Connected Urban Mobility: IoT-Based Traffic Prediction in Smart Cities

Machine learning (ML) optimizes IoT applications by predicting traffic patterns, energy consumption, and enhancing urban services. Smart city initiatives leverage ML for predictive maintenance and resource allocation. This study aims to develop a model that accurately forecasts traffic flow. The traffic environment encompasses all factors affecting traffic, including traffic signals, accidents, rallies, and road repairs that could lead to bottlenecks. With prior knowledge of vehicle congestion in critical areas, drivers and passengers can make informed decisions. Additionally, this model can be utilized in autonomous vehicles, the future of transportation. The exponential growth of traffic and the rise of big data transportation concepts drive the need for advanced prediction models. Current prediction techniques are insufficient for practical applications. The vast amount of data available for traffic flow forecasting is cumbersome and labor-intensive. In this work, we aim to evaluate transportation data with reduced complexity using machine learning and deep learning methods. The developed machine learning model will predict traffic flow, providing users with projected traffic information.