Traffic Prediction with Intelligent Transportation Systems

Traffic congestion on a global level is causing a significant hold-up to urban mobility, as represented by time for travel, fuel wastage, and environmental pollution. Traditional fixed-time traffic controls are unable to modify themselves to real-time traffic variations, necessitating advanced prediction models. This article examines how advanced machine- learning and deep-learning models, especially, Long Short- Term Memory (LSTM) networks, and hybrid CNN-LSTM architectures are effective in predicting and confronting traffic congestion. This study assessed the efficacy of AI- driven traffic predictions and timeliness through the comparison of real-time and historical traffic data provided by multiple sources. The experimental results demonstrate an evident improvement in these models ability to forecast and manage traffic congestion in such a way as to allow decision-makers in the field to be proactive. The contribution will enrich an intelligent transportation system (ITS) to smooth urban traffic flow while combating existing congestion issues.