The need for travel demand models is growing worldwide. Obtaining reasonably accurate level of service (LOS) attributes of different travel modes such as travel time and cost representing the performance of transportation system is not a trivial task, especially in growing cities of developing countries. This study investigates the sensitivity of results of a travel mode choice model to different specifications of network-based LOS attributes using a mixed logit model. The study also looks at the possibilities of correcting some of the inaccuracies in network-based LOS attributes. Further, the study also explores the effects of different specifications of LOS data on implied values of time and aggregation forecasting. The findings indicate that the implied values of time are very sensitive to specification of data and model implying that utmost care must be taken if the purpose of the model is to estimate values of time. Models estimated on all specifications of LOS-data perform well in prediction, likely suggesting that the extra expense on developing a more detailed and accurate network models so as to derive more precise LOS attributes is unnecessary for impact analyses of some policies.
Data Specification, Level of Service Attributes, Travel Mode Choice, Network Models, Mixed Logit, Error Components Logit
nullB. Bhatta, "The Sensitivity of Model Results to Specification of Network-Based Level of Service Attributes: An Application of a Mixed Logit Model to Trave Mode Choice," Journal of Transportation Technologies, Vol. 1 No. 3, 2011, pp. 34-46. doi: 10.4236/jtts.2011.13006.
[1] J. D. Ortúzar and L.G. Willumsen, “Modeling Transport,” 3rd Edition, John Wiley & Sons Ltd, England, 2001.
[2] J. Walker, L. Jieping, S. Sirinivasan and D. Bolduc, “Travel Demand Models in the Developing World: Correcting for Measurement Errors,” TRB 87th Annual Meeting Compendium of Papers DVD, Washington D.C., 13-17 January 2008.
[3] A. Madslien, J. Rekdal and O. I. Larsen, “Development of Regional Models for Passenger Transport in Norway,” (in Norwegian with English Summary), Report 766/2005, Institute of Transport Economics, Oslo, Norway, 1 July, 2005.
[4] J. Rekdal, and O. I. Larsen, “Regional Modell for Oslo- omr?det: Dokumentasjon av Utviklinsarbeid og Teknisk Innf?ring i Anvendelse (RTM23+),” (in Norwegian with English summary), Rapport, M?reforsking Molde, Norway, 16-18 July 2008.
[5] W. Alonso, “Predicting Best with Imperfect Data,” Jour- nal of the American Planning Association, Vol. 34, No. 4, 1968, pp. 248-255. doi:10.1080/01944366808977813
[6] A. Daly and J. D. Ortuzar, “Forecasting and Data Aggregation: Theory and Practice,” Traffic Engineering and Control, Vol. 31, No. 12, 1990, pp. 632-643.
[7] F. A. Reid, and K. Small, “Effects of Temporal Disaggregation of Trip Data on Traveler Behavior Models,” Transportation Research Forum Annual Meeting, MIT, Mass., 1976.
[8] K. Train, “The Sensitivity of Parameter Estimates to Data Specification in Mode Choice Models,” Transportation, Vol. 7, 1978, pp. 301-309. doi:10.1007/BF00165499
[9] J. D. Ortúzar, and A. Ivelic, “Efectos de la Desagregación Temporal de Variables de Servicio en la Especificación y Estabilidad de Funciones de Demanda,” Actas del IV Congreo Panamerican de Ingenieria de Transito y Trans- porte, Santiago, Chile, December, (in Spanish), 1986.
[10] J. D. Ortúzar, and A. Ivelic, “Effects of More Accurately Measured Level of Service Variables on the Specification and Stability of Mode Choice Models,” Proceedings of Seminar C held at the PTRC summer annual meeting, University of Bath, England, 7-11 September, PTRC E- ducation and Research Ltd, 1987.
[11] S.S.C Steimetz and D. Brownstone, “Estimating Commuters’ Value of Time with Noisy Data: A Multiple imputation Approach,” Transportation Research B, Vol. 39, 2005, pp. 865-889. doi:10.1016/j.trb.2004.11.001
[12] A. Monzon and A. Rodriguez-Dapena, “Choice of Mode of Transport for Long-Distance Trips: Solving the Problem of Sparse Data,” Transportation Research A, Vol. 40, 2006. 587-601. doi:10.1016/j.tra.2005.11.007
[13] B. P. Bhatta, “Discrete Choice Analysis with Applications to Travel Demand,” Ph.D. thesis, Molde University College, Molde, Norway, 2009.
[14] B. P. Bhatta and O. I. Larsen, “Errors in Variables in Multinomial Choice Modeling: A Simulation Study Applied to a Multinomial Logit model of Travel Mode Choice,” Transport Policy, Vol. 18, 2011, pp. 326-335. doi:10.1016/j.tranpol.2010.10.002
[15] J. M. Denstadli, R. Hjorthol, A. Rideng and J. I. Lian, “Den Norske Befolkningens Reiser,” (in Norwegian with English summary) T?I rapport 637/2003, Institute of Transport Economics, Oslo, 2003.
[16] B. P. Bhatta and O. I. Larsen, “Are Intrazonal Trips Ignorable?” Transport Policy, Vol. 18, 2011, pp. 13-22. doi:10.1016/j.tranpol.2010.04.004
[17] M. Ben-Akiva and S. R. Lerman, “Discrete Choice Analysis: Theory and Application to Travel Demand,” MIT Press, Cambridge, Mass. 1985.
[18] K. Train, “Qualitative Choice Analysis: Theory, Econometrics and Application to Automobile Demand,” MIT Press, Cambridge, Mass., 1986.
[19] K. Train, “Discrete Choice Methods with Simulation,” Cambridge University Press, Cambridge, UK. 2003. doi:10.1017/CBO9780511753930
[20] D. McFadden, “Disaggregate Behavioral Travel Demand’s RUM Side: A 30-Year Perspective,” Paper presented at Conference on the International Association of Travel Behavior Research, July 2, Brisbane, Australia, 2000.
[21] S. Hess, J. W. Polak, A. Daly and G. Hyman, “Flexible Substitution Patterns in Models of Mode and Time of Day Choice: New Evidence from the UK and the Netherlands,” Transportation, Vol. 34, 2007, pp. 213-238. doi:10.1007/s11116-006-0011-7
[22] S. Hess, “Advanced Discrete Choice models with Applications to Transport Demand,” Ph.D. thesis. Center for Transport Studies, Imperial College London, 2005.
[23] J. H. Boyd and R. E. Mellman, “The Effect of Fuel Economy Standards on the U.S. Automotive Market: A Hedonic Demand Analysis,” Transportation Research A, Vol. 14, 1980, pp. 367-378. doi:10.1016/0191-2607(80)90055-2
[24] N. S. Cardell and F. C. Dunbar, “Measuring the Societal Impacts of Automobile Downsizing,” Transportation Research A, Vol. 14, 1980, pp. 423-434. doi:10.1016/0191-2607(80)90060-6
[25] G. Glasgow, “Mixed Logit Models for Multiparty Elections,” Political Analysis, Vol. 9, 2001, pp. 116-136.
[26] J. A. Herriges and D. J. Phaneuf, “Inducing Patterns of Correlation and Substitution in Repeated Logit Models of Recreation Demand,” American Journal of Agricultural Economics, Vol. 84, 2002, pp. 1076-1090. doi:10.1111/1467-8276.00055
[27] S. Hess and J. W. Polak, “Accounting for Random Taste Heterogeneity in Airport-Choice Modeling,” Transporta- tion Research Record, Vol. 1915, 2005, pp. 36-43. doi:10.3141/1915-05
[28] C. P. Barros and I. Proenca, “Mixed Logit Estimation of Radical Islamic Terrorism in Europe and North America: A Comparative Study,” Journal of Conflict Resolution, Vol. 49, 2005. 298-314. doi:10.1177/0022002704272829
[29] D. Hensher, J. Steward and W. H. Greene, “An Error Component Analysis of Corporate Bankruptcy and Insolvency Risk in Australia,” Economic Record, Vol. 83, 2007, pp. 86-103. doi:10.1111/j.1475-4932.2007.00378.x
[30] D. McFadden and K. Train, “The mixed MNL Models for Discrete Response,” Journal of Applied Econometrics, Vol. 15, 2000, pp. 447-470. doi:10.1002/1099-1255(200009/10)15:5<447::AID-JAE570>3.0.CO;2-1
[31] J. Walker, M. Ben-Akiva and D. Bolduc, “Identification of Parameters in Normal Error Components Logit Mixture Models,” Journal of Applied Econometrics, Vol. 22, 2007, pp. 1095-1125. doi:10.1002/jae.971
[32] G. de Jong, A. Daly, M. Pieters , C. Vellay, M. A. Bradley and F. Hofman, “A Model for Time of Day and Mode Choice Using Error components Logit,” Transportation Research E, Vol. 39, 2003, pp. 245-268. doi:10.1016/S1366-5545(02)00037-6
[33] M. Bierlaire, “BIOGEME: A Free Package for the Estimation of Discrete Choice Models,” Proceedings of the 3rd Swiss Transportation Research Conference, Ascona, Switzerland, March 2003. (http://biogeme.epfl.ch)