A hybrid deep learning method for distracted driving risk prediction based on spatio-temporal driving behavior data-Reference-Cited by-同舟云学术

A hybrid deep learning method for distracted driving risk prediction based on spatio-temporal driving behavior data

Published:2023-12-29 Issue:1 Volume:12 Page:
ISSN:2168-0566
Container-title:Transportmetrica B: Transport Dynamics
language:en
Short-container-title:Transportmetrica B: Transport Dynamics

Author:

Fu Xin¹,Meng Hongwei¹^ORCID,Yang Hao²,Wang Jianwei³

Affiliation:

1. College of Transportation Engineering, Chang’an University, Xi’an, People’s Republic of China

2. Department of Civil and Environmental Engineering, University of Washington, Seattle, WA, USA

3. Engineering Research Center of Highway Infrastructure Digitalization, Ministry of Education, Chang’an University, Xi’an, People’s Republic of China

Funder

the Key R&D Project of the Ministry of Science and Technology of the People’s Republic of China

Publisher

Informa UK Limited

Subject

Transportation,Modeling and Simulation,Software

Link

https://www.tandfonline.com/doi/pdf/10.1080/21680566.2023.2297144

Reference43 articles.

1. Unidirectional and Bidirectional LSTM Models for Short-Term Traffic Prediction

2. Enhancing the Performance of a Model to Predict Driving Distraction with the Random Forest Classifier

3. Detecting and Quantifying Mind Wandering during Simulated Driving

4. A spatiotemporal deep learning approach for citywide short-term crash risk prediction with multi-source data

5. Explaining the road accident risk: Weather effects

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