Genetic Programming to Investigate Design Parameters Contributing to Crash Occurrence on Urban Arterials

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{Das:2010:TRB,
  author =       "Abhishek Das and Mohamed Abdel-Aty and Anurag Pande",
  title =        "Genetic Programming to Investigate Design Parameters
                 Contributing to Crash Occurrence on Urban Arterials",
  journal =      "Transportation Research Record: Journal of the
                 Transportation Research Board",
  year =         "2010",
  volume =       "2147",
  pages =        "25--32",
  publisher =    "Transportation Research Board of the National
                 Academies",
  keywords =     "genetic algorithms, genetic programming",
  ISSN =         "0361-1981",
  URL =          "http://trb.metapress.com/content/52881hl17685547l/fulltext.pdf",
  DOI =          "doi:10.3141/2147-04",
  size =         "8 pages",
  abstract =     "Nonlinear models were developed to estimate crash
                 frequency on urban arterials with partial access
                 control. These multilane arterials consist of midblock
                 segments joined by signalised and signalised
                 intersections (or access points). Crashes included in
                 the analysis are of three major types: rear-end, angle,
                 and head-on. Each crash type is further sorted into
                 mutually exclusive categories on the basis of the
                 roadway element responsible for the crashes: midblock
                 segment, signalised intersection, and access point.
                 Genetic programming (GP) is adopted for predicting
                 crash frequency. GP, which is primarily based on
                 genetic algorithms, uses the concept of evolution to
                 develop models through the processes of crossover and
                 mutation. The GP modelling approach gives independence
                 for model development without restrictions on
                 distribution of data. The models developed were
                 compared to the basic negative binomial models. Morning
                 and afternoon peak periods are observed to have fewer
                 occurrences of rear-end crashes at all roadway
                 elements. Higher traffic volume results in an increased
                 number of angle crashes. Instances of angle crashes
                 have increased at signalised intersections, even at
                 lower maximum posted speeds. A higher average truck
                 factor increases the instances of head-on crashes on
                 midblock segments and at signalised intersections.",
  notes =        "Online Date Friday, October 01, 2010

                 p31 'GP outperforms NB [negative binomial] in lower MSE
                 values (for validation data set only) in six of the
                 nine models discussed in the study.'",
}

Genetic Programming entries for Abhishek Das Mohamed A Abdel-Aty Anurag Pande

Citations