Detecting Car Accidents Based on Traffic Flow Measurements Using Machine Learning Techniques

This paper deals with the problem of detecting the occurrence of a car accident in an urban environment. Firstly, a model based on Cellular Automata is designed to simulate the traffic flow with its main features such as: multiple lanes, cars, traffic lights, buses and bus stops. Afterwards, machine learning techniques are trained with the traffic flow measurements considering both the normal and the situation in which the accident caused a partial closure of the lanes. Several machine learning techniques results are presented to several car breaking scenarios.

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