Mixed Logit Model Application in Mode Choice: Case of Mashhad Work Trips

Document Type : Research Article

Authors

Faculty of Civil & Environmental Engineering, Tarbiat Modares University, Tehran, Iran

Abstract

Modal split models, as the third step in the four-step transportation modeling framework, determine the share of different travel modes. Choice models as probability models have been used in recent decades and have faced significant progress. The mixed logit model has been known for many years, but has only become fully applicable since the advent of computer and simulation technology. This model can approximate various random utility models according to the accuracy required, through adopting appropriate distributions for attributes coefficients in the utility function. The purpose of this research is to present a mixed logit model structure for mode choice, in order to describe the taste variation among individuals and the source of the variation in response to the various attributes that influence the mode choice. The required data is from Mashhad O-D survey in 1387 and model calibration is executed in Biogeme software. Results of mixed logit model indicates among passengers a taste variation in choosing between a personal car and motorcycle, based on car and motorcycle ownership. The source of this taste variation is modeled and captured through random coefficient analysis. Finally, it is shown that mixed logit models are superior to multinomial logit model with a confidence level of 99 percent. The superiority is however small, partly due to the inadequacy of the aggregate data.

Keywords

Main Subjects

References

[1] L.G. Willumsen, Modelling transport, John Wiley & Sons, 2011.
[2] M.E. Ben-Akiva, S.R. Lerman, Discrete choice analysis: theory and application to travel demand, MIT press, 1985.
[3] S. Hess, M. Bierlaire, J.W. Polak, Estimation of value of travel-time savings using mixed logit models, Transportation Research Part A: Policy and Practice, 39(2) (2005) 221-236.
[4] D. McFadden, Analysis of Qualitative Choice Behavior. Zarembka, P.(ed.): Frontiers in Econometrics, in, Academic Press. New York, NY, 1974.
[5] K.E. Train, Discrete choice methods with simulation, Cambridge university press, 2009.
[6] J. Berkson, Application of the logistic function to bio-assay, Journal of the American Statistical Association, 39(227) (1944) 357-365.
[7] A. rezaei, Modeling Air Itinerary Choice in Low a Frequency Market, Case of: International Travels from Tehran Airport, Sharif University of Technology, 2011. (in Persian)
[8] C. Daganzo, Multinominal Probit: The Theory and its Applications to Demand Modeling, in, Academic Press, 1979.
[9] Daganzo, The Theory and its Applications to Demand Forecasting, 1979.
[10] D. McFadden, K. Train, Mixed MNL models for discrete response, Journal of applied Econometrics, (2000) 447-470.
[11] L. Huamin, H. Huang, L. Jianfeng, Parameter estimation of the mixed Logit model and its application, Journal of Transportation Systems Engineering and Information Technology, 10(5) (2010) 73-78.
[12] J.H. Boyd, R.E. Mellman, The effect of fuel economy standards on the US automotive market: an hedonic demand analysis, Transportation Research Part A: General, 14(5-6) (1980) 367-378.
[13] N.S. Cardell, F.C. Dunbar, Measuring the societal impacts of automobile downsizing, Transportation Research Part A: General, 14(5-6) (1980) 423-434.
[14] S. Hess, Advanced discrete choice models with applications to transport demand, University of London, 2005.
[15] Hess, Advanced discrete choice model with applications to transport demand, London, 2005.
[16] S.R. Mehndiratta, TIME-OF-DAY EFFECTS IN INTER-CITY BUSINESS TRAVEL, 1996.
[17] D.A. Hensher, W.H. Greene, The mixed logit model: The state of practice and warnings for the unwary, Institute of Transport Studies, the University of Sydney and Monash University, 2002.
[18] K. Train, G. Sonnier, Mixed logit with bounded distributions of correlated partworths, Applications of simulation methods in environmental and resource economics, (2005) 117-134.
[19] R.-C. Jou, D.A. Hensher, T.-L. Hsu, Airport ground access mode choice behavior after the introduction of a new mode: A case study of Taoyuan International Airport in Taiwan, Transportation Research Part E: Logistics and Transportation Review, 47(3) (2011) 371-381.
[20]
[21] D.A. Hensher, J.M. Rose, W.H. Greene, Applied choice analysis: a primer, Cambridge University Press, 2005.
[22] C. Cirillo, P. Hetrakul, Continuous random coefficient logit models: a comparison of parametric and non-parametric methods to estimate individual preferences over Cybernetic Transportation Systems, in: Transportation Research Board 89th Annual Meeting, 2010.
[23] T. Adler, C. Falzarano, G. Spitz, Modeling service trade-offs in air itinerary choices, Transportation Research Record: Journal of the Transportation Research Board, (1915) (2005) 20-26.
[24] T. Adler, C. Falzarano, G. Spitz, Modeling service trade-offs in air itinerary choices, Transportation Research Record: Journal of the Transportation Research Board, (1915) (2005) 20-26.
[25] H. Poorzahedi, A.r. Mamdoohi, Mode choice modeling for work trips: case study of Mashhad city, Program and development, 2002. (in Persian)
[26] s.a. mirmohammadi, Mixed logit model application in mode choice: case of Mashhad work trips, Tarbiat Modares University, 2013. (in Persian)
[27] M. Bierlaire, Estimation of discrete choice models with BIOGEME 1.8, Transport and Mobility Laboratory, EPFL, Lausanne, Switzerland, 2009.
[28] K. Train, Halton sequences for mixed logit, Department of Economics, UCB, 2000.
[29] S. Hess, J.W. Polak, Mixed logit modelling of airport choice in multi-airport regions, Journal of Air Transport Management, 11(2) (2005) 59-68.
Amirkabir Journal of Civil Engineering
Volume 49, Issue 3
November 2017
Pages 581-592

Files

Share

How to cite

Statistics