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Revisiting the destination ranking procedure in development of an Intervening Opportunities Model for public transit trip distribution

Author

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  • Mohsen Nazem
  • Martin Trépanier
  • Catherine Morency

Abstract

An Enhanced Intervening Opportunities Model (EIOM) is developed for Public Transit (PT). This is a distribution supply dependent model, with single constraints on trip production for work trips during morning peak hours (6:00 a.m.–9:00 a.m.) within the Island of Montreal, Canada. Different data sets, including the 2008 Origin–Destination (OD) survey of the Greater Montreal Area, the 2006 Census of Canada, GTFS network data, along with the geographical data of the study area, are used. EIOM is a nonlinear model composed of socio-demographics, PT supply data and work location attributes. An enhanced destination ranking procedure is used to calculate the number of spatially cumulative opportunities, the basic variable of EIOM. For comparison, a Basic Intervening Opportunities Model (BIOM) is developed by using the basic destination ranking procedure. The main difference between EIOM and BIOM is in the destination ranking procedure: EIOM considers the maximization of a utility function composed of PT Level Of Service and number of opportunities at the destination, along with the OD trip duration, whereas BIOM is based on a destination ranking derived only from OD trip durations. Analysis confirmed that EIOM is more accurate than BIOM. This study presents a new tool for PT analysts, planners and policy makers to study the potential changes in PT trip patterns due to changes in socio-demographic characteristics, PT supply, and other factors. Also it opens new opportunities for the development of more accurate PT demand models with new emergent data such as smart card validations. Copyright Springer-Verlag Berlin Heidelberg 2015

Suggested Citation

  • Mohsen Nazem & Martin Trépanier & Catherine Morency, 2015. "Revisiting the destination ranking procedure in development of an Intervening Opportunities Model for public transit trip distribution," Journal of Geographical Systems, Springer, vol. 17(1), pages 61-81, January.
    • Handle: RePEc:kap:jgeosy:v:17:y:2015:i:1:p:61-81
    DOI: 10.1007/s10109-014-0203-1
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    References listed on IDEAS

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    Cited by:

    1. Oshan, Taylor M., 2022. "Spatial Interaction Modeling," OSF Preprints m3ah8_v1, Center for Open Science.
    2. Oshan, Taylor M., 2022. "Spatial Interaction Modeling," OSF Preprints m3ah8, Center for Open Science.

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    More about this item

    Keywords

    Destination ranking; Intervening Opportunities Model; Public transit planning; Supply dependent model; Trip distribution; C1; C4; C6;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling

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