OPTIMIZATION OF URBAN MULTI-INTERSECTION TRAFFIC FLOW VIA Q-LEARNING

Simple item page
dc.contributor.authorYit, Kwong Chin
dc.contributor.authorHeng, Jin Tham
dc.contributor.authorN.S.V. Kameswara Rao
dc.contributor.authorNurmin Bolong
dc.contributor.authorTze Kin Teo (Kenneth)
dc.date.accessioned2023-06-06T06:57:55Z
dc.date.available2023-06-06T06:57:55Z
dc.date.issued2013
dc.descriptionModeling, Simulation & Computing Laboratory, Material & Mineral Research Unit School of Engineering and Information Technology, Universiti Malaysia Sabah, Malaysia E-mail: 1msclab@ums.edu.my and 2ktkteo@ieee.org Keywords: Reinforcement Learning, Q-Learning, Traffic Networks, Traffic Signal Timing Plan Management, Multi-Agents Systems
dc.description.abstractCongestions of the traffic flow within the urban traffic network have been a challenging task for all the urban developers. Many approaches have been introduced into the current system to solve the traffic congestion problems. Reconfiguration of the traffic signal timing plan has been carried out through implementation of different techniques. However, dynamic characteristics of the traffic flow increase the difficulties towards the ultimate solutions. Thus, traffic congestions still remain as unsolvable problems to the current traffic control system. In this study, artificial intelligence method has been introduced in the traffic light system to alter the traffic signal timing plan to optimize the traffic flows. Q-learning algorithm in this study has enhanced the traffic light system with learning ability. The learning mechanism of Q-learning enables traffic light intersections to release itself from traffic congestions situation. Adjacent traffic light intersections will work independently and yet cooperate with each others to a common goal of ensuring the fluency of the traffic flows within the traffic network. The simulated results show that the Q Learning algorithm is able to learn from the dynamic traffic flow and optimize the traffic flow accordingly.
dc.identifier.urihttps://digitallibrary.peninsulacollege.edu.my/handle/123456789/333
dc.language.isoen
dc.titleOPTIMIZATION OF URBAN MULTI-INTERSECTION TRAFFIC FLOW VIA Q-LEARNING
dc.typeArticle
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Chin Yit Kwong - Optimization Of Urban Multi-Intersection Traffic Flow Via Q-Learning.pdf
Size:
280.91 KB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
license.txt
Size:
738 B
Format:
Item-specific license agreed to upon submission
Description:
Download
Collections
Article Writing