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Article

Adaptation Scheduling for Urban Traffic Lights via FNT-Based Prediction of Traffic Flow

Shandong Provincial Key Laboratory of Network Based Intelligent Computing, University of Jinan, Jinan 250022, China
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Academic Editor: Keemin Sohn
Electronics 2022, 11(4), 658; https://doi.org/10.3390/electronics11040658
Received: 28 December 2021 / Revised: 17 February 2022 / Accepted: 18 February 2022 / Published: 20 February 2022
(This article belongs to the Special Issue AI-Based Transportation Planning and Operation, Volume II)
By linking computational intelligence technology directly to urban transportation systems, a framework for scheduling traffic lights is proposed to enhance their flexibility in adaptation to traffic fluctuation. First, based on the flexible neural tree (FNT) theory, an algorithm for predicting the traffic flow is designed to obtain the variance tendency of traffic load. After that, a strategy for adjusting the duration of traffic signal cycle is designed to tackle the problem of overload or lightweight traffic flow in the next-time frame. While predetermining the duration of signal cycle in the next-time frame, from a utilization perspective, an elastic-adaption strategy for scheduling the separate phase’s green traffic lights is derived from the analytical solution, which is obtained from a designed trade-off scheduling optimization problem to increase the adaptability for the upcoming traffic flow. The experiment results show that the proposed framework can effectively reduce the delay and stopping rate of vehicles, and improves the adaptability for the upcoming traffic flow. View Full-Text
Keywords: traffic light scheduling; traffic flow prediction; duration adjustment of signal cycle; flexible neural tree; trade-off scheduling optimization traffic light scheduling; traffic flow prediction; duration adjustment of signal cycle; flexible neural tree; trade-off scheduling optimization
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MDPI and ACS Style

Han, S.-Y.; Sun, Q.-W.; Yang, X.-H.; Han, R.-Z.; Zhou, J.; Chen, Y.-H. Adaptation Scheduling for Urban Traffic Lights via FNT-Based Prediction of Traffic Flow. Electronics 2022, 11, 658. https://doi.org/10.3390/electronics11040658

AMA Style

Han S-Y, Sun Q-W, Yang X-H, Han R-Z, Zhou J, Chen Y-H. Adaptation Scheduling for Urban Traffic Lights via FNT-Based Prediction of Traffic Flow. Electronics. 2022; 11(4):658. https://doi.org/10.3390/electronics11040658

Chicago/Turabian Style

Han, Shi-Yuan, Qi-Wei Sun, Xiao-Hui Yang, Rui-Zhi Han, Jin Zhou, and Yue-Hui Chen. 2022. "Adaptation Scheduling for Urban Traffic Lights via FNT-Based Prediction of Traffic Flow" Electronics 11, no. 4: 658. https://doi.org/10.3390/electronics11040658

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