Examining imbalanced classification algorithms in predicting real-time traffic crash risk-Reference-Cited by-同舟云学术

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Examining imbalanced classification algorithms in predicting real-time traffic crash risk

Published:2020-09 Issue: Volume:144 Page:105610
ISSN:0001-4575
Container-title:Accident Analysis & Prevention
language:en
Short-container-title:Accident Analysis & Prevention

Author:

Peng Yichuan,Li Chongyi,Wang Ke,Gao Zhen,Yu Rongjie

Funder

National Key R&D Program of China

Chinese National Science Foundation

Publisher

Elsevier BV

Subject

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

Reference36 articles.

1. Identifying crash propensity using specific traffic speed conditions;Abdel-Aty;J. Safety Res.,2005

2. Predicting freeway crashes from loop detector data by matched case-control logistic regression;Abdel-Aty;Transp. Res. Rec.,2004

3. Split models for predicting multivehicle crashes during high-speed and low-speed operating conditions on freeways;Abdel-Aty;Transp. Res. Rec.,2005

4. Real-time prediction of visibility related crashes;Abdel-Aty;Transp. Res. Part C Emerg. Technol.,2012

5. The viability of using automatic vehicle identification data for real-time crash prediction;Ahmed;IEEE trans. Intell. Transp. Syst.,2012

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2. Ensemble learning based approach for traffic incident detection and multi-category classification;Engineering Applications of Artificial Intelligence;2024-06

3. A crash occurrence risk prediction model based on variational autoencoder and generative adversarial network;Transportmetrica B: Transport Dynamics;2024-05-27

4. Calibrated confidence learning for large-scale real-time crash and severity prediction;npj Sustainable Mobility and Transport;2024-05-22

5. Predicting Highway Risk Event with Trajectory Data: A Joint Approach of Traffic Flow and Vehicle Kinematics;Electronics;2024-02-02

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