Exploring Traffic Crash Occurrence Mechanism toward Cross-Area Freeways via an Improved Data Mining Approach
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 148, Issue 9
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.
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Data Availability Statement
Some or all data, models, or code used during the study were provided by a third party. Direct requests for these materials may be made to the provider as indicated in the acknowledgments.
Acknowledgments
This work was supported by China Postdoctoral Science Foundation (Grant No. 2021M700333). Smart Transportation Applications and Research lab provided the research data, and the traffic flow data may be available from the corresponding author with the provider’s permission.
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Information & Authors
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Published In
Journal of Transportation Engineering, Part A: Systems
Volume 148 • Issue 9 • September 2022
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Nov 15, 2021
Accepted: Mar 28, 2022
Published online: Jun 20, 2022
Published in print: Sep 1, 2022
Discussion open until: Nov 20, 2022
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