Hybrid Neural Network and Genetic Algorithm Approach for Adaptive Traffic Signal Timing Optimization
Nayana Mahajan1, Chirag Parekh2, Aradhya Bangal3, Amritpal Singh Banga4
1Dr. Nayana Mahajan, Professor, Department of Electronics & Computer Science, Vidyalankar Institute of Technology, Mumbai (M.H.), India.
2Chirag Parekh, Student, Department of Electronics & Computer Science, Vidyalankar Institute of Technology, Mumbai (M.H.), India.
3Aradhya Bangal, Department of Electronics & Computer Science, Vidyalankar Institute of Technology, Mumbai (M.H.), India.
4Amritpal Singh Banga, Student, Department of Electronics & Computer Science, Vidyalankar Institute of Technology, Mumbai (M.H.), India.
Manuscript received on 17 April 2026 | First Revised Manuscript received on 25 April 2026 | Second Revised Manuscript received on 06 May 2026 | Manuscript Accepted on 15 May 2026 | Manuscript published on 30 May 2026 | PP: 1-7 | Volume-16 Issue-2, May 2026 | Retrieval Number: 100.1/ijsce.B372116020526 | DOI: 10.35940/ijsce.B3721.16020526
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Urban traffic congestion remains one of the most pressing challenges in modern cities, causing significant losses in commute time, fuel consumption, and air quality. Traditional fixed-cycle signal control systems fail to adapt to the dynamic, unpredictable nature of urban traffic, leading to increased intersection delays and network-wide inefficiencies. This paper proposes a two-stage hybrid soft computing framework that combines a Multilayer Perceptron (MLP) Neural Network for multi-variate traffic volume prediction with a PyGAD-based Genetic Algorithm (GA) for adaptive signal timing optimization. In the first stage, the MLP model is trained on a synthetic dataset comprising 8,640 instances across four intersection archetypes — commercial, residential, business, and industrial — using 18 diverse input features spanning temporal, environmental, infrastructure, and historical lag categories. The MLP achieves a test R² value of 0.887, confirming strong generalization with minimal overfitting. In the second stage, the GA employs a three gene chromosome encoding green times and yellow duration, optimized using a composite fitness function with penalty terms to enforce realistic cycle constraints. Three representative urban scenarios — morning peak at a commercial intersection under rain, evening peak at a business intersection under clear conditions, and a weekend afternoon residential scenario — are evaluated. The proposed system achieves a consistent 20–25% reduction in average vehicle waiting time compared to conventional fixed-time signal control, demonstrating practical potential for deployment in Intelligent Transportation Systems (ITS).
Keywords: Traffic Signal Optimization, Neural Network, Genetic Algorithm, MLP, Py GAD, Soft Computing, ITS, Adaptive Control.
Scope of the Article: Artificial Intelligence (AI)