Predicting Pedestrian Involvement in Fatal Crashes Using a TabNet Deep Learning Model-Reference-Cited by-同舟云学术

Predicting Pedestrian Involvement in Fatal Crashes Using a TabNet Deep Learning Model

Published:2023-11-13 Issue: Volume: Page:
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Container-title:Proceedings of the 16th ACM SIGSPATIAL International Workshop on Computational Transportation Science
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Author:

Al-Ani Omar¹^ORCID,Haroon Saquib Mohammed²^ORCID,Caragea Doina²^ORCID,Aziz HM Abdul²^ORCID,Fitzsimmons Eric J.²^ORCID

Affiliation:

1. Kansas State University, Manhattan, United States

2. Kansas State University, Manhattan, USA

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3615895.3628169

Reference48 articles.

1. National Highway Traffic Safety Administration et al. 2023. Fatality Analysis Reporting System (FARS) Analytical User's Manual 1975--2021.

2. Intersection Traffic Prediction Using Decision Tree Models

3. Jorge LM Amaral, Agnaldo J Lopes, José M Jansen, Alvaro CD Faria, and Pedro L Melo. 2012. Machine learning algorithms and forced oscillation measurements applied to the automatic identification of chronic obstructive pulmonary disease. Computer methods and programs in biomedicine 105, 3 (2012), 183--193.

4. An assessment of machine learning and data balancing techniques for evaluating downgrade truck crash severity prediction in Wyoming

5. TabNet: Attentive Interpretable Tabular Learning

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