Measuring impacts of condition variables in rule-based models of space-time choice behavior: Method and empirical illustration

Geographical Analysis

Volumn 35, Issue 1, 2003, Pages 24-45

  Arentze, Theo ^a   Timmermans, Harry ^a  

^a EINDHOVEN UNIVERSITY OF TECHNOLOGY   (Netherlands)

Author keywords

[No Author keywords available]

Indexed keywords

DECISION ANALYSIS; LOCATION DECISION; SPATIOTEMPORAL ANALYSIS; TRANSPORTATION MODE;

EID: 0037271434     PISSN: 00167363     EISSN: None     Source Type: Journal    
DOI: 10.1111/j.1538-4632.2003.tb01099.x     Document Type: Article

References (20)

1. Arentze, T., A. Borgers, F. Hofman, S. Fujii, C. Joh, A. Kikuchi, R. Kitamura, II. Timmermans, and P. Van der Waerden (2001) Rule-based versus Utility-maximizing Models of Activity-Travel Patterns: A Comparison of Empirical Performance. In Travel Behaviour Research: The Leading Edge, by D. Hensher, pp. 569-584. Amsterdam: Pergamon.
2. Arentze, T., F. Hofman, H. van Mourik, and H. Timmermans (2002) The Spatial Transferability of the Albatross Model System: Empirical Evidence from Two Case Studies. The 81th Transportation Research Board Meeting, January 11-16, Washington D.C. Transportation Research Record (forthcoming)
3. Arentze, T.A., and H.J.P. Timmermans (2000) Albatross: A Learning Based Transportation Oriented Simulation System. In European Institute of Retailing and Services Studies, Eindhoven, pp. 152-168.
4. Arentze, T.A. and H.J.P. Timmermans (2002) Albatross 2.0. Eindhoven: European Institute of Retailing and Services Studies.
5. Arentze, T.A., Hofman, F., van Mourik, H., Timmermans, H.J.P., and Wets, G. (2000) Using Decision Tree Induction Systems for Modeling Space-Time Behavior. Geographical Analysis 32, 52-72.
6. Bowman, J.L., and M.E. Ben-Akiva (1999) The Day Activity Schedule Approach to Travel Demand Analysis. Paper presented at the 78th Annual Meeting of the Transportation Research Board, Washington, D.C., January 10-14.
7. Breiman, L., J.H. Friedman, R.A. Olshen, and C.J. Stone (1984). Classification and Regression Trees. Belmont (CA): Wadsworth.
8. Gahegan, M. (2000a) On the application of inductive machine learning tools to geographical analysis. Geographical analysis 32, 113-139.
9. Gahegan, M. (2000b) The case for inductive and visual techniques in the analysis of spatial data. Journal of Geographical Systems 2, 77-83.
10. Kass, G.V. (1980) An Exploratory Technique for Investigating Large Quantities of Categorical Data. Applied Statistics 29, 119-127.
11. Kira, K., and L.A. Rendall (1992) A Practical Approach to Feature Selection. Machine Learning: Proceedings of the Ninth International Conference.
12. Kononenko, I. (1994) Estimating attributes: Analysis and extensions of relief. Proceedings of the European Conference on Machine Learning.
13. Moons, E., G. Wets, K. Verhoef, T. Arentze, and H. Timmermans (2002) The Impact of Irrelevant Attributes on the Performance of Classifier Systems in Generating Activity Schedules. Paper presented at the 81th Transportation Research Board Meeting, Washington D.C., January 13-17.
14. Piramuthu, S. (1996) Effects of feature selection with 'blurring' on neurofuzzy systems. In Proceedings of the 7th International Workshop on Algorithmic Learning Theory, pp. 135-142.
15. Quinlan, J. R. (1993) C4.5 Programs for Machine Learning. San Mateo (CA): Morgan Kaufmann Publishers.
16. Steinberg, D., and P. Colla (1997) CART: Classification and regression trees. San Diego (CA): Salford Systems.
17. Thill, J.C., and A. Wheeler (1999) Tree Induction of Spatial Choice Behavior. Paper presented at the RSAI meetings in Dublin, Ireland, August 1999.
18. Timmermans, H.J.P., T.A. Arentze, and C.H. Joh (2002) Analysing space-time behaviour: New approaches to old problems. Progress in Human Geography 26, 2, 175-190.
19. Wets, G., Vanhoof, K., Arentze, T.A., and Timmermans, H.J.P. (2000) Identifying Decision Structures Underlying Activity Patterns: An Exploration of Data Mining Algorithms. Transportation Research Record 1718, 1-9.
20. Yamamoto, T., R. Kitamura, and J. Fujii (2002) An Analysis of Driver's Route Choice Behavior by Data Mining Algorithms. Paper presented at the 81th Transportation Research Board Conference, Washington D.C., January 13-17.