AI in Traffic Management Predicting, Congestion Patterns and Optimization Signal Timing in Mega Urban Project
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2026
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| _version_ | 1866901364017201152 |
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| author | Amarsinh B. Landage Sakshi S. Rokade Aniket N. Manapure Thameshwari M. Raut |
| author_facet | Amarsinh B. Landage Sakshi S. Rokade Aniket N. Manapure Thameshwari M. Raut |
| contents | Urban traffic congestion has become a critical infrastructural challenge in rapidly expanding metropolitan areas, resulting in elevated fuel consumption, prolonged travel times, and heightened vehicular emissions. Conventional fixed-time traffic signal systems are structurally inadequate to respond to the dynamic and heterogeneous vehicle flow characteristics of modern urban intersections. This paper presents the development and validation of an AI-based smart traffic management system that integrates a Random Forest regression model with a proportional adaptive signal timing algorithm to predict traffic congestion patterns and dynamically optimize green-signal duration. Real-world traffic survey data was collected from Sitabuldi Junction, Nagpur — a major four-road urban intersection — across eight 15-minute time slots during peak morning (09:00–10:00 AM) and evening (17:00–18:00 PM) hours, encompassing six vehicle categories and totalling 12,164 observed vehicles. The Random Forest model, trained on a 70/30 split, achieved a Coefficient of Determination (R²) of 0.79 and a Mean Absolute Error (MAE) of 54 vehicles, demonstrating reliable predictive capability under mixed-traffic conditions. The adaptive signal timing system allocates green time proportionally using the formula G_i = (V_i / ΣV) × C, with a 120-second cycle and enforced bounds of 10–60 seconds. Simulation results confirm dynamic green-time allocation in the range of 25–38 seconds per cycle, with Roads C and D receiving preferential timing due to higher predicted loads. The proposed framework significantly outperforms fixed-time signal systems in terms of queue reduction, traffic distribution equity, and intersection throughput — providing a scalable, computationally efficient blueprint for intelligent urban traffic control. |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_20067303 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | AI in Traffic Management Predicting, Congestion Patterns and Optimization Signal Timing in Mega Urban Project Amarsinh B. Landage Sakshi S. Rokade Aniket N. Manapure Thameshwari M. Raut AI Traffic Management Random Forest Adaptive Signal Timing Congestion Prediction Machine Learning Smart Intersection Urban Mobility Urban traffic congestion has become a critical infrastructural challenge in rapidly expanding metropolitan areas, resulting in elevated fuel consumption, prolonged travel times, and heightened vehicular emissions. Conventional fixed-time traffic signal systems are structurally inadequate to respond to the dynamic and heterogeneous vehicle flow characteristics of modern urban intersections. This paper presents the development and validation of an AI-based smart traffic management system that integrates a Random Forest regression model with a proportional adaptive signal timing algorithm to predict traffic congestion patterns and dynamically optimize green-signal duration. Real-world traffic survey data was collected from Sitabuldi Junction, Nagpur — a major four-road urban intersection — across eight 15-minute time slots during peak morning (09:00–10:00 AM) and evening (17:00–18:00 PM) hours, encompassing six vehicle categories and totalling 12,164 observed vehicles. The Random Forest model, trained on a 70/30 split, achieved a Coefficient of Determination (R²) of 0.79 and a Mean Absolute Error (MAE) of 54 vehicles, demonstrating reliable predictive capability under mixed-traffic conditions. The adaptive signal timing system allocates green time proportionally using the formula G_i = (V_i / ΣV) × C, with a 120-second cycle and enforced bounds of 10–60 seconds. Simulation results confirm dynamic green-time allocation in the range of 25–38 seconds per cycle, with Roads C and D receiving preferential timing due to higher predicted loads. The proposed framework significantly outperforms fixed-time signal systems in terms of queue reduction, traffic distribution equity, and intersection throughput — providing a scalable, computationally efficient blueprint for intelligent urban traffic control. |
| title | AI in Traffic Management Predicting, Congestion Patterns and Optimization Signal Timing in Mega Urban Project |
| topic | AI Traffic Management Random Forest Adaptive Signal Timing Congestion Prediction Machine Learning Smart Intersection Urban Mobility |
| url | https://doi.org/10.5281/zenodo.20067303 |