Modelling activity generation: A utility-based model for activity-agenda formation

Transportmetrica

Volumn 5, Issue 1, 2009, Pages 3-23

  Nurul Habib, Khandker M ^a   Miller, Eric J ^b  

^a UNIVERSITY OF ALBERTA   (Canada)

^b UNIVERSITY OF TORONTO   (Canada)

Author keywords

Activity episode; Activity generation; Activity utility; Activity agenda; Demand system

Indexed keywords

ACTIVITY EPISODES; ACTIVITY GENERATION; ACTIVITY UTILITY; ACTIVITY-AGENDA; DEMAND SYSTEM;

BUDGET CONTROL; SPECIFICATIONS;

TRANSPORTATION;

EID: 77549083611     PISSN: 18128602     EISSN: None     Source Type: Journal    
DOI: 10.1080/18128600802591384     Document Type: Article

References (46)

1. Arentze T. and Timmerman, H.J.P., 2005. ALBATROSS Version 2.0 A Learning Based Transportation Oriented Simulated System, EIRSS, 2005.
2. Axhausen, K., 1998. Can we ever obtain the data we would like to have? In: T Garling, T. Laitila and K. Westin, eds. Theoretical Foundations of Travel Choice Modelling. Oxford: Pergamon Press.
3. Badoe, D. and Miller, E.J., 1998. An automatic segmentation procedure for studying variations in mode choice behaviour. Journal of Advanced Transportation, 32 (2), 190-215.
4. Bhat, C.R., 2003. Simulation estimation of mixed discrete choice models using randomized and scrambled Halton sequences. Transportation Research Part B, 37 (9), 837-855.
5. Bhat, C.R., Guo, J.Y., Srinivasan, S. and Sivakumar, S., 2004. A comprehensive econometric micro-simulator for daily activity travel patterns. Paper presented at the 83rd Annual Meeting of Transportation Research Board, Washington, DC.
6. Bhat, C.R., 2005. A multiple discrete-continuous extreme value model: formulation and application to discretionary time-use decisions. Transportation Research Part B, 39, 679-707.
7. Bhat, C.R., 2006. The multiple discrete-continuous extreme value model: Role of utility function parameters, Identification considerations and Model extension. Paper presented at the 11th International Conference on Travel Behaviour Research, Kyoto, Japan.
8. Bhat, C.R. and Sen, S., 2006. A joint model for the perfect and imperfect substitute goods case: Application to activity time-use decision. Paper presented at the 85th Annual Meeting of the Transportation Research Board, Washington, DC.
9. Carrasco, J.A., 2006. Social Network and Travel Behaviour. PhD Thesis, Department of Civil Engineering, University of Toronto.
10. Carrasco, J.A. and Miller, E.J., 2006. Spatial and social network: the case of travel for social activities. Paper presented at the 11th International Conference on Travel Behaviour Research, Tokyo, Japan, August 16-20.
11. Chen, C. and Mokhtarian, P.L., 2006. Trade-offs between time allocation to maintenence activities/ travel and discretionary activities/travel. Transportmetric, 33, 223-240.
12. Duann, L.S. and Shiaw, M., 2001. Value of travel time: An activity-based analysis with combined RP and SP data. Journal of Advanced Transportation, 35 (1), 15-31.
13. Doherty, S.T. and Miller, E.J., 2000. A computerized household activity scheduling survey. Transportation, 21 (1), 75-97.
14. Doherty, S.T., et al, 2004. Design and assessment of the Toronto Area computerized household activity scheduling survey. Transportation Research Record 1894, 140-149.
15. Ettema, D., 2005. Latent activities: modelling the relationship between travel times and activity participation. Transportation Research Record 1926, 171-181.
16. Garling, T., et al., 1989. Household activity scheduling. In: Transportation Policy, Management and Technology Towards 2001: Selected Proceedings of Fifth World Conference of Transport and Research IV, Ventura, CA: Western Periodical, 235-248.
17. Garling, T. and Garvill, J., 1993. Psychological explanation of participation in everyday activities. In: T. Garling and R.G. Golledge, eds. Behaviour and Environment: Psychological and Geographical Approach. Amsterdam, New York: Elsevier Science Publisher.
18. Galing, T., Kwan, M.-P., and Golledge, R.G., 1994. Computation-process modelling of household activity scheduling. Transportation Research Part B, 28 (5), 355-364.
19. Gourieroux, C. and Monfort, A., 1996. Simulation-Based Econometric Methods. New York: Oxford university Press.
20. Habib, K.M.N. and Miller, E.J., 2005. Modelling duration of work/school episodes using activity diary data for the specification of activity-travel scheduler. In: Proceedings of PROCESSUS Second International Colloquium on the Behavioural Foundation of Land-Use Transportation Models: Frameworks, Models and Applications, 12-15 June, Toronto.
21. Habib, K.M.N. and Miller, E.J., 2006a. Modelling skeletal components of workers' daily activity schedules. Transportation Research Record, 1985, 88-97.
22. Habib, K.M.N. and Miller, E.J., 2006b. Modelling individuals' frequency and time allocation behaviour for shopping activities considering household level random effects. Transportation Research Record, 1985, 78-87.
23. Habib, K.M.N., 2007. Modelling Activity Generation Processes. PhD Thesis, Department of Civil Engineering, University of Toronto.
24. Hicks, J.R., 1946. Value and Capital. 2nd ed. London: Oxford University Press.
25. Joh, C.H., Timmermans, H.J.P, and Arentze, T., 2006. Measuring and predicting adaptation behaviour in multidimensional activity-travel patterns. Transportmetrica, 2 (2), 153-173.
26. Kim, J., Albany, G.M., and Rossi, P.E., 2002. Modelling consumer demand for variety. Marketing Science, 21, 229-250.
27. Kitamura, R., Fujii, S. and Yamamato, T., 1998. An application of microsimulator of daily travel and dynamic network flow to evaluate the effectiveness of TDM measures for CO2 emission reduction. Paper presented at the 77th Annual Meeting of the Transportation Research Board, Washington, DC.
28. Kitamura, R. and Susilo, Y, 2005. Is travel demand insatiable? A study of changes in structural relationships underlying travel. Transportmetrica, 1 (1), 23-45.
29. Kraan, M., 1997. In search of limits to mobility growth with a model for the allocation of time and money. In: D. Ettema and H.J.P. Timmermanns, eds. Activity-Based Approaches to Travel Analysis. UK, USA, Japan: Pergamon Press.
30. Krantz, S.G. and Parks, H.R., 1951. The Implicit Function Theorem: History, Theory and Applications. Boston-Basel-Berlin: Birkhauser Publication.
31. Kockelman, K., 1998. A utility-theory-consistent system of demand equations approach. PhD Thesis, Department of Civil Engineering, University of California, Berkeley.
32. Kockelman, K., 2001. A model for time- and budget-constraint activity demand analysis. Transportation Research Part B, 35, 255-269.
33. Kuhn, H.W. and Tucker, A.W., 1951. Nonlinear programming. In: J. Neyman, ed. Proceedings of the Second Berkeley Symposium on Mathematical Statistics and Probability. Berkeley, CA: University of California Press, 481-492.
34. Litwin, M., 2005. Dynamic household activity scheduling process. PhD Thesis, Department of Civil Engineering, University of Toronto.
35. Meloni, I., Guala, L., and Loddo, A., 2004. Time allocation to discretionary in-home, out-of-home activities and trips. Transportation, 31, 69-96.
36. Miller, E.J., 2005a. Propositions for modelling household decision-making. In: M. Lee-Gosselin and S. Doherty, eds. Integrated Land Use and Transportation Models: Behavioural Foundations. Elsevier: New York.
37. Miller, E.J., 2005b. An integrated framework of modelling short- and long-run household decision- making. In: H.J.P. Timmermans, ed. Progress in Activity Based Analysis. Elsevier: New York.
38. Miller, E.J., 2005c. Project-based activity scheduling for household and person agents. In: H. Mahmassani, ed. Flow, Dynamics and Human Interactions: Proceedings of the 16th International Symposium on Transportation and Traffic Theory. Elsevier: New York.
39. Miller, E.J. and Roorda, M.J., 2003. Prototype model of household activity/travel scheduling. Transportation Research Record 1831, 114-121.
40. Pendyala, R., et al., 2004. FAMOS: The Florida activity mobility simulator, Paper presented at the 83rd Annual Meeting of Transportation Research Board, Washington, DC.
41. Roorda, M.J. and Miller, E.J., 2004. Toronto area panel survey: demonstrating the benefit of a multiple instrument panel survey. Paper presented at the 7th International Conference on Travel Methods, Costa Rica.
42. Schwanen, T. and Dijst, M., 2002. Travel ratios for visit to work place: the relationship between commuting time and work duration. Transportation Research Part A, 36, 573-592.
43. Train, K., 2002. Discrete Choice Method with Simulation. USA: Cambridge University Press.
44. von Haefen, R.H. and Phaneuf, D.J., 2004. Kuhn-Tucker demand system approach to non-market valuation. In: S. Scarpa and A. Alberini, eds. Applications of Simulation Methods in Environmental and Resource Economics. Boston/Dordrecht/London: Kluwer Academic Publishers.
45. von Haefen, R.H., Phaneuf, D. J., and Parson, G. R., 2004. Estimation and welfare analysis of large scale demand system. Journal of Business & Economic Statistics, 22 (2), 194-285.
46. Wales, T.J. and Woodland, A.D., 1982. Estimation of consumer system of binding non-negativity constraints. Journal of Econometrics, 21, 263-285.