@inproceedings{4b5c066baec74907b5fa8041f34a7882,
title = "Calibration-free Traffic Signal Control Method Using Machine Learning Approaches",
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.",
keywords = "calibration-free model, machine learning, queue length, traffic signal control",
author = "Liang Zhang and Lin, \{Wei Hua\}",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Conference on Electrical, Computer, and Energy Technologies, ICECET 2022 ; Conference date: 20-07-2022 Through 22-07-2022",
year = "2022",
doi = "10.1109/ICECET55527.2022.9873047",
language = "English (US)",
series = "International Conference on Electrical, Computer, and Energy Technologies, ICECET 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "International Conference on Electrical, Computer, and Energy Technologies, ICECET 2022",
}