Real-time traffic incident detection based on a hybrid deep learning model-Reference-Cited by-同舟云学术

Real-time traffic incident detection based on a hybrid deep learning model

Published:2020-09-06 Issue: Volume: Page:1-21
ISSN:2324-9935
Container-title:Transportmetrica A: Transport Science
language:en
Short-container-title:Transportmetrica A: Transport Science

Author:

Li Linchao¹,Lin Yi²,Du Bowen³,Yang Fan⁴,Ran Bin⁴

Affiliation:

1. College of Civil and Transportation Engineering, Shenzhen University, Shenzhen, People's Republic of China

2. College of Computer Science, Sichuan University, Chengdu, People's Republic of China

3. State Key Laboratory of Software Development Environment, Beihang University, Beijing, People's Republic of China

4. School of Transportation, Southeast University, Nanjing, People's Republic of China

Funder

National Natural Science Foundation of China

Publisher

Informa UK Limited

Subject

General Engineering,Transportation

Link

https://www.tandfonline.com/doi/pdf/10.1080/23249935.2020.1813214

Reference47 articles.

1. Predicting Freeway Crashes from Loop Detector Data by Matched Case-Control Logistic Regression

2. An Adaptive Conjugate Gradient Neural Network-Wavelet Model for Traffic Incident Detection

3. Ahmed, Mohammed S., and Allen R. Cook. 1979. “Analysis of Freeway Traffic Time-Series Data by Using Box-Jenkins Techniques.” Transportation Research Board Annual Meeting, 1979.

4. Incident detection methods using probe vehicles with on-board GPS equipment

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