Mid-term prediction of at-fault crash driver frequency using fusion deep learning with city-level traffic violation data-Reference-Cited by-同舟云学术

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Mid-term prediction of at-fault crash driver frequency using fusion deep learning with city-level traffic violation data

Published:2021-02 Issue: Volume:150 Page:105910
ISSN:0001-4575
Container-title:Accident Analysis & Prevention
language:en
Short-container-title:Accident Analysis & Prevention

Author:

Wu Yuan-Wei,Hsu Tien-Pen

Funder

Ministry of Science and Technology, Taiwan

National Taiwan University

Publisher

Elsevier BV

Subject

Public Health, Environmental and Occupational Health,Safety, Risk, Reliability and Quality,Human Factors and Ergonomics

Reference103 articles.

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3. Geographical unit based analysis in the context of transportation safety planning;Abdel-Aty;Transp. Res. Part A Policy Pract.,2013

4. End-to-end environmental sound classification using a 1D convolutional neural network;Abdoli;Expert Syst. Appl.,2019

5. Spatial analysis of fatal and injury crashes in Pennsylvania;Aguero-Valverde;Accid. Anal. Prev.,2006

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