A Genetic Programming Model for Real-Time Crash Prediction on Freeways

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@Article{Xu:2012:ieeITSe,
  author =       "Chengcheng Xu and Wei Wang2 and Pan Liu",
  journal =      "IEEE Transactions on Intelligent Transportation
                 Systems",
  title =        "A Genetic Programming Model for Real-Time Crash
                 Prediction on Freeways",
  year =         "2013",
  volume =       "14",
  number =       "2",
  month =        jun,
  pages =        "574--586",
  keywords =     "genetic algorithms, genetic programming, Binary logit
                 model, freeway, genetic programming (GP), real-time
                 crash prediction, traffic safety",
  DOI =          "doi:10.1109/TITS.2012.2226240",
  ISSN =         "1524-9050",
  abstract =     "This paper aimed at evaluating the application of the
                 genetic programming (GP) model for real-time crash
                 prediction on freeways. Traffic, weather, and crash
                 data used in this paper were obtained from the I-880N
                 freeway in California, United States. The random forest
                 (RF) technique was conducted to select the variables
                 that affect crash risk under uncongested and congested
                 traffic conditions. The GP model was developed for each
                 traffic state based on the candidate variables that
                 were selected by the RF technique. The traffic flow
                 characteristics that contribute to crash risk were
                 found to be quite different between congested and
                 uncongested traffic conditions. This paper applied the
                 receiver operating characteristic (ROC) curve to
                 evaluate the prediction performance of the developed GP
                 model for each traffic state. The validation results
                 showed that the prediction performance of the GP models
                 were satisfactory. The binary logit model was also
                 developed for each traffic state using the same
                 training data set. The authors compared the ROC curve
                 of the GP model and the binary logit model for each
                 traffic state. The GP model produced better prediction
                 performance than did the binary logit model for each
                 traffic state. The GP model was found to increase the
                 crash prediction accuracy under uncongested traffic
                 conditions by an average of 8.2percent and to increase
                 the crash prediction accuracy under congested traffic
                 conditions by an average of 4.9percent.",
  notes =        "Also known as \cite{6357306}",
}

Genetic Programming entries for Chengcheng Xu Wei Wang2 Pan Liu

Citations