A highway crash risk assessment method based on traffic safety state division

Autoři: Dongye Sun aff001;  Yunfei Ai aff001;  Yunhua Sun aff001;  Liping Zhao aff003
Působiště autorů: China Transport Telecommunications & Information Center, Beijing China aff001;  National Engineering Laboratory for Transportation Safety and Emergency informatics, Beijing, China aff002;  Beijing Institute of New Technology Applications, Beijing, China aff003
Vyšlo v časopise: PLoS ONE 15(1)
Kategorie: Research Article
doi: 10.1371/journal.pone.0227609


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.

Klíčová slova:

Clustering algorithms – Data processing – Forecasting – Markov models – Optimization – Probability distribution – Statistical models – Traffic safety


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Článek vyšel v časopise


2020 Číslo 1