A Large-Scale Neural Network Model for Real-Time Crash Prediction in Urban Road Networks

This study proposes a large-scale Artificial Neural Network (ANN) model for predicting crashes on freeways and arterials in urban road networks. ANN is a biologically-inspired information processing paradigm which is composed of interconnected processing elements (neurons). This study considers the probability of crash occurrence as a class variable (output) and applies a ANN classifier to predict a crash occurrence within a given area in the network up to 3 hours into the future. As feature variables (input), this study uses traffic condition information (link speed) from the whole network. The proposed ANN model are trained and tested using actual traffic and crash data collected in Brisbane, Australia from 2013 to 2016. Crash prediction capabilities were compared with those from two other models: Logistic Regression and Support Vector Machine. The evaluation results show that the proposed ANN crash prediction model provides the best performance in all tested cases.

• Supplemental Notes:
  • This paper was sponsored by TRB committee ABJ70 Standing Committee on Artificial Intelligence and Advanced Computing Applications.
• Authors:
  • Wang, Guangxing
  • Kim, Jiwon
• Conference:
  • Transportation Research Board 97th Annual Meeting
  • Location: Washington DC, United States
  • Date: 2018-1-7 to 2018-1-11
• Date: 2018

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References; Tables;
• Pagination: 8p

Subject/Index Terms

• TRT Terms: Arterial highways; Crash risk forecasting; Freeways; Mathematical prediction; Neural networks; Probability; Regression analysis; Urban highways
• Geographic Terms: Brisbane (Australia)
• Subject Areas: Highways; Planning and Forecasting; Safety and Human Factors;

Filing Info

• Accession Number: 01659760
• Record Type: Publication
• Report/Paper Numbers: 18-06519
• Files: TRIS, TRB, ATRI
• Created Date: Feb 12 2018 9:59AM