Reinforcement Learning Approach for Traffic Congestion Prediction Empowered with Explainable Artificial Intelligence (XAI)

Abstract

Traffic congestion in urban areas is an insistent challenge, increasing pollution levels and economic inefficiencies while reducing quality of life. Traditional traffic management systems, largely static and based on rules of thumb, struggle to dynamically adapt to ever-changing to solve the urban traffic problems. This work introduces a novel predictive framework using Reinforcement Learning enhanced with Explainable Artificial Intelligence techniques to boost both the interpretability and effectiveness of the model. A key hurdle involves securely transmitting data between the machine learning model and cloud servers, facilitated via Blockchain based smart contracts. The proposed RL approach augmented with RL-XAI represents the solution to strengthen security, privacy, and accuracy when detecting traffic jams. By using BC and RL and then evaluating outcomes through XAI, this approach achieves significantly more precise predictions and a reduced missing data rate relative to conventional Machine Learning methods and enhances security, reliability, and overall accuracy by a remarkable 5% compared to another approach.