Collision Detection and Prevention in a Proposed Road Traffic Flow Model by Integrating the IDM Model
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Form:Einzelkauf Download
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Sprache:Englisch
Fr. 37.90
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Produktdetails
Format
Kopierschutz
Nein
Family Sharing
Nein
Text-to-Speech
Nein
Erscheinungsdatum
15.07.2025
Verlag
GRINSeitenzahl
114 (Printausgabe)
Dateigröße
2980 KB
Sprache
Englisch
EAN
9783389140932
Doctoral Thesis / Dissertation from the year 2024 in the subject Computer Science, , language: English, abstract: 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.
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