AI Article Synopsis
- The paper proposes a systematic method to evaluate how various traffic conditions affect highway crash risks by using a traffic safety state division approach.
- The study utilizes matched case-control techniques on highway crash and traffic data to minimize external factors' impact on the analysis.
- By employing a multi-parameter fusion cluster method and Bayesian conditional logistic regression, the research identifies distinct traffic safety states and their corresponding crash risk levels, suggesting that focused management can enhance highway safety.
Article Abstract
In order to quantitatively analyze the influence of different traffic conditions on highway crash risk, a method of crash risk assessment based on traffic safety state division is proposed in this paper. Firstly, the highway crash data and corresponding traffic data of upstream and downstream are extracted and processed by using the matched case-control method to exclude the influence of other factors on the model. Secondly, considering the weight of traffic volume, speed and occupancy, a multi-parameter fusion cluster method is applied to divide traffic safety state. In addition, the quantitative relationship between different traffic states and highway crash risk is analyzed by using Bayesian conditional logistic regression model. Finally, the results of case study show that different traffic safety conditions are in different crash risk levels. The highway traffic management department can improve the safety risk management level by focusing on the prevention and control of high-risk traffic safety conditions.
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Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6959613 | PMC |
| http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0227609 | PLOS |
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