Collision Detection and Prevention in a Proposed Road Traffic Flow Model by Integrating the IDM Model

Tesis Doctoral / Disertación , 2024 , 114 Páginas

Ciencias de computación

This book presents an in-depth study on modeling collision avoidance systems in road traffic, leveraging advances in machine learning and informed neural networks. It introduces a novel macroscopic traffic flow model based on Lighthill-Whitham-Richards (LWR) in 1D and 2D to capture longitudinal and lateral traffic flows. RBF, collocation B-spline and PINN methods were used for numerical resolution, providing insights into traffic dynamics and collision phenomena. Using the SUMO (Simulation of Urban Mobility) platform, extensive data from the proposed model were collected to train classifiers such as logistic regression, gradient boosting, AdaBoost and SVM to predict collisions well. To mitigate the high number of collisions, the IDM (Intelligent Driver Model) model was properly integrated, improving the behavior and promoting traffic safety.

• Autores: Mourad Haddioui (Autor), Youssef Qaraai (Autor)
• Año de publicación: 2024
• Páginas: 114
• No. de catálogo: V1597508
• ISBN (PDF): 9783389140932
• ISBN (Libro): 9783389140949
• Idioma: Inglés
• Etiqueta: Simulation du trafic routier, Prévention des collisions, Apprentissage automatique, Réseaux de neurones informés par la physique, Modèles macroscopiques de flux de trafic, Simulation de la mobilité urbaine, Modèle de conducteur intelligent, Dynamique des flux de trafic, Méthodes de résolution numérique, Sécurité routière, Apprentissage profond, Modèles bidimensionnels de trafic, road traffic, machine learning, informed neural networks, novel macroscopic traffic flow model, Lighthill-Whitham-Richards (LWR), SUMO (Simulation of Urban Mobility) platform, logistic regression, gradient boosting, AdaBoost, SVM, IDM (Intelligent Driver Model), traffic safety