A human-in-the-loop ensemble fusion framework for road crash prediction: coping with imbalanced heterogeneous data from the driver-vehicle-environment system-Reference-Cited by-同舟云学术

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A human-in-the-loop ensemble fusion framework for road crash prediction: coping with imbalanced heterogeneous data from the driver-vehicle-environment system

Published:2024-09-03 Issue: Volume: Page:1-17
ISSN:1942-7867
Container-title:Transportation Letters
language:en
Short-container-title:Transportation Letters

Author:

Elamrani Abou Elassad Dauha¹,Elamrani Abou Elassad Zouhair¹²^ORCID,Ed-Dahbi Abdel Majid¹,El Meslouhi Othmane¹,Kardouchi Mustapha³,Akhloufi Moulay³,Jahan Nusrat²

Affiliation:

1. SARS Research Team, Computer Science Department, ENSAS, Cadi Ayyad University, Marrakesh, Morocco

2. Data Science Laboratory, Information Technology & Management Department, Daffodil International University, Birulia, Bangladesh

3. PRIME Laboratory, Computer Science Department, Moncton University, Moncton, Canada

Funder

Centre National pour la Recherche Scientifique et Technique

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/19427867.2024.2392063

Reference113 articles.

1. Bayesian Updating Approach for Real-Time Safety Evaluation with Automatic Vehicle Identification Data

2. Does time of day matter at highway work zone crashes?

3. Missing Data Techniques for Structural Equation Modeling.

4. iBump: Smartphone application to detect car accidents

5. Predicting stroke events with a proactive fusion system: a comprehensive study on imbalance class handling in computational biomechanics

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