A dynamic model of time-budget and activity generation: Development and empirical derivation

Transportation Research Part C: Emerging Technologies

Volumn 19, Issue 2, 2011, Pages 242-253

  Arentze, Theo A ^a   Timmermans, Harry J P ^a  

^a EINDHOVEN UNIVERSITY OF TECHNOLOGY   (Netherlands)

Author keywords

Activity generation; Dynamic models; Micro simulation; Travel demand modeling

Indexed keywords

BUDGET CONTROL; SURVEYS;

ACTIVITY PATTERNS; ACTIVITY-BASED MODELS; BUDGET CONSTRAINT; ESTIMATE DYNAMICS; MICROSIMULATION; NATIONAL TRAVEL SURVEYS; STATISTICAL CHARACTERISTICS; TRAVEL DEMAND MODELING;

DYNAMIC MODELS;

EMPIRICAL ANALYSIS; ESTIMATION METHOD; SIMULATION; SPATIAL ANALYSIS; STATISTICAL ANALYSIS; TRAVEL BEHAVIOR; TRAVEL DEMAND;

EID: 78951490641     PISSN: 0968090X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.trc.2010.05.011     Document Type: Article

References (16)

1. Arentze T., Timmermans H. A need-based model of multi-day, multi-person activity generation. Transportation Research Part B 2009, 43:251-265.
2. Arentze, T.A., Ettema, D., Timmermans, H.J.P., 2010a. Estimating a model of dynamic activity generation based on 1-day observations: method and results. Transportation Research Part B, in press.
3. Arentze T.A., Ettema D., Timmermans H.J.P. Incorporating time and income constraints in dynamic agent-based models of activity generation and time use: approach and illustration. Transportation Research Part C 2010, 18:71-83.
4. Arentze T.A., Timmermans H.J.P. A learning-based transportation oriented simulation system. Transportation Research Part B 2004, 38:613-633.
5. Arentze T.A., Timmermans H.J.P., Jorritsma P., Olde Kalter M.-J. More gray hair - but for whom? Scenario-based simulations of elderly activity travel patterns in 2020. Transportation 2008, 35:613-627.
6. Axhausen K.W., Zimmerman A., Schönfelder S., Rindsfüser G., Haupt T. Observing the rhythms of daily life: a six-week travel diary. Transportation 2002, 29:95-124.
7. Bayarma, A., Kitamura, R., Susilo, Y.O., 2007. On the recurrence of daily travel patterns: a stochastic-process approach to multi-day travel behavior. In: Proceedings of the 86th Annual Meeting of the Transportation Research Board, Washington, DC (CD-ROM).
8. Bhat C.R., Guo J.Y., Srinivasan S., Sivakumar A. Comprehensive econometric microsimulator for daily activity-travel patterns. Transportation Research Record 2004, 1894:57-66.
9. Bhat C.R. A multiple discrete-continuous extreme value model: formulation and application to discretionary time-use decisions. Transportation Research Part B 2005, 39:679-707.
10. Bhat C.R., Srinivasan S., Axhausen K.W. An analysis of multiple interepisode durations using a multivariate hazard model. Transportation Research Part B 2005, 39:797-823.
11. Habib, K.M.N., Miller, E.J., 2007. Modeling daily activity program generation considering within-day and day-to-day dynamics in activity-travel behaviour. In: Proceedings of the 86th Annual Meeting of the Transportation Research Board, Washington, DC (CD-ROM).
12. Pendyala R.M., Kitamura R., Kikuchi A., Yamamoto T., Fujii S. Florida activity mobility simulator: overview and preliminary validation results. Transportation Research Record 2005, 1921:123-130.
13. Roorda, M.J., Miller, E.J., Habib, N.K.M., 2007. Validation of TASHA: a 24-hour activity scheduling microsimulation model. In: Proceedings of the 86th Annual Meeting of the Transportation Research Board, Washington, DC (CD-ROM).
14. Schlich R., Axhausen K.W. Habitual travel behaviour: evidence from a six-week travel diary. Transportation 2003, 30:13-26.
15. Visser I., Raijmakers M.E.J., Molenaar P.C.M. Fitting hidden Markov models to psychological data. Scientific Programming 2002, 10:185-199.
16. Vovsha P., Bradley M., Bowman J. Activity-based travel forecasting models in the United States: progress since 1995 and prospects for the future. Progress in Activity-Based Analysis 2004, 389-414. Elsevier. H. Timmermans (Ed.).