Calibration-free Traffic Signal Control Method Using Machine Learning Approaches

Liang Zhang; Wei Hua Lin

doi:10.1109/ICECET55527.2022.9873047

Calibration-free Traffic Signal Control Method Using Machine Learning Approaches

Liang Zhang, Wei Hua Lin

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 Scopus citations

Abstract

Many existing traffic signal control strategies are operated with data from roadside surveillance systems. In recent years, vehicle-based data have become more and more accessible for various applications. In this paper, we propose a calibration-free traffic signal control scheme using vehicle-based data as input. Traffic conditions are characterized as discrete queue cycle state (DQCS) which are then used as input to the calibration-free traffic signal control scheme with the reinforcement learning approach. The k-nearest neighbor algorithm is applied in our calibration-free model. The effectiveness of the proposed model is examined with different traffic scenarios.

Original language	English (US)
Title of host publication	International Conference on Electrical, Computer, and Energy Technologies, ICECET 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665470872
DOIs	https://doi.org/10.1109/ICECET55527.2022.9873047
State	Published - 2022
Event	2022 IEEE International Conference on Electrical, Computer, and Energy Technologies, ICECET 2022 - Prague, Czech Republic Duration: Jul 20 2022 → Jul 22 2022

Publication series

Name	International Conference on Electrical, Computer, and Energy Technologies, ICECET 2022

Conference

Conference	2022 IEEE International Conference on Electrical, Computer, and Energy Technologies, ICECET 2022
Country/Territory	Czech Republic
City	Prague
Period	7/20/22 → 7/22/22

Keywords

calibration-free model
machine learning
queue length
traffic signal control

ASJC Scopus subject areas

Artificial Intelligence
Computer Science Applications
Computer Vision and Pattern Recognition
Energy Engineering and Power Technology
Electrical and Electronic Engineering
Media Technology
Instrumentation

Access to Document

10.1109/ICECET55527.2022.9873047

Cite this

Zhang, L., & Lin, W. H. (2022). Calibration-free Traffic Signal Control Method Using Machine Learning Approaches. In International Conference on Electrical, Computer, and Energy Technologies, ICECET 2022 (International Conference on Electrical, Computer, and Energy Technologies, ICECET 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICECET55527.2022.9873047

Calibration-free Traffic Signal Control Method Using Machine Learning Approaches. / Zhang, Liang; Lin, Wei Hua.
International Conference on Electrical, Computer, and Energy Technologies, ICECET 2022. Institute of Electrical and Electronics Engineers Inc., 2022. (International Conference on Electrical, Computer, and Energy Technologies, ICECET 2022).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Zhang, L & Lin, WH 2022, Calibration-free Traffic Signal Control Method Using Machine Learning Approaches. in International Conference on Electrical, Computer, and Energy Technologies, ICECET 2022. International Conference on Electrical, Computer, and Energy Technologies, ICECET 2022, Institute of Electrical and Electronics Engineers Inc., 2022 IEEE International Conference on Electrical, Computer, and Energy Technologies, ICECET 2022, Prague, Czech Republic, 7/20/22. https://doi.org/10.1109/ICECET55527.2022.9873047

Zhang L, Lin WH. Calibration-free Traffic Signal Control Method Using Machine Learning Approaches. In International Conference on Electrical, Computer, and Energy Technologies, ICECET 2022. Institute of Electrical and Electronics Engineers Inc. 2022. (International Conference on Electrical, Computer, and Energy Technologies, ICECET 2022). doi: 10.1109/ICECET55527.2022.9873047

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abstract = "Many existing traffic signal control strategies are operated with data from roadside surveillance systems. In recent years, vehicle-based data have become more and more accessible for various applications. In this paper, we propose a calibration-free traffic signal control scheme using vehicle-based data as input. Traffic conditions are characterized as discrete queue cycle state (DQCS) which are then used as input to the calibration-free traffic signal control scheme with the reinforcement learning approach. The k-nearest neighbor algorithm is applied in our calibration-free model. The effectiveness of the proposed model is examined with different traffic scenarios.",

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AB - Many existing traffic signal control strategies are operated with data from roadside surveillance systems. In recent years, vehicle-based data have become more and more accessible for various applications. In this paper, we propose a calibration-free traffic signal control scheme using vehicle-based data as input. Traffic conditions are characterized as discrete queue cycle state (DQCS) which are then used as input to the calibration-free traffic signal control scheme with the reinforcement learning approach. The k-nearest neighbor algorithm is applied in our calibration-free model. The effectiveness of the proposed model is examined with different traffic scenarios.

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BT - International Conference on Electrical, Computer, and Energy Technologies, ICECET 2022

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Calibration-free Traffic Signal Control Method Using Machine Learning Approaches

Abstract

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Keywords

ASJC Scopus subject areas

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