Please use this identifier to cite or link to this item: http://hdl.handle.net/10125/59570

A Deep Learning Based Model for Driving Risk Assessment

File Size Format  
0130.pdf 460.78 kB Adobe PDF View/Open

Item Summary

Title:A Deep Learning Based Model for Driving Risk Assessment
Authors:Bian, Yiyang
Lee, Chang Heon
Zhao, J Leon
Wan, Yibo
Keywords:Decision Support for Smart Cities
Decision Analytics, Mobile Services, and Service Science
driving risk, aggressive driving behavior, deep learning
Date Issued:08 Jan 2019
Abstract:In this paper a novel multilayer model is proposed for assessing driving risk. Studying aggressive behavior via massive driving data is essential for protecting road traffic safety and reducing losses of human life and property in smart city context. In particular, identifying aggressive behavior and driving risk are multi-factors combined evaluation process, which must be processed with time and environment. For instance, improper time and environment may facilitate abnormal driving behavior. The proposed Dynamic Multilayer Model consists of identifying instant aggressive driving behavior that can be visited within specific time windows and calculating individual driving risk via Deep Neural Networks based classification algorithms. Validation results show that the proposed methods are particularly effective for identifying driving aggressiveness and risk level via real dataset of 2129 drivers’ driving behavior.
Pages/Duration:10 pages
URI:http://hdl.handle.net/10125/59570
ISBN:978-0-9981331-2-6
DOI:10.24251/HICSS.2019.158
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
Appears in Collections: Decision Support for Smart Cities


Please email libraryada-l@lists.hawaii.edu if you need this content in ADA-compliant format.

This item is licensed under a Creative Commons License Creative Commons