Predicting Crashes on Expressway Ramps with Real-Time Traffic and Weather Data

Limited research has been conducted on real-time crash analysis of expressway ramps, although there have been many studies in recent years on estimating real-time crash prediction models for main lines. This study presents Bayesian logistic regression models for single-vehicle (SV) and multivehicle (MV) crashes on expressway ramps by using real-time microwave vehicle detection system data, real-time weather data, and ramp geometric information. The results find that the logarithm of the vehicle count, average speed in a 5-min interval, and visibility are significant factors for the occurrence of SV and MV crashes. The Bayesian logistic regression models show that curved ramps and wet road surfaces would increase the possibility of an SV crash, and off-ramps would result in high risk of MV crashes. The high standard deviation of speed in a 5-min interval would significantly increase MV crash likelihood. Random Forests software was applied in variable importance analysis, and the results revealed that the most important factors influencing crashes on ramps were traffic variables, the second most important factors are weather variables, and the least important but still significant factor was the ramp geometry.

• Record URL:
  https://doi.org/10.3141/2514-04
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/isbn/9780309369367
• Authors:
  • Wang, Ling
  • Shi, Qi
  • Abdel-Aty, Mohamed
• Publication Date: 2015

Language

• English

Media Info

• Media Type: Print
• Features: Figures; References; Tables;
• Pagination: pp 32–38
• Monograph Title: Safety Data, Analysis, and Evaluation
• Serial:
  • Transportation Research Record: Journal of the Transportation Research Board
  • Issue Number: 2514
  • Publisher: Transportation Research Board
  • ISSN: 0361-1981

Subject/Index Terms

• TRT Terms: Bayes' theorem; Crash characteristics; Crash risk forecasting; Expressways; Highway curves; Highway design; Interchange ramps; Mathematical prediction; Real time information; Speed; Traffic data; Traffic density; Weather
• Subject Areas: Data and Information Technology; Highways; Safety and Human Factors; I80: Accident Studies;

Filing Info

• Accession Number: 01550133
• Record Type: Publication
• ISBN: 9780309369367
• Report/Paper Numbers: 15-0185
• Files: TRIS, TRB, ATRI
• Created Date: Jan 16 2015 8:29AM