Evolvable traffic signal control for intersection congestion alleviation with enhanced particle swarm optimisation
- Resource Type
- Conference
- Authors
- Chuo, Helen Sin Ee; Tan, Min Keng; Chong, Alex Chee Hoe; Chin, Renee Ka Yin; Teo, Kenneth Tze Kin
- Source
- 2017 IEEE 2nd International Conference on Automatic Control and Intelligent Systems (I2CACIS) Automatic Control and Intelligent Systems (I2CACIS), 2017 IEEE 2nd International Conference on. :92-97 Oct, 2017
- Subject
- Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Optimization
Particle swarm optimization
Traffic control
Timing
Mathematical model
Computational modeling
Automobiles
Flow Management
Particle Swarm Optimisation
Traffic Network Optimisation
Traffic Signal Timing Plan
- Language
Urban congestion in major cities of Malaysia is getting severe over decades with increasing active vehicles and travelling time on the road. Part of Intelligent Transportation Systems development involves advanced computation in traffic management to cope for the projecting congestion trend. This work simulates traffic system and develop an optimising algorithm to instruct the traffic signal timing plan. A multiple-intersection traffic system has been developed using probability and statistical model based on the real case traffic data collected from local traffic intersection. Enhanced particle swarm optimisation algorithm is developed to ensure result consistency with smaller variation. As a result, the algorithm suggested signal timing increases the average waiting time of non-congested directions by approximately 4.17% but reduces the queue length at congested junction significantly in order to even up the flow at intersections.