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Errors in variables in multinomial choice modeling: A simulation study applied to a multinomial logit model of travel mode choice

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  • Bhatta, Bharat P.
  • Larsen, Odd I.
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    Abstract

    Modeling travel demand is a vital part of transportation planning and management. Level of service (LOS) attributes representing the performance of transportation system and characteristics of travelers including their households are major factors determining the travel demand. Information on actual choice and characteristics of travelers is obtained from a travel survey at an individual level. Since accurate measurement of LOS attributes such as travel time and cost components for different travel modes at an individual level is critical, they are normally obtained from network models. The network-based LOS attributes introduce measurement errors to individual trips thereby causing errors in variables problem in a disaggregate model of travel demand. This paper investigates the possible structure and magnitude of biases introduced to the coefficients of a multinomial logit model of travel mode choice due to random measurement errors in two variables, namely, access/egress time for public transport and walking and cycling distance to work. A model was set up that satisfies the standard assumptions of a multinomial logit model. This model was estimated on a data set from a travel survey on the assumption of correctly measured variables. Subsequently random measurement errors were introduced and the mean values of the parameters from 200 estimations were presented and compared with the original estimates. The key finding in this paper is that errors in variables result in biased parameter estimates of a multinomial logit model and consequently leading to poor policy decisions if the models having biased parameters are applied in policy and planning purposes. In addition, the paper discusses some potential remedial measures and identifies research topics that deserve a detailed investigation to overcome the problem. The paper therefore significantly contributes to bridge the gap between theory and practice in transport.

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    Bibliographic Info

    Article provided by Elsevier in its journal Transport Policy.

    Volume (Year): 18 (2011)
    Issue (Month): 2 (March)
    Pages: 326-335

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    Handle: RePEc:eee:trapol:v:18:y:2011:i:2:p:326-335

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    Related research

    Keywords: Level of service attributes Errors in variables Travel mode choice Network models Bias;

    References

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    1. McFadden, Daniel L., 1984. "Econometric analysis of qualitative response models," Handbook of Econometrics, Elsevier, in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 2, chapter 24, pages 1395-1457 Elsevier.
    2. Hu, Yingyao, 2006. "Bounding parameters in a linear regression model with a mismeasured regressor using additional information," Journal of Econometrics, Elsevier, Elsevier, vol. 133(1), pages 51-70, July.
    3. Bound, John & Brown, Charles & Mathiowetz, Nancy, 2001. "Measurement error in survey data," Handbook of Econometrics, Elsevier, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 5, chapter 59, pages 3705-3843 Elsevier.
    4. Brownstone, David & Train, Kenneth, 1999. "Forecasting new product penetration with flexible substitution patterns," University of California Transportation Center, Working Papers qt1j6814b3, University of California Transportation Center.
    5. Kao, Chihwa & Schnell, John F., 1987. "Errors in variables in the multinomial response model," Economics Letters, Elsevier, Elsevier, vol. 25(3), pages 249-254.
    6. Li, Tong & Hsiao, Cheng, 2004. "Robust estimation of generalized linear models with measurement errors," Journal of Econometrics, Elsevier, Elsevier, vol. 118(1-2), pages 51-65.
    7. Stephane Hess & John Polak & Andrew Daly & Geoffrey Hyman, 2007. "Flexible substitution patterns in models of mode and time of day choice: new evidence from the UK and the Netherlands," Transportation, Springer, Springer, vol. 34(2), pages 213-238, March.
    8. Kang-tsung Chang & Zaher Khatib & Yanmei Ou, 2002. "Effects of zoning structure and network detail on traffic demand modeling," Environment and Planning B: Planning and Design, Pion Ltd, London, Pion Ltd, London, vol. 29(1), pages 37-52, January.
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    Cited by:
    1. Mariel Chladkova, Petr & Abdullah, Sabah & De Ayala Bilbao, Amaya & Hoyos Ramos, David, 2010. "Selecting random parameters in discrete choice experiment for environmental valuation: A simulation experiment," BILTOKI, Universidad del País Vasco - Departamento de Economía Aplicada III (Econometría y Estadística) 2010-09, Universidad del País Vasco - Departamento de Economía Aplicada III (Econometría y Estadística).
    2. Peer, Stefanie & Knockaert, Jasper & Koster, Paul & Tseng, Yin-Yen & Verhoef, Erik T., 2013. "Door-to-door travel times in RP departure time choice models: An approximation method using GPS data," Transportation Research Part B: Methodological, Elsevier, Elsevier, vol. 58(C), pages 134-150.

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