Predicting crash occurrence at intersections in Texas: an opportunity for machine learning-Reference-Cited by-同舟云学术

Predicting crash occurrence at intersections in Texas: an opportunity for machine learning

Published:2023-02-15 Issue: Volume: Page:1-21
ISSN:0308-1060
Container-title:Transportation Planning and Technology
language:en
Short-container-title:Transportation Planning and Technology

Author:

Charm Theodore¹,Wang Haoqi²,Zuniga-Garcia Natalia³,Ahmed Mostaq⁴,Kockelman Kara M.³

Affiliation:

1. Department of Government, The University of Texas at Austin, Austin, TX, USA

2. Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, USA

3. Department of Civil, Architectural and Environmental Engineering, The University of Texas at Austin, Austin, TX, USA

4. Department of Community and Regional Planning, The University of Texas at Austin, Austin, TX, USA

Publisher

Informa UK Limited

Subject

Transportation,Geography, Planning and Development

Link

https://www.tandfonline.com/doi/pdf/10.1080/03081060.2023.2177651

Reference57 articles.

1. Development of Artificial Neural Network Models to Predict Driver Injury Severity in Traffic Accidents at Signalized Intersections

2. ACS. 2020. “American Community Survey Data via API.” Accessed September 1, 2022. https://www.census.gov/programs-surveys/acs/data/data-via-api.html.

3. The Target Parameter of Adjusted R-Squared in Fixed-Design Experiments

4. A random parameters with heterogeneity in means and Lindley approach to analyze crash data with excessive zeros: A case study of head-on heavy vehicle crashes in Queensland

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. Predicting pedestrian crash occurrence and injury severity in Texas using tree-based machine learning models;Transportation Planning and Technology;2023-06-14