Title: Research on coordinated control method of urban traffic based on neural network

Authors: Lede Niu; Mei Pan

Addresses: School of Tourism and Geographical Sciences, Yunnan Normal University, Kunming, 650500, China ' School of Tourism and Geographical Sciences, Yunnan Normal University, Kunming, 650500, China

Abstract: Due to the complexity of data in urban traffic coordination and control, there is a lack of relevance between data nodes of existing coordination methods, which leads to urban traffic holdup and congestion. A coordinated urban traffic control method based on neural network is proposed. The traffic flow prediction model is constructed to predict and calculate the urban traffic flow, green signal ratio, phase and period. The coordinated control method based on neural network is used to fine-tune the traffic signal, green signal ratio, phase and cycle, so as to improve the traffic capacity at the intersection during the peak period, and finally realise the coordinated control of urban traffic. The simulation results show that the proposed method can effectively alleviate the traffic capacity at intersections, and the time required for the method to run the whole process is less, which indicates that the proposed method is effective and reliable.

Keywords: neural network; urban traffic control; signal light coordination; simulation.

DOI: 10.1504/IJICA.2022.121385

International Journal of Innovative Computing and Applications, 2022 Vol.13 No.1, pp.18 - 26

Received: 07 Sep 2019
Accepted: 21 Apr 2020
Published online: 10 Mar 2022 *