Identifying Wrong-Way Driving (WWD) Crashes in Police Reports Using Text Mining Techniques

Wrong-way driving (WWD) has been a long-lasting issue for transportation agencies and law enforcement, since it causes pivotal threats to road users. Notwithstanding being rare, crashes occurring due to WWD are more severe than other types of crashes. It is time consuming to identify true WWD crashes from large crash database. It often involves a large man-hours to review hardcopy of crash reports. Otherwise, it may cause overestimation or underestimation of WWD crash frequencies. To fill this gap, the present study aims at identifying WWD crashes from other motor vehicle crashes in police reports. By applying text mining techniques, useful information can be extracted from the crash report narratives. In order to distinguish real WWD crashes from other motor vehicle crashes, machine learning methods were implemented to develop classification algorithms. In this study, four classification algorithms, including Naïve Bayes (NB), Random Forest (RF), Support Vector Machine (SVM), and Neural Network (NN) were implemented to categorize crash reports as WWD and non-WWD crashes. Hardcopies of crash reports were used to evaluate the performance of each classification algorithm. Results indicated that RF outperformed in identifying true WWD crashes in comparison with other algorithms with the highest accuracy of 98%.

• Record URL:
  https://annualmeeting.mytrb.org/OnlineProgram/Details/15775
• Supplemental Notes:
  • This paper was sponsored by TRB committee ACS20 Standing Committee on Safety Performance Analysis.
• Corporate Authors:

  Transportation Research Board

  ,    
• Authors:
  • Hosseini, Parisa
  • Jalayer, Mohammad
  • Das, Subasish
  • Zhou, Huaguo
• Conference:
  • Transportation Research Board 100th Annual Meeting
  • Location: Washington DC, United States
  • Date: 2021-1-5 to 2021-1-29
• Date: 2021

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References; Tables;
• Pagination: 16p

Subject/Index Terms

• TRT Terms: Algorithms; Artificial intelligence; Classification; Data mining; Documents; Machine learning; Police reports; Wrong way driving
• Subject Areas: Data and Information Technology; Highways; Safety and Human Factors;

Filing Info

• Accession Number: 01764270
• Record Type: Publication
• Report/Paper Numbers: TRBAM-21-01970
• Files: TRIS, TRB, ATRI
• Created Date: Feb 4 2021 11:00AM