Citation

Yang Y, Yuan Z, Meng R. J. Transp. Eng. A: Systems 2022; 148(9): e04022052.

Copyright

(Copyright © 2022, American Society of Civil Engineers)

DOI

10.1061/JTEPBS.0000698

PMID

unavailable

Abstract

Accurately identifying traffic crash risk factors is an important way to improve freeway safety. The purpose of this research is to reveal the internal coupling mechanisms of and differences between freeway traffic crashes in various area types, as well as to overcome the defects of compatibility and accuracy in the application of conventional data mining algorithms toward road traffic safety. First, the area types were divided into urban, suburban, and mountainous freeways in this research, based on the UW-DRIVENet (Digital Roadway Interactive Visualization and Evaluation Network, University of Washington) transportation big data platform, where data of more than 30,000 traffic crashes in Washington state in 2016 were extracted. The data set was designed via six dimensions: people, vehicle, road, environment, crash, and time. Furthermore, the weighted orientated multiple dimension interactive Apriori algorithm (WOMDI-Apriori) was proposed. In this improved algorithm, a subjective and objective joint weighting model based on interval analytic hierarchy process (IAHP) and gray relational degree was applied to quantify the weight of data fields. Finally, regarding three different area types of freeways, the improved algorithm was adopted to mine the association rules from the perspective of multidimensional interaction: full mapping crash cause and crash dimension autocorrelation perspectives. The results revealed the differences in traffic crash causes and risk factors of cross-freeways. The results show that the accuracy of the improved WOMDI-Apriori algorithm is 82.7%, 88.5%, and 80.5% higher than that of the conventional Apriori association rule algorithm when applied to urban, suburban, and mountainous area freeways, respectively, which indicates that WOMDI-Apriori algorithm can better reveal the causes of freeway traffic crashes and identify crash precursors more accurately. In conclusion, the WOMDI-Apriori algorithm proposed in this research can be used as an effective approach for risk identification of freeway traffic crashes and can also provide theoretical guidance for future freeway traffic safety improvement.

Language: en

Keywords

Apriori; Coupling mechanism analysis; Data mining; Freeway; Traffic crash