Measuring Impacts of Condition Variables in Rule-Based Models of Space-Time Choice Behavior: Method and Empirical Illustration

The use of rule-based systems for modeling space-time choice has gained increasing research interests over the last years. The potential advantage of the rule-based approach is that it can handle interactions between a large set of predictors. Decision tree induction methods are available and have been explored for deriving rules from data. However, the complexity of the structures that are generated by such knowledge discovery methods hampers an interpretation of the rule-set in behavioral terms with as a consequence that the models typically remain a black box. To solve this problem, this paper develops a method for measuring the size and direction of the impact of condition variables on the choice variable as predicted by the model. The paper illustrates the method based on location and transport-mode choice models that are part of Albatross model—an activity-based model of space-time choice.

[1]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[2]  Richard A. Olshen,et al.  CART: Classification and Regression Trees , 1984 .

[3]  Ta Theo Arentze,et al.  Analysing space-time behaviour: new approaches to old problems , 2002 .

[4]  Satoshi Fujii,et al.  Rule-based versus utility-maximizing models of activity-travel patterns , 2000 .

[5]  Hjp Harry Timmermans,et al.  A learning-based transportation oriented simulation system , 2004 .

[6]  G. V. Kass.  An Exploratory Technique for Investigating Large Quantities of Categorical Data , 1980 .

[7]  Toshiyuki Yamamoto,et al.  Drivers’ Route Choice Behavior: Analysis by Data Mining Algorithms , 2002 .

[8]  Geert Wets,et al.  Identifying Decision Structures Underlying Activity Patterns: An Exploration of Data Mining Algorithms , 2000 .

[9]  John L. Bowman,et al.  The Day Activity Schedule Approach to Travel Demand Analysis , 1998 .

[10]  Leo Breiman,et al.  Classification and Regression Trees , 1984 .

[11]  Igor Kononenko,et al.  Estimating Attributes: Analysis and Extensions of RELIEF , 1994, ECML.

[12]  Larry A. Rendell,et al.  A Practical Approach to Feature Selection , 1992, ML.

[13]  David A. Hensher,et al.  Travel behaviour research : the leading edge , 2001 .

[14]  Harry Timmermans,et al.  Spatial Transferability of the Albatross Model System: Empirical Evidence from Two Case Studies , 2002 .

[15]  Selwyn Piramuthu,et al.  Effects of Feature Selection with 'Blurring' on NeuroFuzzy Systems , 1996, ALT.

[16]  Mark Gahegan,et al.  The case for inductive and visual techniques in the analysis of spatial data , 2000, J. Geogr. Syst..

[17]  Jean-Claude Thill,et al.  Tree Induction of Spatial Choice Behavior , 1999 .