Abstract
Nowadays, traffic congestion has become a significant challenge in urban areas and densely populated cities. Real-time traffic signal control is an effective method to reduce traffic jams. This paper proposes a particle swarm optimisation with linearly decreasing weight (LDW-PSO) to tackle the signal intersection control problem, where a finite-interval model and an objective function are built to minimise spoilage time. The performance was evaluated in real-time simulation imitating a crowded intersection in Dalian city (in China) via the SUMO traffic simulator. The simulation results showed that the LDW-PSO outperformed the classical algorithms in this research, where queue length can be reduced by up to 20.4% and average waiting time can be reduced by up to 17.9%.
Highlights
Nowadays, with the industrialization of the world, the population in cities has grown exponentially
Refs. [20,21] applied new meta-heuristic algorithms to the traffic control problem and achieved significant results. This is the first of SL-particle swarm optimisation (PSO) and micro artificial system (MAIS) to be used as a traffic signal optimiser
Many algorithms applied in Traffic Signal Control System (TSCS) have been proposed and developed in recent years
Summary
With the industrialization of the world, the population in cities has grown exponentially. All control parameters in the fixed-cycle signal control system, including cycle length, phase duration, and sequence, are preset offline according to historical traffic flow information, regardless of the number of vehicles. The automatic model of adaptive signal control is designed to change the control strategy when traffic regimes change It depends on the use of traffic sensors and the real-time calculation of traffic flow. PSO is a well-known algorithm that performs fast convergence to optimal solutions [16] It is a highly desirable property for an optimal traffic signal cycle program, where new traffic signal schedules must be updated immediately when traffic regimes change.
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