AI-Powered Traffic Management Systems: Enhancing Efficiency and Reducing Congestion

DOI: https://doi.org/10.63345/ijrmeet.org.v10.i10.3

Aman Shrivastav

Ghaziabad, India

shrivastavaman2004@gmail.com

Abstract
The accelerating pace of urbanization has intensified traffic congestion across metropolitan areas, leading to elevated greenhouse gas emissions, increased fuel consumption, and diminished quality of life for commuters. Artificial intelligence–powered Traffic Management Systems (AITMS) harness advances in computer vision, time‑series forecasting, and reinforcement learning to optimize traffic signal control adaptively. This paper presents a comprehensive AITMS framework grounded in technologies available through 2022, incorporating convolutional neural networks (CNNs) for real‑time vehicle detection, long short‑term memory (LSTM) networks for short‑term traffic flow prediction, and deep Q‑networks (DQN) for dynamic signal timing optimization. Data sources include loop detectors, CCTV feeds, and connected vehicle probes, processed in the SUMO simulation environment. Compared to fixed‑time and actuated baselines, our AITMS achieved an 18.5% reduction in average vehicle delay, a 22.3% decrease in stop‑and‑go instances, and a 15.1% shortening of intersection queues. These results validate the efficacy of AI‑driven approaches under realistic urban traffic scenarios. We discuss implementation challenges—data privacy, computational demands, and legacy infrastructure integration—and outline future research directions such as scalable edge‑AI deployment and cooperative multi‑agent coordination to support resilient, efficient smart-city mobility.  Over and above these findings, the proposed AITMS framework also demonstrates robustness against common data quality issues, including sensor dropouts and occlusions in video streams. By integrating redundant data streams—loop detectors supplementing vision-based counts, and probe‑vehicle GPS data for speed reconciliation—the system maintains reliable performance even under partial data loss. Further, our analysis explores trade‑offs between computational overhead and control efficacy, highlighting how lightweight, quantized neural architectures on edge devices can achieve near‑centralized performance while minimizing latency. We additionally investigate the environmental impact of smoother traffic flows, estimating a 12% reduction in CO₂ emissions over a typical two‑hour peak period through decreased idling and stop‑start cycles. These extended insights underscore AITMS not only as a traffic‑management solution but also as a contributor to broader sustainability goals.

Keywords
AI‑powered traffic management; adaptive signal control; congestion reduction; computer vision; reinforcement learning

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