Deep hybrid learning framework for spatiotemporal crash prediction using big traffic data-Reference-Cited by-同舟云学术

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Deep hybrid learning framework for spatiotemporal crash prediction using big traffic data

Published:2022-07 Issue: Volume: Page:
ISSN:2046-0430
Container-title:International Journal of Transportation Science and Technology
language:en
Short-container-title:International Journal of Transportation Science and Technology

Author:

Kashifi Mohammad Tamim,Al-Turki Mohammed,Sharify Abdul Wakil

Funder

King Fahd University of Petroleum and Minerals

Publisher

Elsevier BV

Subject

Management, Monitoring, Policy and Law,Transportation,Automotive Engineering,Civil and Structural Engineering

Reference56 articles.

1. Al-Dogom, D., Aburaed, N., Al-Saad, M., Almansoori, S., 2019. Spatio-temporal Analysis and Machine Learning for Traffic Accidents Prediction. 2019 2nd International Conference on Signal Processing and Information Security, ICSPIS 2019. doi: 10.1109/ICSPIS48135.2019.9045892.

2. Al-ghamdi, A.S., 2002. Emergency medical service rescue times in Riyadh 34, 499–505.

3. Traffic accident detection and condition analysis based on social networking data;Ali;Accid. Anal. Prev.,2021

4. Comparing the efficiency of different computation intelligence techniques in predicting accident frequency;Amiri;IATSS Research,2020

5. A spatiotemporal deep learning approach for citywide short-term crash risk prediction with multi-source data. Accident Analysis and Prevention 122;Bao;Accid. Anal. Prev.,2019

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