In this paper, an analytical methodology is presented for computing the accessibility of transportation stations gap (ATSG) using differences between the accessibility indices for the public and private stations within a certain travel time thresholds in the city of Mashhad, Iran. Analyzing the conceptual differences among current modal accessibility methods of transportation stations can help to evaluate ATSG more accurately and minimizes the number of gaps between the private and public transportation stations. To address this need, we conducted a comparative analysis of the different accessibility methods and developed a novel approach based on spatio-temporal analysis. Three accessibility models were analyzed—contour (cumulative) (CN), potential access (PA), and gravity metric (GM)—considering three spatial factors (land use, transportation infrastructure, and population densities). Temporal factors including three travel time thresholds (3, 15, and 60 min) and three peak time periods (morning, noon, and afternoon) were analyzed to calculate the ATSG values for the private and public modes. Resulted ATSG values indicated superior accessibility for private cars in all three time thresholds applying three methods. However, our subsequent in-depth analysis of results determined that the ATSG values fluctuated as the time thresholds changed in the CN and PA methods. In the GM method, these values systematically increased as the time thresholds increased. The consequences of the GM method were more in line with reality and showed higher accuracy compared with others.


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    Titel :

    Comparative Analysis of Different Accessibility to Transportation Stations Gap Indices


    Weitere Titelangaben:

    Transportation Research Record: Journal of the Transportation Research Board


    Beteiligte:
    Algaidan, Hiba (Autor:in) / Shad, Rouzbeh (Autor:in) / Attya, Hussein (Autor:in)


    Erscheinungsdatum :

    2024-02-02




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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