International Journal of Advanced Research in Computer and Communication Engineering

Abstract: Intelligent Transportation Systems (ITS) integrate advanced sensing, communication, and computational technologies to enhance traffic efficiency, safety, and sustainability. With the rapid growth of urban traffic, traditional traffic management approaches are no longer sufficient to handle dynamic and complex transportation scenarios. This paper presents an intelligent transportation framework that utilizes Machine Learning and optimization algorithms for real-time traffic analysis and decision-making. K-means clustering is employed to identify traffic density patterns, while Decision Tree and Random Forest algorithms are used for traffic congestion and accident prediction. Shortest Path algorithms such as Dijkstra and A* are applied for dynamic route optimization based on real-time traffic conditions. Simulation results demonstrate that the proposed approach reduces average travel time, improves traffic flow, and enhances road safety. The study highlights the effectiveness of algorithm-driven ITS solutions in supporting smart city transportation and sustainable mobility.

Keywords: Intelligent Transportation Systems, Machine Learning Algorithms, Traffic Prediction, Route Optimization, Smart Mobility.

Downloads: | DOI: 10.17148/IJARCCE.2026.15212