Proposal of a Machine Learning Approach for Traffic Flow Prediction

Rapid global urbanization has led to a growing urban population, posing challenges in trans-portation management. Persistent issues such as traffic congestion, environmental pollution, and safety risks persist despite attempts to mitigate them, hindering urban progress. This paper fo-cuses on the critical need for accurate traffic flow forecasting, considered one of the main effec-tive solutions for containing traffic congestion in urban scenarios. The challenge of predicting traffic flow is addressed by proposing a two-level machine learning approach. The first level us-es an unsupervised clustering model to extract patterns from sensor-generated data, while the second level employs supervised machine learning models. Although the proposed approach requires the availability of data from traffic sensors to realize the training of the machine learn-ing models, it allows traffic flow prediction in urban areas without sensors. In order to verify the prediction capability of the proposed approach, a real urban scenario is considered