A Study of Artificial Neural Network-Based Real-Time Traffic Signal Timing Design Model Utilizing Smart Intersection Data

Engineering Proceedings

article

Sang-Tae Oh¹  Jin-Tae Kim¹ 

[1] Department of Transportation Policy and Systems Engineering, Korea National University of Transportation

关键词: traffic control;    signal timings;    deep learning;    artificial intelligence;    simulation;    real time;   

DOI  :  10.3390/engproc2023036032

来源: mdpi

【 摘 要 】

The smart intersection (SI) systems, as they are named in the Republic of Korea, are part of the ITS services implemented under local government projects with financial support from the central government. They collect real-time traffic data available at signalized intersections with advanced detection systems for surveillance purposes only. A traffic signal method utilizing such valuable data has been desirable but unavailable as yet in practice. This paper proposes a new approach to designing traffic signal timings, reflecting the demand changing in real time, by utilizing SI surveillance data. The proposed artificial neural network model generates suitable traffic signal timings trained to be near optimum based on surveillance data for each directional movement.

【 授权许可】

Unknown