This paper proposes an adaptive traffic signal control (ATSC) system optimized using genetic algorithm (GA) to reduce travel delays at multiple intersections. The growing urban population has caused significant traffic congestion, leading to wasted time, higher costs, and fuel waste. The main aim is to enhance traffic optimization performance by improving ATSC signal timings at multiple intersections, optimized using GA. A $2\times 2$ grid traffic network is developed to represent the multi-agent network, and the GA-based traffic optimizer is formulated to optimize traffic signals based on the traffic inflow rate. The performance of the GA-based signal optimizer is evaluated and compared with pretimed traffic control based on the average delay and queue length of vehicles passing through the intersections in undersaturated and saturated traffic. The simulations results show a decrease of 4.7 % (1.7 s) in delay and 6.8 % (0.6 pcu) in queue length for GA-based traffic control during undersaturated traffic scenarios, and a decrease of 18.7 % (8.4 s) in delay and 14.1 % (1.8 pcu) in queue length during saturated traffic scenarios. These results highlight the effectiveness of the GA-based signal optimizer in managing traffic flow in a multi-agent-based signalized traffic network. This paper provided a valuable contribution to the field of traffic signal optimization, particularly in urban settings where congestion is a significant issue.
Adaptive Traffic Signal Control using Genetic Algorithm for a 2×2 Traffic Network
2024-08-26
551432 byte
Conference paper
Electronic Resource
English
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