Application of genetic programming clustering in defining LOS criteria of urban street in Indian context

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

@Article{Patnaik:2016:TBS,
  author =       "Ashish Kumar Patnaik and Prasanta Kumar Bhuyan",
  title =        "Application of genetic programming clustering in
                 defining {LOS} criteria of urban street in Indian
                 context",
  journal =      "Travel Behaviour and Society",
  volume =       "3",
  pages =        "38--50",
  year =         "2016",
  ISSN =         "2214-367X",
  DOI =          "doi:10.1016/j.tbs.2015.08.003",
  URL =          "http://www.sciencedirect.com/science/article/pii/S2214367X15000277",
  abstract =     "India is a highly populated country having second
                 largest road network in the world. Owing to boastfully
                 population, the congestion is growing rapidly on the
                 urban road networks. The level of service (LOS) is not
                 substantially defined for heterogeneous traffic flow
                 with different operational characteristics. Defining
                 LOS is essentially a classification problem. The
                 application of cluster analysis is the worthiest
                 proficiency to solve such problem for which genetic
                 programming (GP) clustering, an evolutionary algorithm
                 is used in this study. Five cluster validation
                 parameters are used to examine the optimal number of
                 clusters. The cluster validation parameters are used to
                 obtain the number of categories of urban street
                 classes. After acquiring optimal number of clusters, GP
                 clustering is implemented to the free flow speed (FFS)
                 data to get ranges of different urban street classes.
                 Again, GP clustering is enforced on average travel
                 speeds of street segments to specify the ranges of
                 different LOS categories. Speed data used in this study
                 are collected using Trimble GeoXT GPS receivers fitted
                 on mid-sized vehicles for five major urban corridors
                 comprising of 100 street segments of Greater Mumbai
                 region. Result shows that FFS of urban street classes
                 and average travel speed of LOS categories are lower
                 than that mentioned in Highway Capacity Manual (HCM
                 2000) on account of physical and surrounding
                 environmental characteristics. Also, average travel
                 speed of LOS categories expressed in terms percentage
                 of FFS of urban street classes found to be different
                 from that mentioned in HCM 2010.",
  keywords =     "genetic algorithms, genetic programming, Urban
                 streets, Level of service (LOS), Clustering analysis,
                 Free flow speed (FFS), Highway Capacity Manual (HCM)",
}

Genetic Programming entries for Ashish Kumar Patnaik Prasanta Kumar Bhuyan

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