Developing an Optimized Artificial Neural Network to Predict Traffic Crash Injury Severity

A structure optimization algorithm for developing an artificial neural network (ANN) for predicting crash injury severity has been proposed in this study to improve its generalization capacity. Two-vehicle crash records in 2006 from the Florida Department of Highway Safety and Motor Vehicles (DHSMV) have been collected in order to demonstrate the proposed method and to compare it with a traditional statistical model -- an ordered logit (OL) model. According to the results, both the trained and optimized ANN models outperform the OL model in terms of predictive accuracy, which again displays its great potential for modeling crash injury severity. Although the accuracy of the training and testing dataset are equivalent in the trained and optimized ANN models in the case study, 33 of 210 connections in the trained ANN model have been deleted by the optimization algorithm. The computational burden of prediction has been reduced, which indicates the optimized ANN model to be a good alternative for crash injury severity analysis.


  • English

Media Info

  • Media Type: Web
  • Features: References;
  • Pagination: pp 2396-2407
  • Monograph Title: CICTP 2014: Safe, Smart, and Sustainable Multimodal Transportation Systems

Subject/Index Terms

Filing Info

  • Accession Number: 01531347
  • Record Type: Publication
  • ISBN: 9780784413623
  • Files: TRIS, ASCE
  • Created Date: Jul 2 2014 3:03PM