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Using Decision Tree Induction Systems for Modeling Space‐Time Behavior
Author(s) -
Arentze T. A.,
Hofman F.,
Mourik H.,
Timmermans H.J.P.,
Wets G.
Publication year - 2000
Publication title -
geographical analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.773
H-Index - 65
eISSN - 1538-4632
pISSN - 0016-7363
DOI - 10.1111/j.1538-4632.2000.tb00431.x
Subject(s) - chaid , decision tree , computer science , decision rule , decision tree learning , incremental decision tree , decision support system , machine learning , heuristic , decision tree model , artificial intelligence , operations research , data mining , mathematics
Discrete choice models are commonly used to predict individuals' activity and travel choices either separately or simultaneously in activity‐scheduling models. This paper investigates the possibilities of decision tree induction systems as an alternative approach. The ability of decision trees to represent heuristic decision rules is evaluated and a method of capturing interactions across decisions in a sequential decision model is outlined. Decision tree induction algorithms, such as C4.5, CART, and CHAID, are suited to derive the decision rules from empirical data. A case study to illustrate the approach considers decisions of individuals when they are faced with the choice to combine different out‐of‐home activities into a multipurpose, multistop trip or make a trip for each activity separately. Data from a large‐scale activity diary survey are used to induce the decision rules. Possible directions of future research are identified.