Article
TEMPORAL AND PARAMETRIC STUDY OF TRAVELLER PREFERENCE HETEROGENEITY USING RANDOM PARAMETER LOGIT MODEL
DOI: 10.7708/ijtte.2014.4(4).07
4 / 4 / 437-455 Pages
Author(s)
AHM Mehbub Anwar - SMART Infrastructure Facility, University of Wollongong, Wollongong NSW 2522, Australia -
Abstract
In travel demand models, traditional objective attributes (TOAs) are very commonly used as explanatory variables. Nowadays, it is understood that latent variables (LVs) also significantly influence travellers’ behaviour. A hybrid choice modelling approach allows LVs in mode choice utility functions to be addressed. Specifically, a hybrid random parameter logit (HRPL) model has been developed to explore these influences. In this study, a traditional RPL (TRPL) model is compared with an HRPL model. For the later model, a two-step approach (also known as sequential approach) is implemented to incorporate LVs in choice models. Step 1 is the estimation of a MIMIC (multiple indicators and multiple causes) model; a type of regression model with a latent dependent variable(s). Step 2 is the estimation of a choice model with random parameters; information from the first step is incorporated in the second step. The paper analyses and compares the results of applying these models to a real urban case study using two datasets: 2008/09 and 2010/11 household travel survey (HTS) of Sydney Statistical Division (SSD), and also evaluates the predicted changes of mode choice probabilities based on hypothetical scenarios. Our results show that the HRPL model is superior to TRPL models that ignore the effect of LVs on traveller choice. The minimal changes in the parameter coefficients between the two datasets for each model suggest that the changes in traveller choice behaviour are gradual. Three hypothetical scenarios are simulated to forecast the changes that would be relevant to transport policy responses.
Number of downloads: 1766
Keywords:
latent variables;
traditional objective variables;
modelling;
comparison;
forecasting;
logit model;
mode choice;
policy;
Acknowledgements:
The authors are grateful to the personnel of Bureau of Transport Statistics (BTS) affiliated with Transport for NSW, Australia for providing access to the largest and most comprehensive household travel survey data of SSD. The University of Wollongong, especially SMART Infrastructure Facility, also deserves special thanks for financial support to carry out this research.
References:
Anwar, A.H.M.M.; Tieu, K.; Gibson, P.; Win, K.T.; Berryman, J.M. 2014. Analysing the heterogeneity of traveller mode choice preference using a random parameter logit model from the perspective of principal-agent theory, International Journal of Logistics Systems and Management. DOI: http://dx.doi.org/10.1504/IJLSM.2014.061015, 17(4): 447-471.
Anwar, A.H.M.M.; Tieu, K.; Gibson, P.; Win, K.T.; Berryman, J.M. 2013. Analysing the merit of latent variables over traditional objective attributes for traveller mode choice using RPL model. In Proceedings of the 3rd International Choice Modelling Conference, online, Sydney.
Anwar, A.H.M.M.; Tieu, K.; Gibson, P.; Berryman, J.M.; Win, K.T. 2011. Structuring the influence of latent variables in traveller preference heterogeneity. In Transportdynamics – Proceedings of the 16th International Conference of Hong Kong Society for Transportation Studies, (ed. W.Y. Szeto, S.C. Wong and N.N. Sze): 141-148.
Ashok, K.; William, R.D.; Yuan, S. 2002. Extending discrete choice models to incorporate attitudinal and other latent variables, Journal of Marketing Research. DOI: http://dx.doi.org/10.1509/jmkr.39.1.31.18937, 39(1): 31-46.
Ben-Akiva, M.; McFadden, D.; Train, K.; Walker, J.; Bhat, C.; Bierlaire, M.; Bolduc, D.; Boersch-Supan, A.; Brownstone, D.; Bunch, D.S.; Daly, A.; Palma, A.D.; Gopinath, D.; Karlstrom, A.; Munizaga, M.A. 2002a. Hybrid choice models: progress and challenges, Marketing Letters. DOI: http://dx.doi.org/10.1023/A:1020254301302, 13(3): 163-175.
Ben-Akiva, M.; Walker, J.L.; Bernardino, A.T.; Gopinath, D.A.; Morikawa, T.; Polydoropoulou, A. 2002b. Integration of choice and latent variable models. In Perpetual motion: travel behaviour research opportunities and challenges, (ed. HS Mahmassani), Amsterdam Pergamon: 431-470.
Ben-Akiva, M.; Bradley, M.; Morikawa, T.; Benjamin, J.; Novak, T.; Oppewal, H.; Rao, V. 1994. Combining revealed and stated preferences data, Marketing Letters. DOI: http://dx.doi.org/10.1007/BF00999209, 5(4): 335-350.
Bhat, C.R. 1998. Accommodating variations in responsiveness to level-of-service measures in travel mode choice modelling, Transportation Research Part A: Policy and Practice. DOI: http://dx.doi.org/10.1016/S0965-8564(98)00011-1, 32(7): 495-507.
Bhat, C.R. 2000. Incorporating observed and unobserved heterogeneity in urban work travel mode choice modelling, Transportation Science. DOI: http://dx.doi.org/10.1287/trsc.34.2.228.12306, 34(2): 228-238.
Bolduc, D.A. 1999. Practical technique to estimate multinomial probit models in transportation, Transportation Research Part B: Methodological. DOI: http://dx.doi.org/10.1016/S0191-2615(98)00028-9, 33(1): 63-79.
Bolduc, D.; Boucher, N.; Alvarez-Daziano, R. 2008. Hybrid choice modelling of new technologies for car choice in Canada, Transportation Research Record: Journal of the Transportation Research Board. DOI: http://dx.doi.org/10.3141/2082-08, 2082(2008): 63-71.
Bureau of Transport Statistics (BTS). 2012. 2010/11 Household travel survey summary report, 2012 release, Transport for New South Wales, Sydney.
Byrne, B.M. 2010. Structural equation modelling with AMOS: Basic concepts, applications, and programming, 2nd ed., New York, Routledge.
Can, V.V. 2013. Estimation of travel mode choice for domestic tourists to Nha Trang using multinomial probit model, Transportation Research Part A: Policy and Practice. DOI: http://dx.doi.org/10.1016/j.tra.2013.01.025, 49: 149-159.
Cao, X.; Mokhtarian, P.L.; Handy, S.L. 2009. Examining the impacts of residential self-selection on travel behaviour: A focus on empirical findings, Transport Reviews: A Transnational Transdisciplinary Journal. DOI: http://dx.doi.org/10.1080/01441640802539195, 29(3): 359-395.
Choo, S.; Mokhtarian, P.L. 2004. What types of vehicle do people drive.? The role of attitude and lifestyle in influencing vehicle type choice, Transportation Research Part A: Policy and Practice. DOI: http://dx.doi.org/10.1016/j.tra.2003.10.005, 38(3): 201-222.
Cohen, A.; Harris, N. 1998. Mode choice for VFR journeys, Journal of Transport Geography. DOI: http://dx.doi.org/10.1016/S0966-6923(97)00038-0, 6(1): 43-51.
Commins, N.; Nolan, A. 2011. The determinants of mode of transport to work in the greater Dublin area, Transport Policy. DOI: http://dx.doi.org/10.1016/j.tranpol.2010.08.009, 18(1): 259-268.
Daly, A.; Hess, A.; Patruni, B.; Potoglou, D.; Rohr, C. 2012. Using ordered attitudinal indicators in a latent variable choice model: A study of the impact of security on rail travel behaviour, Transportation. DOI: http://dx.doi.org/10.1007/s11116-011-9351-z, 39(2): 267-297.
Dissanayake, D.; Morikawa, T. 2005. Household travel behaviour in developing countries: Nested logit model of vehicle ownership, mode choice, and trip chaining, Transportation Research Record: Journal of the Transportation Research Board. DOI: http://dx.doi.org/10.3141/1805-06, 1805(2002): 45-52.
Domarchi, C.; Tudela, A.; Gonzalez, A. 2008. Effect of attitudes, habit and affective appraisal on mode choice: An application to university workers, Transportation. DOI: http://dx.doi.org/10.1007/s11116-008-9168-6, 35(5): 585-599.
Ewing, R.; Schroeer, W.; Greene, W. 2004. School location and student travel analysis of factors affecting mode choice, Transportation Research Record: Journal of the Transportation Research Board, 1895: 55-63.
Fesenmaier, D.R. 1988. Integrating activity patterns into destination choice models, Journal of Leisure Research, 20(3): 175-191.
Fleischer, A.; Tchetchik, A.; Toledo, T. 2012. The impact of fear of flying on travellers' flight choice: Choice model with latent variables, Journal of Travel Research. DOI: http://dx.doi.org/10.1177/0047287512437856, 51(5): 653-663.
Fujii, S.; Garling, T. 2003. Application of attitude theory for improved predictive accuracy of stated preference methods in travel demand analysis, Transportation Research Part A: Policy and Practice. DOI: http://dx.doi.org/10.1016/S0965-8564(02)00032-0, 37(4): 389-402.
Gopinath, A.D. 1995. Modeling heterogeneity in discrete choice processes: application travel demand, PhD thesis, Massachusetts Institute of Technology, USA.
Greene, W.H.; Hensher, D.A. 2003. A latent class model for discrete choice analysis: contrasts with mixed logit, Transportation Research Part B: Methodological. DOI: http://dx.doi.org/10.1016/S0191-2615(02)00046-2, 37(8): 681-698.
Habib, K.M.N. 2012. Modeling commuting mode choice jointly with work start time and work duration, Transportation Research Part A: Policy and Practice. DOI: http://dx.doi.org/10.1016/j.tra.2011.09.012, 46(1): 33-47.
Habib, K.M.N.; Zaman, M.H. 2012. Effects of incorporating latent and attitudinal information in mode choice models, Transportation Planning and Technology. DOI: http://dx.doi.org/10.1080/03081060.2012.701815, 35(5): 561-576.
Hess, S.; Stathopoulos, A. 2011. Linking response quality to survey engagement: A combined random scale and latent variable approach. In Proceedings of the 2nd International Choice Modelling Conference, Leeds, UK.
Hess, S.; Stathopoulos, A.; Daly, A. 2011. Allowing for heterogeneous decision rules in discrete choice models: an approach and four case studies. In Proceedings of the 2nd International Choice Modelling Conference, Leeds, UK.
Jialing, H.; Jun, L.; Xinjun, L.; Honggang, X. 2013. Modal choice of recreational tourists under regional transportation integration: A case study of pearl river delta, Applied Mechanics and Materials. DOI: http://dx.doi.org/10.4028/www.scientific.net/AMM.253-255.287, 253-255: 287-292.
Johansson, M.V.; Heldt, T.; Johansson, P. 2006. The effects of attitudes and personality traits on mode choice, Transportation Research Part A: Policy and Practice. DOI: http://dx.doi.org/10.1016/j.tra.2005.09.001, 40(6): 507-525.
McCarthy, P. 1996. Market price and income elasticities of new vehicle demands, The Review of Economics and Statistics, 78(3): 543-547.
Mokhtarian, P.L. 1998. A synthetic approach to estimating the impacts of telecommuting on travel, Urban Studies. DOI: http://dx.doi.org/10.1080/0042098984952, 35(2): 215-241.
Nicolau, J.L.; Mas, F.J. 2006. The influence of distance and prices on the choice of tourist destinations: The moderating role of motivations, Tourism Management. DOI: http://dx.doi.org/10.1016/j.tourman.2005.09.009, 27(5): 982-996.
Ortuzar, J.de D.; Willumsen, L.G. 2001. Modelling transport, 3rd ed. Chichester, John Wiley and Sons.
Raveau, S.; Alvarez-Daziano, R.; Yanez, M.F.; Bolduc, D.; Ortuzar, J.de D. 2010. Sequential and simultaneous estimation of hybrid discrete choice models: Some new findings, Transportation Research Record: Journal of the Transportation Research Board. DOI: http://dx.doi.org/10.3141/2156-15, 2156(2010): 131-139.
Sheriff, K.M.M.; Gunasekaran, A.; Nachiappan, S. 2012. Reverse logistics network design: A review on strategic perspective, International Journal of Logistics Systems and Management. DOI: http://dx.doi.org/10.1504/IJLSM.2012.047220, 12(2): 171-194.
Srinivasan, S.; Walker, J.L. 2009. Vehicle ownership and mode use: The challenge of sustainability, Transportation. DOI: http://dx.doi.org/10.1007/s11116-009-9205-0, 36(4): 367-370.
Temme, D.; Paulssen, M.; Dannewald, T. 2008. Incorporating latent variables into discrete choice models – A simultaneous estimation approach using SEM software, BuR Business Research Journal. DOI: http://dx.doi.org/10.1007/BF03343535, 1(2): 220-237.
Train, K. 2009. Discrete choice method with simulation, 2nd ed. New York, Cambridge University Press.
Train, K.E. 1998. Recreation demand models with taste differences over people, Land Economics, 74(2): 230-239.
Train, K.E. 1980. A structured logit model of auto ownership and mode choice, Review of Economic Studies. DOI: http://dx.doi.org/10.2307/2296997, 28(2): 357-370.
Walker, J.L.; Ben-Akiva, M. 2002. Generalized random utility models, Mathematical Social Sciences. DOI: http://dx.doi.org/10.1016/S0165-4896(02)00023-9, 43(3): 303-343.
Washbrook, K.; Haider, W.; Jaccard, M. 2006. Estimating commuter mode choice: A discrete choice analysis of the impact of road pricing and parking charge, Transportation. DOI: http://dx.doi.org/10.1007/s11116-005-5711-x, 33(6): 621-639.
Zeid, M.A. 2009. Measuring and modelling activity and travel well-being, PhD thesis, Massachusetts Institute of Technology.
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