An integrated intervening opportunities model (IIOM) was developed for public transit (PT) trips. This model is generation–distribution and supply-dependent, with single constraints only on trip production values for work and study PT trips made during morning peak hours (6:00 to 9:00 a.m.) within the Island of Montreal, Quebec, Canada. Several data sets, including the 2008 origin–destination survey of the Greater Montreal Area, 2006 census of Canada, General Transit Feed Specification network data, and school enrollment data, along with the geographical data of the Greater Montreal Area, were used. The IIOM is a nonlinear model with sociodemographic, socioeconomic, and PT supply characteristics, as well as work and study spatial location attributes. Analysis of the modeling performance by means of several goodness-of-fit measures showed that the IIOM was well behaved (i.e., globally it had good prediction capabilities) and more accurate than the classical gravity model. On the basis of explanatory variables used in the IIOM, the study presents a new tool for PT analysts, planners, and policy makers for studying potential changes in PT trip patterns, as a result of changes in sociodemographic and socioeconomic characteristics, PT supply, and so on. Also, this study opens new opportunities for development of more accurate PT demand models with new emergent data such as smartcard entries.
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References
1.
BonnelP.Prévoir la Demande de Transport.Presses Ponts et Chaussées, Paris, 2004.
2.
OrtuzarJ. d. D., and WillumsenL. G.Modelling Transport.John Wiley & Sons, Chichester, United Kingdom, 1994.
3.
HensherD. A., and ButtonK. J.Handbook of Transport Modelling.Elsevier, Amsterdam, Netherlands, 2008.
4.
WilsonA. G.Advances and Problems in Distribution Modelling. Transportation Research, Vol. 4, No. 1, 1970, pp. 1–18.
5.
WillsM. J.A Flexible Gravity-Opportunities Model for Trip Distribution. Transportation Research B, Vol. 20, No. 2, 1986, pp. 89–111.
6.
AlmeidaL. M. W., and GoncalvesM. B.A Methodology to Incorporate Behavioral Aspects in Trip Distribution Models with an Application to Estimate Student Flow. Environment and Planning A, Vol. 33, No. 6, 2001, pp. 1125–1138.
7.
GoncalvesM. B., and UIysséa NetoI.The Development of a New Gravity–Opportunity Model for Trip Distribution. Environment and Planning A, Vol. 25, No. 6, 1993, pp. 817–826.
8.
GoncalvesM. B., and De CursiJ. E. S.Parameter Estimation in a Trip Distribution Model by Random Perturbation of a Descent Method. Transportation Research B, Vol. 35, No. 2, 2001, pp. 137–161.
9.
KermanshahM.Notes de Cours Prévision de la Demande en Transport. Sharif University of Technology, Tehran, Iran, 2004.
10.
ChowL.-F., ZhaoF., LiM.-T., and LiS.-C.Development and Evaluation of Aggregate Destination Choice Models for Trip Distribution in Florida. In Transportation Research Record: Journal of the Transportation Research Board, No. 1931, Transportation Research Board of the National Academies, Washington, D.C., 2005, pp. 18–27.
11.
VeenstraS., ThomasT., and TutertB.Trip Distribution for Limited Destinations: A Case Study for Grocery Shopping Trips in the Netherlands. Presented at 89th Annual Meeting of the Transportation Research Board, Washington, D.C., 2010.
12.
FengS., and LiX.Research on Public Transit Passenger OD Matrix Estimation. Proc. 8th International Conference on Applications of Advanced Technologies in Transportation Engineering (SinhaK. C., FwaT. F., CheuR. L., and LeeD.-H., eds.), Beijing, China, ASCE, Reston, Va., 2004, pp. 417–420.
13.
NazemM., TrépanierM., and MorencyC.Hierarchical Intervening Opportunities Model for Public Transit Trip Distribution. Presented at Conference on Advanced Systems for Public Transit (CASPT), Pontificia Universidad Catolica de Chile, Santiago, Chile, 2012.
De GrangeL., TroncosaR., IbeasA., and GonzalezF.Gravity Model Estimation with Proxy Variables and the Impact of Endogeneity on Transportation Planning. Transportation Research A, Vol. 43, No. 2, 2009, pp. 105–116.
16.
Thamizh ArasanV., WermuthM., and SrinivasB. S.Modelling of Stratified Urban Trip Distribution. Journal of Transportation Engineering, Vol. 122, No. 5, 1996, pp. 342–349.
17.
RajessonF.Modèle Hybride d'Estimation de la Demande de Transport Collectif.Département de Mathématiques et de Génie Industriel, école Polytechnique de Montréal, Montreal, Quebec, Canada, 2009.
18.
StoufferS. A.Intervening Opportunities: A Theory Relating Mobility and Distance. American Sociological Review, Vol. 5, No. 6, 1940, pp. 845–867.
19.
StoufferS. A.Intervening Opportunities and Competing Migrants. Journal of Regional Science, Vol. 2, No. 1, 1960, pp. 1–26.
20.
SchneiderM.Gravity Models and Trip Distribution Theory. Papers in Regional Science, Vol. 5, No. 1, 1959, pp. 51–56.
21.
HensherD. A.Urban Transport Economics.Cambridge University Press, Cambridge, United Kingdom, 1977.
22.
EashR.Several More Improvements in Understanding, Calibrating, and Applying the Opportunity Mode.Chicago Area Transportation Study, Chicago, Ill., 1983.
23.
EashR.Development of a Doubly Constrained Intervening Opportunity Model for Trip Distribution.Chicago Area Transportation Study, Chicago, Ill., 1984.
24.
YaldiG., TaylorM. A. P., and YueW. L.Forecasting Origin-Destination Matrices by Using Neural Network Approach: A Comparison of Testing Performance Between Back Propagation, Variable Learning Rate, and Levenberg-Marquardt Algorithms. Proc. Australasian Transport Research Forum, Adelaide, Australia, Planning and Transport Research Centre, Western Australia, 2011. http://www.patrec.org/atrf.aspx.
25.
Mobilité des Personnes.Secrétariat à l'Enquête Origine-Destination, Agence Métropolitaine de Transport, Montreal, Quebec, Canada, 2010.
26.
BlackW. R.A Note on the Use of Correlation Coefficients for Assessing Goodness-of-Fit in Spatial Interaction Models. Transportation, Vol. 18, No. 3, 1991, pp. 199–206.
27.
SmithD. P., and HutchinsonB. G.Goodness-of-Fit Statistics for Trip Distribution Models. Transportation Research A, Vol. 15, No. 4, 1981, pp. 295–303.
28.
HuP., and PoolerJ.An Empirical Test of the Competing Destinations Model. Journal of Geographical Systems, Vol. 4, No. 3, 2002, pp. 301–323.
29.
EvansN. J., and PoolerJ.Distance Deterrence Effects in Constrained Spatial Interaction Models of Interprovincial Migration. Canadian Journal of Regional Science, Autumn 1987, pp. 259–279.
30.
AkwawuaS., and PoolerJ.The Development of an Intervening Opportunities Model with Spatial Dominance Effects. Journal of Geographical Systems, Vol. 3, No. 1, 2001, pp. 69–86.
31.
2006 Census of Population, Quebec. Cat. No. 94-581-X2006002.StatCan, Ottawa, Ontario, Canada, 2011.
KnudsenD. C., and FotheringhamA. S.Matrix Comparison, Goodness-of-Fit, and Spatial Interaction Modeling. International Regional Science Review, Vol. 10, No. 2, 1986, pp. 127–147.
34.
SheraliH. D., NarayananA., and SivanandanR.Estimation of Origin-Destination Trip-Tables Based on a Partial Set of Traffic Link Volumes. Transportation Research B, Vol. 37, No. 9, 2003, pp. 815–836.
35.
NazemM., TrépanierM., and MorencyC.Demographic Analysis of Route Choice for Public Transit. In Transportation Research Record: Journal of the Transportation Research Board, No. 2217, Transportation Research Board of the National Academies, Washington, D.C., 2011, pp. 71–78.
