TY  - JOUR
AU  - Garg, Tanya
AU  - Kaur, Gurjinder
PY  - 2026
DA  - 2026/04/06
TI  - A Hybrid GASAPSO Algorithm for Traffic Signal Delay Minimization
JO  - ICCK Transactions on Machine Intelligence
T2  - ICCK Transactions on Machine Intelligence
JF  - ICCK Transactions on Machine Intelligence
VL  - 2
IS  - 3
SP  - 144
EP  - 160
DO  - 10.62762/TMI.2025.542785
UR  - https://www.icck.org/article/abs/TMI.2025.542785
KW  - traffic management
KW  - traffic signal
KW  - Webster delay
KW  - GASAPSO
KW  - delay minimization
AB  - Traffic management is a crucial issue due to the massive increase in vehicles on the road. In order to improve traffic flow, reduce congestion, and ensure commuter safety at junctions, traffic light regulation is essential. This research aims to minimize delay and accident risk while maximizing vehicle throughput through intersections. In this paper, we introduced a new hybrid approach by combining various algorithms, which include a genetic algorithm (GA), Simulated Annealing (SA), and Particle Swarm Optimization (PSO) to reduce traffic delay at traffic lights. The Webster delay function has been used as a fitness function to evaluate the algorithmic performance. Our hybrid algorithm efficiently explores the solution space to pinpoint the ideal signal timings through extensive simulations and experiments, resulting in shorter vehicle wait times at traffic lights. Our proposed algorithm, GASAPSO, successfully reduced the traffic delay to 186.5 seconds at 250 iterations. Moreover, GASAPSO has shown the highest accuracy over other algorithms. PSO, GA, and SA work together to converge to superior solutions, increasing the overall effectiveness of the traffic control system. Finally, the performance of our proposed algorithm has been compared with the existing hybrid algorithms in the same context to validate our approach. The proposed algorithm has also been validated on real time traffic data for the Patiala City.
SN  - 3068-7403
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 

@article{Garg2026A,
  author = {Tanya Garg and Gurjinder Kaur},
  title = {A Hybrid GASAPSO Algorithm for Traffic Signal Delay Minimization},
  journal = {ICCK Transactions on Machine Intelligence},
  year = {2026},
  volume = {2},
  number = {3},
  pages = {144-160},
  doi = {10.62762/TMI.2025.542785},
  url = {https://www.icck.org/article/abs/TMI.2025.542785},
  abstract = {Traffic management is a crucial issue due to the massive increase in vehicles on the road. In order to improve traffic flow, reduce congestion, and ensure commuter safety at junctions, traffic light regulation is essential. This research aims to minimize delay and accident risk while maximizing vehicle throughput through intersections. In this paper, we introduced a new hybrid approach by combining various algorithms, which include a genetic algorithm (GA), Simulated Annealing (SA), and Particle Swarm Optimization (PSO) to reduce traffic delay at traffic lights. The Webster delay function has been used as a fitness function to evaluate the algorithmic performance. Our hybrid algorithm efficiently explores the solution space to pinpoint the ideal signal timings through extensive simulations and experiments, resulting in shorter vehicle wait times at traffic lights. Our proposed algorithm, GASAPSO, successfully reduced the traffic delay to 186.5 seconds at 250 iterations. Moreover, GASAPSO has shown the highest accuracy over other algorithms. PSO, GA, and SA work together to converge to superior solutions, increasing the overall effectiveness of the traffic control system. Finally, the performance of our proposed algorithm has been compared with the existing hybrid algorithms in the same context to validate our approach. The proposed algorithm has also been validated on real time traffic data for the Patiala City.},
  keywords = {traffic management, traffic signal, Webster delay, GASAPSO, delay minimization},
  issn = {3068-7403},
  publisher = {Institute of Central Computation and Knowledge}
}