Incidents Prediction in Road Junctions Using Artificial Neural Networks

Sustainable Buildings and Cities

土木建筑工程

Hajji, Tarik^1 ; Alami Hassani, Aicha^1 ; Ouazzani Jamil, Mohammed^1

Laboratoire Systèmes et Environnements Durables (SED), Faculté des Sciences de l'Ingénieur (FSI), Université Privée de Fès (UPF), Fez, Morocco^1

关键词: Comparative studies;    Incident detection systems;    Learning database;    Monitoring system;    Moving objects;    Multiple cameras;    Prediction probabilities;    Road junction;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/353/1/012017/pdf
DOI  :  10.1088/1757-899X/353/1/012017

学科分类：土木及结构工程学

来源: IOP

【 摘 要 】

The implementation of an incident detection system (IDS) is an indispensable operation in the analysis of the road traffics. However the IDS may, in no case, represent an alternative to the classical monitoring system controlled by the human eye. The aim of this work is to increase detection and prediction probability of incidents in camera-monitored areas. Knowing that, these areas are monitored by multiple cameras and few supervisors. Our solution is to use Artificial Neural Networks (ANN) to analyze moving objects trajectories on captured images. We first propose a modelling of the trajectories and their characteristics, after we develop a learning database for valid and invalid trajectories, and then we carry out a comparative study to find the artificial neural network architecture that maximizes the rate of valid and invalid trajectories recognition.