The scheduling of daily activities requires decisions to be made on which activities to conduct, where, when, for how long, and the transport mode involved. In a common conceptualization of the decision process, a multitude of time frames is involved. Longer term decisions determine the allocation of time across main activity categories as well as the profiling of institutionalized activities such as work and some leisure activities. The authors introduce a conceptual framework for modeling the formation of activity agendas that represents the results of such longer term decisions. The framework identifies the elementary decisions and arranges these decisions in a hierarchy. In the proposed system, the decisions are handled by a set of relatively autonomous interacting agents.
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References
1.
ArentzeTHofmanFvan MourikHTimmermansH J P, 2000a, “ALBATROSS: a multi-agent rule-based model of activity pattern decisions”, paper presented at the 79th Annual Transportation Research Board Meeting, Washington, DC, 9–13 January; copy available from the authors
2.
ArentzeTHofmanFvan MourikHTimmermansHWetsG, 2000b, “Using decision tree induction systems for modeling space–time behavior”Geographical Analysis32330–350
3.
ArentzeTDijstMDugundjiFJohC-HKapoenLKrijgsmanSMaatCTimmermansHVeldhuisenJ, 2001, “The AMADEUS Program: scope and conceptual development”, paper presented at the 9th World Conference on Transport Research, 22–27 July, Seoul; copy available from the author
4.
BeckerG S, 1965, “A theory of the allocation of time”The Economic Journal75493–517
5.
BhatC R, 1999, “A comprehensive and operational analysis framework for generating the daily activity travel profiles of workers”, paper presented at the 78th Annual Meeting of the Transportation Research Board, Washington, DC, 10–14 January; copy available on a conference CDrom from the TRB
6.
BhatCKoppelmanF S, 2000, “Activity-based travel demand analysis: history, results and future directions”, paper presented at the 79th Annual Transportation Research Board Meeting, Washington, DC, 9–13 January; copy available on a conference CDrom from the TRB
7.
BorgersATimmermansHvan der WaerdenP, 2002, “Patricia: predicting activity – travel interdependencies using a suite of choice-based interlinked analyses”, paper presented at the 81st Annual Transport Research Board Meeting, Washington, DC, 13–17 January; copy available from Professor Timmermans
8.
BowmanJ LBen-AkivaM E, 1999, “The day activity schedule approach to travel demand analysis”, presented at the 78th Annual Meeting of the Transportation Research Board, Washington, DC, 10–14 January; copy available on a conference CDrom from the TRB
9.
FosgerauM, 1998, “PETRA: an activity based approach to travel demand analysis”, paper presented at the 8th World Conference on Transport Research, Antwerp; copy available on a conference CDrom from the TRB
10.
FrustiTBhatC R, 2002, “Exploratory analysis of fixed commitments in individual activity–travel analysis”, paper presented at the 81st Annual Transportation Research Board Meeting, Washington, DC, 13 – 17 January; copy available on a conference CDrom from the TRB
11.
FujiiSKitamuraRMonmaT, 1998, “A utility-based micro-simulation model system of individual's activity–travel patterns”, paper presented at the 77th Annual Meeting of Transportation Research Board, Washington, DC; copy available on a conference CDrom from the TRB
12.
GolobT F, 1998, “A model of household choice of activity participation and mobility”, in Theoretical Foundations of Travel Choice Modeling Eds GärlingTLaitilaTWestinK (Elsevier, Oxford) pp 365–397
13.
JohC HArentzeT ATimmermansH J P, 2002, “Modeling individuals' activity–travel rescheduling heuristics: theory and numerical experiments”, paper presented at the 81st Annual Transportation Research Board Meeting, Washington, DC, 13–17 January; copy available from the authors
14.
KitamuraR, 1984, “A model of daily time allocation to discretionary out-of-home activities and trips”Transportation Research B18255–266
15.
KitamuraRFujiiS, 1998, “Two computational process models of activity – travel choice”, in Theoretical Foundations of Travel Choice Modeling Eds GärlingTLaitilaTWestinK (Elsevier, Oxford) pp 251–279
16.
KockelmanK M, 1999, “Application of a utility-theory-consistent system-of-demand equations approach to household activity travel choice”, paper presented at the 78th Annual Meeting of the Transportation Research Board, Washington, DC, 10–14 January; copy available on a conference CDrom from the TRB
17.
KraanM, 1997, “In search for limits to mobility growth with a model for the allocation of time and money”, in Activity-based Approaches to Travel Analysis Eds EttemaD FTimmermansH J P (Pergamon Press, Oxford) pp 89–116
18.
LuXPasE I, 1997, “A structural equations model of the relationships among socio-demographics, activity participation and travel behavior”, paper presented at the 76th Annual Meeting of the Transportation Research Board, Washington, DC; copy available on a conference CDrom from the TRB
19.
LuXPasE I, 1999, “Socio-demographics, activity participation and travel behavior”Transportation Research A331–18
20.
MaJGouliasK G, 1997, “A dynamic analysis of person and household activity and travel patterns using data from the first two waves in the Puget Sound Transportation panel”Transportation24309–331
21.
MaJGouliasK G, 1999, “Application of Poisson regression models to activity frequency analysis and prediction”, paper presented at the 78th Annual Meeting of the Transportation Research Board, Washington, DC, 10–14 January; copy available on a conference CDrom from the TRB
22.
SupernakJ, 1992, “Temporal utility profiles of activities and travel: uncertainty and decision making”Transportation Research B2661–76
23.
TimmermansHArentzeTDijstMDugundjiEJohC-HKapoenLKrijgsmanSMaatCVeldhuisenJ, 2002, “Amadeus: a framework for developing a dynamic multi-agent, multi-period, activity-based micro-simulation model of travel demand”, paper presented at the 81st Annual Transportation Research Board Meeting, Washington, DC, 13–17 January; copy available 13–17 January
24.
WetsGVanhoofKArentzeTTimmermansH, 2000, “Identifying decision structures underlying activity patterns: an exploration of data mining algorithms”Transportation Research Recordnumber 1718, 1–9
25.
WinstonG C, 1982, “Time-specific analysis of household activities”, in The Timing of Economic Activities: Firms, Households, and Markets in Time-specific Analysis Ed. WinstonG C (Cambridge University Press, Cambridge) pp 155–223
