Spatio-Temporal Accident Prediction: Effects of Negative Sampling on Understanding Network-Level Accident Occurrence

In projects centered around rare event case data, the challenge of data comprehension is greatly increased due to insufficient data for deriving insight and analysis. This is particularly the case with traffic accident occurrence, where positive events (accidents) are rare with, in most cases, no data set existing for negative events (non accidents). One method to increase available data is negative sampling. In this work, four negative sampling techniques are presented with varying ratios of negative to positive data. These types of techniques are based on spatial, temporal, and a mixture of the two types of data, with the data ratios acting as class balancing tools. The best performing model found was with a negative sampling technique that shifted temporal information and had an even 50/50 data split, with an F-1 score of 93.68. These results are promising for Intelligent Transportation System (ITS) applications to inform of potential accident locations in an entire area for proactive measures to be put in place.

Language

  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; References; Tables;
  • Pagination: 14p

Subject/Index Terms

Filing Info

  • Accession Number: 01764400
  • Record Type: Publication
  • Report/Paper Numbers: TRBAM-21-02286
  • Files: TRIS, TRB, ATRI
  • Created Date: Dec 23 2020 11:25AM