Modelling Service Quality Offered by Signalized Intersections from Automobile Users' Perspective in Urban Indian Context

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@Article{KAR:2020:TRP,

• author = "Manaswinee Kar and Suprava Jena and Abhishek Chakraborty and Prasanta Kumar Bhuyan",
• title = "Modelling Service Quality Offered by Signalized Intersections from Automobile Users' Perspective in Urban Indian Context",
• journal = "Transportation Research Procedia",
• volume = "48",
• pages = "904--922",
• year = "2020",
• note = "Recent Advances and Emerging Issues in Transport Research - An Editorial Note for the Selected Proceedings of WCTR 2019 Mumbai",
• ISSN = "2352-1465",
• DOI = "doi:10.1016/j.trpro.2020.08.109",
• URL = "http://www.sciencedirect.com/science/article/pii/S2352146520305251",
• keywords = "genetic algorithms, genetic programming, Signalized Intersections, Automobile Users, Level of Service, Perception survey, Multi-Gene Genetic Programming",
• abstract = "This article proposes modelling the service quality offered by signalized intersections, nodal focuses in a transportation network, from automobile users' perspective in the urban Indian context. Indian traffic is generally heterogeneous in nature, which implies non-motorized vehicles and pedestrians share the same space as the motorized vehicles. All possible geometric, traffic, and built-environmental data were collected from 45 diversified signalized intersections located in one of the metropolitan cities of India (Kolkata). Along with these, responses from around 9000 on-street automobile users were gathered seeking socio-demographic information and overall satisfaction scores (ranging from 6 = excellent to 1 = worst). Accordingly, the parameters exerting significant (p < 0.001) influences on the overall satisfaction scores were highlighted by Pearson's correlation analysis. The arrangement of significant parameters comprised of only six attributes which were quantitative in nature. Exceptionally reliable, however, less erratic automobile level of service (ALOS) models were formulated considering these six variables with the assistance of a unique and widely used artificial intelligence technique in particular, multi-gene genetic programming (MGGP). The model displayed incredible likelihood efficiencies in the present article and delivered a high coefficient of determination (R2) estimations of 0.875 under the prevalent site conditions. The sensitivity analysis of demonstrated attributes showed that traffic volume per effective road width, effect of non-motorized vehicles, and pavement condition index profoundly influenced the ALOS of signalized intersections in the urban Indian context. The vital results of this work would, to a great extent, help the transportation organizers and architects in evaluating the operational efficiencies of signalized intersections and in making efficient resolutions for the better administration of automobile traffic",

}

Genetic Programming entries for Manaswinee Kar Suprava Jena Abhishek Chakraborty Prasanta Kumar Bhuyan

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