A Crash Prediction Method Based on Artificial Intelligence Techniques and Driving Behavior Event Data

Yunjong Kim, Juneyoung Park and Cheol Oh
Additional contact information
Yunjong Kim: Department of Transportation and Logistics Engineering, Hanyang University, Ansan 15588, Korea
Juneyoung Park: Department of Transportation and Logistics Engineering, Hanyang University, Ansan 15588, Korea
Cheol Oh: Department of Transportation and Logistics Engineering, Hanyang University, Ansan 15588, Korea

Sustainability, 2021, vol. 13, issue 11, 1-15

Abstract: Various studies on how to prevent and deal with traffic accidents are ongoing. In the past, the key research emphasis was on passive accident response measures that analyzed roadway-based historical data to identify road sections with high crash risk. Through assessing crash risks by analyzing simulation data and actual vehicle driving trajectory data, this study suggests a method of effectively preventing accidents before they happen. In this analysis, using digital tachograph (DTG) data, which is the vehicle trajectory data for commercial vehicles running on Korean highways, hazardous and normal traffic flows were identified and extracted. Driving behavior event data for both types of traffic flow was processed by measuring safety indicators through the extracted data. Safety indicators with a high impact on traffic flow classification were then extracted using gradient boosting, a representative ensemble technique. A neural network analysis was performed using the extracted safety indicators as independent variables to create a traffic flow classifier, which had a high accuracy of 94.59%. The DTG data set was also classified based on the severity of each accident that occurred in the studied roadway, the time of the accident, and the weather; the results were compiled to enable comprehensive accident prediction. It is expected that proactive crash prevention will be possible in the future by evaluating real-time accident risks using the findings and ensemble-based methodologies of this paper.

Keywords: crash risk; driving behavior event data; ensemble; gradient boosting; safety indicators (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2071-1050/13/11/6102/pdf (application/pdf)
https://www.mdpi.com/2071-1050/13/11/6102/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:13:y:2021:i:11:p:6102-:d:564490

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().