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

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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.

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References

  • Akwawua S, Pooler J (2001) The development of an intervening opportunities model with spatial dominance effects. J Geogr Syst 3(1):69–86

    Article  Google Scholar 

  • Almeida LMW, Goncalves MB (2001) A methodology to incorporate behavioral aspects in trip distribution models with an application to estimate student flow. Environ Plan A 33(6):1125–1138

    Article  Google Scholar 

  • Black WR (1991) A note on the use of correlation coefficients for assessing goodness-of-fit in spatial interaction models. Transportation 18(3):199–206

    Article  Google Scholar 

  • Bonnel P (2004) Prévoir la Demande de Transport. Presses Ponts et Chaussées, Paris

    Google Scholar 

  • Chow L-F, Zhao F, Li M-T, Li S-C (2005) Development and evaluation of aggregate destination choice models for trip distribution in Florida. Transp Res Rec 1931(1):18–27

    Article  Google Scholar 

  • De Grange L, Troncoso R, Ibeas A, González F (2009) Gravity model estimation with proxy variables and the impact of endogeneity on transportation planning. Transp Res Part A 43(2):105–116

    Google Scholar 

  • Eash R (1983) Several more improvements in understanding, calibrating, and applying the opportunity model. Chicago Area Transportation Study, Chicago

    Google Scholar 

  • Eash R (1984) Development of a doubly constrained intervening opportunity model for trip distribution. Chicago Area Transportation Study, Chicago

    Google Scholar 

  • Evans NJ, Pooler J (1987) Distance deterrence effects in constrained spatial interaction models of interprovincial migration. Can J Reg Sci Autumn 1987(1):259–279

    Google Scholar 

  • Feng S, Li X (2004) Research on public transit passenger OD matrix estimation. Paper presented at the 8th international conference on applications of advanced technologies in transportaion engineering, Beijing, China

  • Goncalves MB, De Cursi JES (2001) Parameter estimation in a trip distribution model by random perturbation of a descent method. Transp Res Part B 35(2):137–161

    Article  Google Scholar 

  • Goncalves MB, UIysséa Neto I (1993) The development of a new gravity—opportunity model for trip distribution. Environ Plan A 25(6):817–826

    Article  Google Scholar 

  • Google (2012) What is GTFS? https://developers.google.com/transit/gtfs/. Accessed 7 July 2012

  • Hensher DA (1977) Urbain transport economics. Cambridge University Press, Cambridge

    Google Scholar 

  • Hensher DA, Button KJ (2008) Handbook of transport modelling. Elsevier, Amsterdam

    Google Scholar 

  • Hu P, Pooler J (2002) An empirical test of the competing destinations model. J Geogr Syst 4(3):301–323

    Article  Google Scholar 

  • Kermanshah M (2004) Notes de Cours “Prévision de la Demande en Transport”. Sharif University of Technology, Tehran

    Google Scholar 

  • Knudsen DC, Fotheringham AS (1986) Matrix comparison, goodness-of-fit and spatial interaction modeling. Int Reg Sci Rev 10(2):127–147

    Article  Google Scholar 

  • Mishra S, Wang Y, Zhu X, Moeckel R, Mahaparta S (2013) Comparison between gravity and destination choice models for trip distribution in Maryland. Paper presented at the TRB of the National Acdemies of Science annual meeting, Washington, DC

  • Mobilité des Personnes (2010) Secrétariat à l’Enquête Origine-Destination (OD), Agence Métropolitaine de Transport (AMT). Montreal, QC

    Google Scholar 

  • Nazem M, Trépanier M, Morency C (2011) Demographic analysis of route choice for public transit. Transp Res Rec 2217(2):71–78

    Article  Google Scholar 

  • Nazem M, Trépanier M, Morency C (2012) Hierarchical intervening opportunities model for public transit trip distribution. Paper presented at the conference on advanced systems for public transit (CASPT), Pontificia Universidad Catolica de Chile, The Ritz-Carlton, Santiago, Chile

  • Nazem M, Trépanier M, Morency C (2013) Integrated intervening opportunities model for public transit trip generation-distribution: a supply-dependent approach. Transp Res Rec 2350(1):47–57. doi:10.3141/2350-06

    Article  Google Scholar 

  • Ortuzar JdD, Willumsen LG (1994) Modelling transport. Wiley, Chichester

    Google Scholar 

  • Rajesson F (2009) Modèle Hybride d’Estimation de la Demande de Transport Collectif. École Polytechnique de Montréal, Montréal

    Google Scholar 

  • Schneider M (1959) Gravity models and trip distribution theory. Reg Sci Assoc 5(1):51–56

    Article  Google Scholar 

  • Smith DP, Hutchinson BG (1981) Goodness-of-fit statistics for trip distribution models. Transp Res A 15(4):295–303

    Article  Google Scholar 

  • 2006 Census of Population, Quebec—Cat. No. 94-581-X2006002 (2011a) http://ivt.crepuq.qc.ca/. Accessed 11 Oct 2011

  • Statistics Canada (2011b) www.statcan.gc.ca

  • Stouffer SA (1940) Intervening opportunities: a theory relating mobility and distance. Am Sociol Rev 5(6):845–867

    Article  Google Scholar 

  • Stouffer SA (1960) Intervening opportunities and competing migrants. J Reg Sci 2:1–26

    Article  Google Scholar 

  • Thamizh Arasan V, Wermuth M, Srinivas BS (1996) modelling of stratified urban trip distribution. J Transp Eng 122(5):342–349

    Article  Google Scholar 

  • Veenstra SA, Thomas T, Tutert SIA (2010) Trip distribution for limited destinations: a case study for grocery shopping trips in the Netherlands. Paper presented at the 89th annual meeting of the Transportation Research Board, Washington DC

  • Wills MJ (1986) A flexible gravity-opportunities model for trip distribution. Transp Res Part B Methodol 20(2):89–111

    Article  Google Scholar 

  • Wilson AG (1970) Advances and problems in distribution modelling. Transp Res 4(1):1–18

    Article  Google Scholar 

  • Wong JC (2013) Use of the GTFS in transit perdormance measurment. Georgia Institute of Technology, Atlanta

    Google Scholar 

  • Yaldi G, Taylor MAP, Yue WL (2011) Forecasting origin-destination matrices by using neural network approach: a comparison of testing performance between back propagation, variable learning rate and Levenberg–Marquardt Algorithms. In: Australasian transport research forum 2011 proceedings, Adelaide, Australia. http://www.patrec.org/atrf.aspx

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Acknowledgments

The authors acknowledge the support of Natural Sciences and Engineering Research Council of Canada (NSERC) and the Agence Métropolitaine de Transport (AMT). We thank the survey consortium that provided the Greater Montreal Origin–Destination (OD) survey and the GTFS data. Special thanks to François Godefroy, Audrey Godin, Pierre-Léo Mongeon-Bourbonnais, Éric Poliquin, Hubert Verreault, and other members of the Mobilité chair at École Polytechnique de Montréal for their help in obtaining and preparing the different data sets.

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Nazem, M., Trépanier, M. & Morency, C. Revisiting the destination ranking procedure in development of an Intervening Opportunities Model for public transit trip distribution. J Geogr Syst 17, 61–81 (2015). https://doi.org/10.1007/s10109-014-0203-1

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  • DOI: https://doi.org/10.1007/s10109-014-0203-1

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