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July 15, Tuesday
12:00 – 14:00

This work presents research in the field of machine learning and transportation studies. Urban Traffic Control System (UTCS), which rely on a network of sensors, aim to describe real-time traffic in urban areas using a set of parameters and estimating them. Though the state of the art systems focus on data analysis, little has been done to date in terms of prediction. In this work, we describe a machine learning system for traffic flow management and control to address the problem of traffic regulation. This new algorithm is obtained by combining a Random Forests algorithm with an Adaboost algorithm as a weak learner. We show that o! ur algorithm performs relatively well on real data, and enables, according to the traffic flow evaluation model, to estimate and predict whether there is congestion or not at a given time at a given road intersection. The problem of avoiding traffic congestion can be solved by updating the signal timing of the traffic lights at an intersection in advance, based on the prediction of heavy traffic.