Exploring the Determinants of Pedestrian Crash Severity Using an AutoML Approach-Reference-Cited by-同舟云学术

Exploring the Determinants of Pedestrian Crash Severity Using an AutoML Approach

Published:2024-06-13 Issue: Volume:8 Page:442-455
ISSN:
Container-title:International Conference on Transportation and Development 2024
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Author:

Rafe Amir¹¹,Singleton Patrick A.¹¹

Affiliation:

1. Dept. of Civil and Environmental Engineering, Utah State Univ., Logan, UT.

Publisher

American Society of Civil Engineers

Link

https://ascelibrary.org/doi/pdf/10.1061/9780784485514.039

Reference24 articles.

1. Using Machine Learning Models to Forecast Severity Level of Traffic Crashes by R Studio and ArcGIS;Al-Mistarehi B. W.;Frontiers in Built Environment,2022

2. A bibliometric analysis and benchmark of machine learning and automl in crash severity prediction: The case study of three colombian cities;Angarita-Zapata J. S.;Sensors,2021

3. Batista, G. E. A. P. A., Bazzan, A. L. C., and Monard, M. C. (2003). Balancing Training Data for Automated Annotation of Keywords: a Case Study. In Proceedings of the Second Brazilian Workshop on Bioinformatics, January.

4. Examining the influence of rural land uses and accessibility-related factors to estimate pedestrian safety: The use of GIS and machine learning techniques

5. Shedding light on the pedestrian safety crisis: An analysis across the injury severity spectrum by lighting condition;Ferenchak N. N.;Traffic Injury Prevention,2022

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A Comparative Study Using Generalized Ordered Probit, Stacking Ensemble, and TabNet: Application to Determinants of Pedestrian Crash Severity;Data Science for Transportation;2024-06-28