| Title |
Study on Intersection Signal Optimization Using Tabu Search-Ant
Colony Algorithm |
| Authors |
안홍기(An, Hong Ki);김동선(Kim, Dong Sun) |
| DOI |
https://doi.org/10.12652/Ksce.2025.45.3.0385 |
| Keywords |
교차로 최적화, Tabu Search-Ant Colony algorithm, CO2, 다목적함수 최적화 Intersection optimization, Tabu Search-Ant Colony algorithm, CO2, Multi-objective optimization |
| Abstract |
Congestion or delays occurring at intersections can significantly impact adjacent roadways, potentially leading to widespread urban
traffic congestion. Accordingly, intersections should not be viewed merely as points of connection between roads, but rather as critical
control nodes within the broader transportation system. Minimizing congestion at intersections is thus a major challenge for
transportation engineers. In recent years, growing interest in artificial intelligence(AI) algorithms has led to increased research on
intelligent traffic signal control systems aimed at enhancing intersection performance. Many studies have focused on multi-objective
optimization models employing AI-based algorithms such as Genetic Algorithms(GA) and Particle Swarm Optimization(PSO),
targeting reductions in travel time, delays, and the number of stops, or improvements in intersection capacity. This study proposes a
hybrid Tabu Search-Ant Colony Optimization algorithm that incorporates CO2 emissions as a key objective within a multi-objective
framework. The proposed algorithm was applied to a signalized intersection in Alor Setar, Malaysia, and its performance was
evaluated using SIDRA analysis. The optimized signal cycle length of 116 seconds resulted in a 20 % reduction in queue length, an
18 % reduction in CO2 emissions, a 25 % decrease in the number of stops, and a 15 % improvement in intersection capacity. These
findings provide empirical evidence that the proposed algorithm is effective in achieving sustainable and efficient traffic signal control. |