TRAFFIC CONGESTION MODELING AND MANAGEMENT USING INTELLIGENT TRANSPORTATION TECHNOLOGIES

Abstract

Traffic congestion has become a major challenge in urban transportation systems due to rapid urbanization and increasing vehicle ownership. Conventional traffic management methods are often inadequate to handle complex and dynamic traffic conditions. This paper presents a comprehensive approach to traffic congestion modeling and management using intelligent transportation technologies. Advanced data collection techniques, real-time monitoring, and intelligent decision-making frameworks are employed to analyze traffic flow characteristics. The proposed system integrates traffic sensing, data analytics, and adaptive control strategies to improve network performance. Key congestion indicators such as travel time, queue length, and delay are evaluated. Simulation-based analysis is conducted to assess system effectiveness. The results demonstrate that intelligent transportation technologies significantly reduce congestion levels. The study supports the adoption of smart traffic management solutions for sustainable urban mobility.