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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/4004

Title: Allocating time and location information to activity-travel patterns through reinforcement learning
Authors: JANSSENS, Davy
Lan, Yu
WETS, Geert
Chen, Guoqing
Issue Date: 2007
Citation: KNOWLEDGE-BASED SYSTEMS, 20(5). p. 466-477
Abstract: The Reinforcement Machine Learning technique presented in this paper simulates time and location information for a given sequence of activities and transport modes. The main contributions to the current state-of-the art are the allocation of location information in the simulation of activity-travel patterns, the non-restriction to a given number of activities and the incorporation of realistic travel times. Furthermore, the time and location allocation problem were treated and integrated simultaneously, which means that the respondents' reward is not only maximized in terms of minimum travel duration, but also simultaneously in terms of optimal time allocation. (C) 2007 Elsevier B.V. All rights reserved.
Notes: Hasselt Univ, Transportat Res Inst, B-3590 Diepenbeek, Belgium. Tsing Hua Univ, Sch Econ & Management, Beijing 100084, Peoples R China.WETS, G, Hasselt Univ, Transportat Res Inst, Wetenschapspk 5 Bus 6, B-3590 Diepenbeek, Belgium.geert.wets@uhasselt.be
URI: http://hdl.handle.net/1942/4004
DOI: 10.1016/j.knosys.2007.01.008
ISI #: 000247762200004
ISSN: 0950-7051
Category: A1
Type: Journal Contribution
Validation: ecoom, 2008
Appears in Collections: Research publications

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