Intelligent Transportation System for Sustainable and Efficient Urban Mobility: Machine Learning Approach for Traffic Flow Prediction

Intelligent transportation systems (ITSs) offer an innovative solution to the escalating challenges of urban mobility in growing cities. As urbanization continues to rise, cities grapple with congestion, prolonged commutes, pollution, and a deteriorating quality of life. ITS aims to revolutionize urban transportation by making it more sustainable, efficient, and user-friendly. It enhances public transportation with improved scheduling, payment systems, and increased connectivity between various modes of transport. Artificial intelligence plays a pivotal role within ITS, particularly in predicting and optimizing traffic flow. Advanced machine learning (ML) models and data analytics provide real time, precise traffic flow predictions, leading to better traffic management and reduced congestion. AI-driven traffic flow prediction utilizes data from sensors, cameras, and connected vehicles to enable efficient traffic signal control, dynamic route planning, and adaptive infrastructure development. Successful implementation relies on robust data collection, model training, and seamless integration with urban infrastructure. As cities confront urbanization and traffic congestion challenges, the synergy between artificial intelligence and ITS holds great promise for creating more intelligent, efficient, and sustainable urban environments. This paper presents various ML algorithms for traffic flow prediction, with a focus on using historical and real-time data to enhance traffic control, alleviate congestion, reduce emissions, and provide a superior commuting experience for urban residents. With urban populations continuing to grow, the role of ML in traffic flow prediction becomes a critical tool for achieving sustainable and smarter urban transportation systems.