Deriving rules from activity diary data: A learning algorithm and results of computer experiments

Theo A. Arentze, Frank Hofman and Harry J.P. Timmermans
Additional contact information
Theo A. Arentze: Urban Planning Group EIRASS, Eindhoven University of Technology, P.O.Box 513, 5600 MB, Eindhoven, The Netherlands (e-mail: eirass@bwk.tue.nl)
Frank Hofman: Ministry of Transport, Public Works and Water Management, P.O.Box 1031, 3000 BA, Rotterdam, The Netherlands
Harry J.P. Timmermans: Urban Planning Group EIRASS, Eindhoven University of Technology, P.O.Box 513, 5600 MB, Eindhoven, The Netherlands (e-mail: eirass@bwk.tue.nl)

Journal of Geographical Systems, 2001, vol. 3, issue 4, 325-346

Abstract: Abstract. Activity-based models consider travel as a derived demand from the activities households need to conduct in space and time. Over the last 15 years, computational or rule-based models of activity scheduling have gained increasing interest in time-geography and transportation research. This paper argues that a lack of techniques for deriving rules from empirical data hinders the further development of rule-based systems in this area. To overcome this problem, this paper develops and tests an algorithm for inductively deriving rules from activity-diary data. The decision table formalism is used to exhaustively represent the theoretically possible decision rules that individuals may use in sequencing a given set of activities. Actual activity patterns of individuals are supplied to the system as examples. In an incremental learning process, the system progressively improves on the selection of rules used for reproducing the examples. Computer experiments based on simulated data are performed to fine-tune rule selection and rule value update functions. The results suggest that the system is effective and fairly robust for parameter settings. It is concluded, therefore, that the proposed approach opens up possibilities to derive empirically tested rule-based models of activity scheduling. Follow-up research will be concerned with testing the system on empirical data.

Keywords: Key words: Activity-based models; activity scheduling; rule-based systems; decision tables; inductive machine learning; adaptive systems (search for similar items in EconPapers)
Date: 2001
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s101090100069

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