Modeling non-parametric effects of two-vehicle speed on crash risk at intersections: Leveraging two-dimensional additive logistic regression beyond univariable approach-Reference-Cited by-同舟云学术

Modeling non-parametric effects of two-vehicle speed on crash risk at intersections: Leveraging two-dimensional additive logistic regression beyond univariable approach

Published:2023-09-26 Issue: Volume: Page:1-22
ISSN:1943-9962
Container-title:Journal of Transportation Safety & Security
language:en
Short-container-title:Journal of Transportation Safety & Security

Author:

Cui Pengfei¹^ORCID,Yang Xiaobao¹,Ma Lu²,Mu Chaoxu³

Affiliation:

1. MOT Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, School of System Science, Beijing Jiaotong University, Beijing, China

2. MOT Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, School of Traffic and Transportation, Beijing Jiaotong University, Beijing, China

3. School of Electrical and Information Engineering, Tianjin University, Tianjin, China

Funder

Fundamental Research Funds for the Central Universities

Publisher

Informa UK Limited

Subject

Safety Research,Transportation

Link

https://www.tandfonline.com/doi/pdf/10.1080/19439962.2023.2250307

Reference47 articles.

1. Driving speed and the risk of road crashes: A review

2. Characteristics of crashes that increase the risk of serious injuries;Augenstein J.;Annual Proceedings. Association for the Advancement of Automotive Medicine,2003

3. The Benefits and Tradeoffs for Varied High-Severity Injury Risk Thresholds for Advanced Automatic Crash Notification Systems

4. The effectiveness of a 20 mph speed limit intervention on vehicle speeds in Bristol, UK: A non-randomised stepped wedge design

5. Quantification of the impact of traffic incidents on speed reduction: A causal inference based approach

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. Advancing urban traffic accident forecasting through sparse spatio-temporal dynamic learning;Accident Analysis & Prevention;2024-06