Article
MODE CHOICE ANALYSIS: THE DATA, THE MODELS AND FUTURE AHEAD
DOI: 10.7708/ijtte.2014.4(3).03
4 / 3 / 269-285 Pages
Author(s)
Ch. Ravi Sekhar - Transportation Planning Division, CSIR-Central Road Research Institute, New Delhi-110025, India -
Abstract
Mode choice is one of the most vital stages in transportation planning process and it has direct impact on the policy making decisions. Mode choice models deals very closely with the human choice making behaviour and thus continues to attract researchers for further exploration of commuter’s choice making process. The objective of this study is to carryout detailed review on various modeling methods of mode choice analysis and bottlenecks associated with the same. The factors that affect the psyche of the travelers have been discussed; further various types of data required and their method of collection has been briefed up. This paper particularly emphasizes on statistical mode choice models such as multinomial logit and probit models as well as recent advanced soft computing techniques such as Artificial Neural Network models (ANN) and Fuzzy approach model that are employed for modal split analysis. Comparative analysis were made among various modeling techniques for modeling the complex mode choice of behaviour of models carried out by various researchers in the literature and a discussion on the need of future hybrid soft computing models has been attempted.
Number of downloads: 9695
References:
Abdel-Aty, M.; Abdelwahab, H. 2001. Calibration of nested mode choice model for Florida, Final research report, University of central Florida.
Abuhamoud, M.A.A.; Atiq, R.; Rahmat, O.K.; Ismail, A. 2011. Modeling of Transport Mode in Libya: a Binary Logit Model for Government Transportation Encouragement, Australian Journal of Basic and Applied Sciences, 5(5): 1291-1296.
Adler, T.; R immer, L.; Carpenter, D. 2002. Use of Internet-Based Household Travel Diary Survey Instrument, Transportation Research Record: Journal of the Transportation Research Board. DOI: http://dx.doi.org/10.3141/1804-18, 1804: 134-143.
Ajzen, I. 1985. From intentions to actions: A theory of planned behavior. In Kuhl, J., Beckman, J. (Eds.), Action-control: From cognition to behavior. Heidelberg, Germany: Springer. 11-39.
Al-Ahmadi, H.M. 2006. Development of Intercity Mode Choice Models for Saudi Arabia, JKAU: Eng.Sci, 17(1): 3-21.
Algers, S.; Bergström, P.; Dahlberg, M.; Dillén, J.L. 1998. Mixed Logit Estimation of the Value of Travel Time, Swedish Institute for Transport and Communications Analysis (SIKA).
Ben-Akiva, M.E.; Lerman, S.R. 1985. Discrete Choice Analysis: Theory and Application to Travel Demand, The MIT Press, Cambridge, Massachusetts, the USA.
Ben-Akiva, M.E.; Morikawa, T. 1990a. Estimation of switching models from revealed preferences and stated intentions, Transportation Research Part A: General. DOI: http://dx.doi.org/10.1016/0191-2607(90)90037-7, 24(6): 485-495.
Ben-Akiva, M.E.; Morikawa, T. 1990b. Estimation of Travel Demand Models from Multiple Data Sources. In M. Koshi (Ed.), Transportation and Traffic Theory. Oxford: Elsevier Science Ltd. 461-476.
Bhat, C.R. 1995. A Heteroscedastic Extreme Value Model of Intercity Mode Choice, Transportation Research Part B: Methodological. DOI: http://dx.doi.org/10.1016/0191-2615(95)00015-6, 29(6): 471-483.
Bhat, C.R. 2000. Incorporating Observed and Unobserved Heterogeneity in Urban Work Mode Choice Modeling, Transportation Science. DOI: http://dx.doi.org/10.1287/trsc.34.2.228.12306, 34(2): 228-238.
Bhat, C.R.; Sardesai, R. 2006. The Impact of Stop-Making and Travel Time Reliability on Commute Mode Choice, Transportation Research Part B: Methodological. DOI: http://dx.doi.org/10.1016/j.trb.2005.09.008, 40(9): 709-730.
Can, V.V. 2013. Estimation of travel mode choice for domestic tour ists to Nha Trang using the 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.
Cantarella, G.E.; De Luca, S. 2003. Modeling Transportation Mode Choice through Artificial Neural Networks. In Proceedings of the Fourth International Symposium on Uncertainty Modeling and Analysis. DOI: http://dx.doi.org/10.1109/ISUMA.2003.1236145, 84-90.
Casas, J.; Arce, C. 1999. Trip Reporting in Household Travel Diaries: A Comparison to GPSCollected Data. Presented at the 78th Annual Meeting of the Transportation Research Board, Washington, D.C.
Daganzo, C. 1979. Multinomial Probit: The Theory and its Application to Demand Forecasting, Academic Press, New York.
Deb, S.K. 1993. Fuzzy set approach in mass transit mode choice. In Proceedings of the ISUMA ‘93, Second International Symposium on Uncertainty Modeling and Analysis. IEEE Computer Press, College Park, Maryland. 262-268.
Dow, J.K.; Endersby, J.W. 2004. Multinomial Probit and Multinomial Logit: A Comparison of Choice Models for Voting Research. Electoral Studies. 23 p.
Edara, P.K. 2003. Mode Choice Modeling Using Artificial Neural Networks, M.S. Thesis, Virginia Polytechnic Institute, Virginia.
Frank, L.; Bradley, M.; Kavage, S.; Chapman, J.; Lawton, T.K. 2008. Urban form, travel time, and cost relationships with tour complexity and mode choice, Transportation. DOI: http://dx.doi.org/10.1007/s11116-007-9136-6, 35(1): 37-54.
Ghareib, A .H. 1996. Estimation of Logit and Probit Models in a Mode Choice Situation, Journal of Transportation Engineering. DOI: http://dx.doi.org/10.1061/(ASCE)0733-947X(1996)122:4(282), 122(4): 282-290.
Hess, S. 2005. Advanced discrete choice models with applications to transport demand, Ph.D Thesis, Centre for Transport Studies Imperial College London.
Kalfs, N. 1995. The Effects of Different Data Collection Procedures in Time Use Research. Paper presented at the Transportation Research Board Annual Meeting, Washington, D.C.
Kalic, M.; Teodorovic, D. 1999. Modal Split Modeling Using Fuzzy Logic. In Proceedings of the Conference “Modelling and Management in Transportation”, Poznan-Cracow, Poland. 91-96.
Khan, O. 2007. Modelling Passenger Mode Choice Behavior Using Computer Aided Stated Preference Data, Ph.D Thesis, Queensland University of Technology.
Koppeleman, F.; Bhat, C. 2006. A Self Instructing Course in Mode Choice Modeling: Multinomial and Nested Logit Models, U.S. Department of Transportation, Federal Transit Administration.
Koppleman, F.S.; Wen, C-H. 2000. The Paired Combinatorial Logit Model: Properties, Estimation and Application, Transportation Research Part B: Methodological. DOI: http://dx.doi.org/10.1016/S0191-2615(99)00012-0, 32(2): 75-89.
Lerman, S. 1975. A Disaggregate Behavioral Model of Urban Mobility Decisions, Ph.D. Thesis, Department of Civil Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts.
Lesley, L. 2009. Generalized Cost and Value of Time As a Method of Patronage Forecasting, Acta Technica Jaurinensis, 2(1): 57-68.
Lisco, T.E. 1967. Value of Commuters time-A Study in Urban Transportation, PhD. Thesis, University of Chicago.
McFadden, D. 1978. Modelling the choice of residential location. In Karlquist A., ed., Spatial Interaction Theory and Planning Models, North Holland, Amsterdam. 75-96.
McFadden, D. 1980. Economet r ic models for Probabilistic choice among Products, The Journal of Business, 53(3): 13-29.
Mukala, P.K.; Chunchu, M. 2011. Mode choice modelling for intercity transportation in India: A case of Guwahati to five metro cities, International Journal of Earth Sciences and Engineering, 4(6): 364-374.
Ortúzar, J. de D.; Willumsen, L.G. 2002. Modeling Transport, 3rd Edition, JohnWiley & Sons, Chichester, Sussex, England.
Persson, U.; Nilsson, K.; Hjalte, K.; Norinder, A. 1998. Beräkningav Vägverkets riskvärden. En kombination av “contingent valuation” – skattningar och uppmätta hälsoförluster hos vägtrafikskadade personer behandlade vid fyra sjukhus. The Swedish Institute of Health Economics (IHE), Mimeo.
Pinjari, A.R.; Pendyala, R.M.; Bhat, C.R.; Waddell, P.A. 2007. Modeling residential sorting effects to understand the impact of the built environment on commute mode choice, Transportation. DOI: http://dx.doi.org/10.1007/s11116-007-9127-7, 34(5): 557-573.
Racca, D.P.; Ratledge, E.C. 2004. Factors That Affect and/or Can Alter Mode Choice, Delaware Transportation Institute and The State of Delaware Department of Transportation, University of Delaware, Newark.
Rao, S.P.V.; Sikdar, P.K. 1999. Calibration of fuzzy functions from revealed preference pattern in urban mode choice. CUPUM ‘99 Computers in Urban Planning and Urban Management On the edge of the millennium. In Proceedings of the 6th International Conference, Venice.
Ravi Sekhar, Ch. 1999. Mode choice Analysis using Artificial Neural Network, Master Thesis, Indian Institute of Technology, Roorkee.
Seyedabrishami, S.; Shafahi, Y. 2013. A joint model of destination and mode choice for urban trips: a disaggregate approach, Transportation Planning and Technology. DOI: http://dx.doi.org/10.1080/03081060.2013.851507, 36(8): 703-721.
Stratham, J.; Dueker, K. 1996. Transit service, parking charges, and mode choice for the journey to work: an analysis of the 1990 NPTS, Journal of Public Transportation, 1(1).
Van Wee, B.; Holwerda, H.; van Baren, R. 2003. Preferences for modes, residential location and travel behaviour: the relevance of land-use impacts on mobility, European Journal of Transport and Infrastructure Research, 2(3-4): 305-315.
Wang, D.; Borgers, A.; Oppewal, H.; Timmermans, H. 2000. A stated choice approach to developing multifaceted models of activity behavior, Transportation Research Part A: Policy and Practice. DOI: http://dx.doi.org/10.1016/S0965-8564(99)00045-2, 34(8): 625-643.
Wolf, J. 2004. Defining GPS and GPS Capabilities. In Hensher, D., Button, K., Haynes, K., Stopher, P. (editors), Handbook on Transport Geography and Spatial Systems, Elsevier, Handbook No. 5, Chapter 20.
Xie, C.; Lu, J.; Parkany, E. 2003. Work Travel Mode Choice Modeling Using Data Mining: Decision Trees And Neural Networks, Transportation Research Record: Journal of the Transportation Research Board. DOI: http://dx.doi.org/10.3141/1854-06, 1854: 50-61.
Yaldi, G.; Taylor, A.P.; Yue, W.L. 2010. Examining the Possibility of Fuzzy Set Theory Application in Travel Demand Modelling, Journal of the Eastern Asia Society for Transportation Studies, 8: 579-592.
Yang, L.; Zheng, G.; Zhu, X. 2013. Cross-nested logit model for the joint choice of residential location, travel mode, and departure time, Habitat International. DOI: http://dx.doi.org/10.1016/j.habitatint.2012.06.002, 38: 157-166.
Ye, X.; Pendyala, R.M.; Gottardi, G. 2007. An exploration of the relationship between mode choice and complexity of trip chaining patterns, Transportation Research Part B: Methodological. DOI: http://dx.doi.org/10.1016/j.trb.2006.03.004, 41: 96-112.
Zadeh, L. 1965. Fuzzy sets, Information and Control, 8: 338-353.
Zenina, N.; Borisov, A. 2011. Transportation Mode Choice Analysis Based on Classification Methods, IT and Management Science, 49: 49-53.
Zhao, F.; Li, M-T.; Chow, L-F.; Gan, A.; Shen, L.D. 2002. FSUTMS Mode Choice Modeling: Factors Affecting Transit Use and Access, National Center for Transit Research, University of South Florida, Tampa, Florida.
Quoted IJTTE Works
There is no quoted studies.
Related Keywords