Transport Choice Modeling for the Evaluation of New Transport Policies

Created by W.Langdon from gp-bibliography.bib Revision:1.4504

@Article{su10041230,
  author =       "Ander Pijoan and Oihane Kamara-Esteban and 
                 Ainhoa Alonso-Vicario and Cruz E. Borges",
  title =        "Transport Choice Modeling for the Evaluation of New
                 Transport Policies",
  journal =      "Sustainability",
  volume =       "10",
  year =         "2018",
  number =       "4",
  article_number = "1230",
  keywords =     "genetic algorithms, genetic programming",
  ISSN =         "2071-1050",
  URL =          "http://www.mdpi.com/2071-1050/10/4/1230",
  DOI =          "doi:10.3390/su10041230",
  abstract =     "Quantifying the impact of the application of
                 sustainable transport policies is essential in order to
                 mitigate effects of greenhouse gas emissions produced
                 by the transport sector. One of the most common
                 approaches used for this purpose is that of traffic
                 modelling and simulation, which consists of emulating
                 the operation of an entire road network. This article
                 presents the results of fitting 8 well known data
                 science methods for transport choice modelling, the
                 area in which more research is needed. The models have
                 been trained with information from Biscay province in
                 Spain in order to match as many of its commuters as
                 possible. Results show that the best models correctly
                 forecast more than 51percent of the trips recorded.
                 Finally, the results have been validated with a second
                 data set from the Silesian Voivodeship in Poland,
                 showing that all models indeed maintain their
                 forecasting ability.",
}

Genetic Programming entries for Ander Pijoan Oihane Kamara-Esteban Ainhoa Alonso-Vicario Cruz Enrique Borges

Citations