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Multiple classification analysis in trip production models

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  • Guevara, Cristian Angelo
  • Thomas, Alan

Abstract

We analyse various Multiple Classification Analysis (MCA) methods to model trip production (generation). We first show that the MCA version most widely used in transport engineering implies a rarely feasible assumption, the transgression of which may drive a significant overestimation of the future number of trips and a systematic bias in its socio-economic composition. To illustrate this effect, we use Monte Carlo simulation and real data from Santiago, Chile to compare the various MCA approaches, concluding that the aforementioned form should be discarded. Our analysis also shows that the MCA method which is more robust to the structure of the underlying model, is the simple calculation of trip rates as averages for each category. Finally, we hint at the need to use more sophisticated formulations than MCA to model trip production.

Suggested Citation

  • Guevara, Cristian Angelo & Thomas, Alan, 2007. "Multiple classification analysis in trip production models," Transport Policy, Elsevier, vol. 14(6), pages 514-522, November.
    • Handle: RePEc:eee:trapol:v:14:y:2007:i:6:p:514-522
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    Citations

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    Cited by:

    1. Guevara, C. Angelo, 2018. "Overidentification tests for the exogeneity of instruments in discrete choice models," Transportation Research Part B: Methodological, Elsevier, vol. 114(C), pages 241-253.
    2. André Romano Alho & João Abreu e Silva, 2017. "Modeling retail establishments’ freight trip generation: a comparison of methodologies to predict total weekly deliveries," Transportation, Springer, vol. 44(5), pages 1195-1212, September.
    3. Guevara, C. Angelo, 2015. "Critical assessment of five methods to correct for endogeneity in discrete-choice models," Transportation Research Part A: Policy and Practice, Elsevier, vol. 82(C), pages 240-254.
    4. Lucas, Karen & Bates, John & Moore, José & Carrasco, Juan Antonio, 2016. "Modelling the relationship between travel behaviours and social disadvantage," Transportation Research Part A: Policy and Practice, Elsevier, vol. 85(C), pages 157-173.
    5. Guevara, C. Angelo & Tirachini, Alejandro & Hurtubia, Ricardo & Dekker, Thijs, 2020. "Correcting for endogeneity due to omitted crowding in public transport choice using the Multiple Indicator Solution (MIS) method," Transportation Research Part A: Policy and Practice, Elsevier, vol. 137(C), pages 472-484.
    6. Mwakalonge, Judith L. & Badoe, Daniel A., 2012. "Comparison of Alternative Methods for Estimating Household Trip Rates of Cross-Classification Cells with Inadequate Data," Journal of the Transportation Research Forum, Transportation Research Forum, vol. 51(2).
    7. de Grange, Louis & Troncoso, Rodrigo & González, Felipe, 2012. "An empirical evaluation of the impact of three urban transportation policies on transit use," Transport Policy, Elsevier, vol. 22(C), pages 11-19.
    8. C. Angelo Guevara, 2017. "Mode-valued differences of in-vehicle travel time Savings," Transportation, Springer, vol. 44(5), pages 977-997, September.
    9. Prasanta K. Sahu & Agnivesh Pani, 2020. "Freight generation and geographical effects: modelling freight needs of establishments in developing economies and analyzing their geographical disparities," Transportation, Springer, vol. 47(6), pages 2873-2902, December.

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