AI-Enhanced Adaptive Traffic Management System for Urban Congestion Reduction

Thesis Abstract

Urban traffic congestion remains a pervasive challenge in metropolitan areas worldwide, resulting in significant economic losses, increased environmental pollution, and diminished quality of urban life. Conventional traffic management systems, often based on static signal timings and heuristics, fail to adapt effectively to dynamic traffic conditions, leading to persistent congestion during peak hours. The advent of artificial intelligence (AI) presents an opportunity to develop intelligent, adaptive traffic control systems that respond in real-time to fluctuating traffic patterns. This study aims to design, develop, and evaluate an AI-enhanced adaptive traffic management system intended to mitigate urban congestion. The primary objectives are to (1) analyze current traffic management limitations, (2) develop a machine learning-based model capable of predicting traffic flow, and (3) implement an adaptive signal control mechanism responsive to live data feeds. Employing a mixed-methods research design, the study combines quantitative modeling with qualitative system assessment. The population of the study encompasses traffic sensor data collected from 15 major intersections within the metropolitan area over a period of one year, totaling approximately 10 million data points. A purposive sampling approach was used to select critical intersections characterized by high congestion levels and data completeness. Data collection instruments include inductive loop detectors, camera feeds, and real-time GPS data from connected vehicles, processed through a centralized data aggregation platform. The machine learning models utilized include Long Short-Term Memory (LSTM) networks and Random Forest regression to forecast short-term traffic flow, complemented by reinforcement learning algorithms for signal timing optimization. Data analysis involves training and validating predictive models using 70% of the collected data, applying cross-validation techniques to ensure robustness. The models’ predictive performance will be evaluated via metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R-squared values. The effectiveness of the adaptive control mechanism will be assessed through simulation-based analysis employing the Simulation of Urban Mobility (SUMO) platform, comparing metrics such as average delay time, queue length, and vehicle throughput with baseline fixed-timing systems. Additionally, this study incorporates a comparative analysis of AI-based predictions with traditional traffic flow models, such as the Bureau of Public Roads (BPR) function, to establish the superiority of AI methods. Expected findings suggest that the AI-enhanced system will significantly outperform conventional models, reducing average congestion-related delays by up to 30% and improving traffic throughput at key intersections. The adaptive signal control mechanism will demonstrate higher responsiveness to sudden traffic fluctuations, leading to smoother traffic flow and decreased pollution emissions from idling vehicles. The study aims to contribute novel insights into the application of deep learning and reinforcement learning techniques within urban traffic management, bridging gaps in existing literature regarding real-time AI applications for congestion mitigation. In conclusion, this research underscores the potential of integrating advanced AI techniques into urban traffic systems to enhance their responsiveness and efficiency. The study recommends policy adoption of AI-driven adaptive control in urban planning frameworks, alongside infrastructure investments to facilitate real-time data collection. Future research should focus on expanding the scope of AI models to incorporate multi-modal transportation data and exploring scalable implementations across diverse urban settings. Overall, the findings promise to inform policymakers, urban planners, and traffic engineers seeking sustainable and intelligent solutions to urban congestion challenges.