BConvLSTM: a deep learning-based technique for severity prediction of a traffic crash-Reference-Cited by-同舟云学术

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BConvLSTM: a deep learning-based technique for severity prediction of a traffic crash

Published:2024-05-02 Issue: Volume: Page:1-11
ISSN:1358-8265
Container-title:International Journal of Crashworthiness
language:en
Short-container-title:International Journal of Crashworthiness

Author:

Vinta Surendra Reddy¹,Rajarajeswari Pothuraju²,Kumar M. Vijay³,Kumar G. Sai Chaitanya⁴

Affiliation:

1. School of Computer Science and Engineering, VIT-AP University, Amaravati, India

2. Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, Andhra Pradesh, India

3. Department of Information Technology, Lakki Reddy Bali Reddy College of Engineering, Mylavaram, Andhra Pradesh, India

4. Department of Artificial Intelligence, DVR and Dr. HS MIC College of Technology, Kanchikacherla, India

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/13588265.2024.2348397

Reference23 articles.

1. Predicting multiple types of traffic accident severity with explanations: A multi-task deep learning framework

2. Highway Accident Severity Prediction for Optimal Resource Allocation of Emergency Vehicles and Personnel

3. An analytic framework using deep learning for prediction of traffic accident injury severity based on contributing factors

4. Machine learning based accident prediction in secure IoT enable transportation system

5. An Improved Particle Swarm Optimization for Prediction of Accident Severity

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