A Self-Organizing Map-Based Adaptive Traffic Light Control System with Reinforcement Learning

Ying Cih Kao, Cheng Wen Wu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Increasing urban congestion is a common problem in big cities all over the world. Some Adaptive traffic control (ATC) systems have been proposed to reduce the total travel delay time, which is the main factor of congestion cost. However, previous solutions need a great number of sensors and hardware devices, which are hard to deploy. Fortunately, the advent of advanced Internet of Things (IoT) has made possible more effective and efficient solutions for the congestion issue. Besides, with the availability of the Machine Learning (ML) models, there is hope that the ATC system can be improved with the IoT approach, adopting the ML models. In this paper, we propose a machine learning model for adaptive traffic light control system. First, we assume traffic data is collected from internet-connected IOT sensing devices in vehicles on the roads. Next, the proposed machine learning model receives the data analyzed in cloud, and generates an optimal traffic light period as output. Finally, the optimal traffic light period is transformed to traffic light setting signals to be delivered to the IoT actuating devices in the crossroads. For verifying the proposed model, we build a traffic simulator. For a 24-hour simulated period, the proposed model reduces 55.7% waiting time and 12.76% maximum road occupancy on average, as compared with the Fixed Traffic Light Control System (FTLCS). We also simulate different traffic levels, and our model performs consistently better than the FTLCS in the overall waiting time and maximum road occupancy. The experimental results show that the proposed model is able to alleviate the traffic congestion problem.

Original languageEnglish
Title of host publicationConference Record of the 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018
EditorsMichael B. Matthews
PublisherIEEE Computer Society
Pages2060-2064
Number of pages5
ISBN (Electronic)9781538692189
DOIs
Publication statusPublished - 2019 Feb 19
Event52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018 - Pacific Grove, United States
Duration: 2018 Oct 282018 Oct 31

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
Volume2018-October
ISSN (Print)1058-6393

Conference

Conference52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018
CountryUnited States
CityPacific Grove
Period18-10-2818-10-31

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Computer Networks and Communications

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