control inputs for the signal timing that minimizes future traffic delays. We evaluated the proposed method in a microscopic traffic simulation environment (VISSIM) with a 35-intersection network of Bellevue city in Washington. Two different traffic demand patterns: (1) normal traffic demands; and (2) time-varying traffic demands were simulated to compare the performance of different control strategies. Experimental results show that (1) the proposed bilinear system model can better describe traffic system dynamics than linear-model based methods, such as our previously developed linear-quadratic regulator control; and (2) the proposed method outperforms the state-of-the-art signal control strategies, namely the max-pressure and the self-organizing traffic light control methods. We have also shown that the proposed method is applicable to all other possible network layouts and signal controller phasing structures.},
doi = {10.1109/tits.2022.3215537},
url = {https://www.osti.gov/biblio/1897010}, journal = {IEEE Transactions on Intelligent Transportation Systems},
issn = {1524-9050},
number = 1,
volume = 24,
place = {United States},
year = {Sun Jan 01 00:00:00 EST 2023},
month = {Sun Jan 01 00:00:00 EST 2023}
}

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  • Wei, Hua; Zheng, Guanjie; Yao, Huaxiu
  • KDD '18: The 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
  • https://doi.org/10.1145/3219819.3220096

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