Improving performance of multiagent rule-based model for activity pattern decisions with bayesian networks

Transportation Research Record

Volumn , Issue 1894, 2004, Pages 75-83

  Janssens, Davy ^a   Wets, Geert ^a   Brijs, Tom ^a   Vanhoof, Koen ^a   Arentze, Theo ^b   Timmermans, Harry ^b  

^a HASSELT UNIVERSITY   (Belgium)

^b EINDHOVEN UNIVERSITY OF TECHNOLOGY   (Netherlands)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL COMPLEXITY; COMPUTER SIMULATION; MATHEMATICAL MODELS; PERSONNEL; PUBLIC POLICY; SOCIETIES AND INSTITUTIONS; URBAN PLANNING;

BAYESIAN NETWORKS; DECISION RULES; MULTIAGENT RULE-BASED MODEL; PATTERN DECISIONS;

MOTOR TRANSPORTATION;

EID: 19944370142     PISSN: 03611981     EISSN: None     Source Type: Journal    
DOI: 10.3141/1894-09     Document Type: Conference Paper

References (15)

1. Bowman, J. L., M. Bradley, Y. Shiftan, T. K. Lawton, and M. E. Ben-Akiva. Demonstration of an Activity-Based Model System for Portland. Presented at 8th World Conference on Transport Research, Antwerp, Belgium, 1998.
2. Fujii, S., R. Kitamura, and T. Monma. Microsimulation Model System of Individuals' Activity-Travel Patterns Based on Random Utility Theory. Presented at 77th Annual Meeting of the Transportation Research Board, Washington, D.C., 1998.
3. Arentze, T. A., and H. J. P. Timmermans. Albatross: A Learning-Based Transportation Oriented Simulation System. European Institute of Retailing and Services Studies, Eindhoven, Netherlands, 2000.
4. Wets, G., K. Vanhoof, T. Arentze, and H. Timmermans. Identifying Decision Structures Underlying Activity Patterns: An Exploration of Data Mining Algorithms. In Transportation Research Record: Journal of the Transportation Research Board, No. 1718, TRB, National Research Council, Washington, D.C., 2000, pp. 1-9.
5. Moons, E., G. Wets, M. Aerts, T. A. Arentze, and H. Timmermans. The Impact of Irrelevant Attributes on the Accuracy of Classifier Systems in Generating Activity Schedules. Submitted to Environment and Planning A, June 2004.
6. Ben-Akiva, M., J. Bowman, S. Ramming, and J. Walker. Behavioral Realism in Urban Transportation Planning Models. Presented at Transportation Models in the Policy-Making Process: A Symposium in Memory of Greig Harvey, Asilomar Conference Center, Pacific Grove, Calif., 1998.
7. Janssens, D., G. Wets, T. Brijs, K. Vanhoof, and H. Timmermans. Identifying Behavioral Principles Underlying Activity Patterns by Means of Bayesian Networks. Presented at 82nd Annual Meeting of the Transportation Research Board, Washington, D.C., 2003.
8. Kass, G. V. An Exploratory Technique for Investigating Large Quantities of Categorical Data. Applied Statistics, Vol. 29, 1980, pp. 119-127.
9. Pearl, J. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann Publishers, Palo Alto, Calif., 1988.
10. Cheng, J., D. Bell, and W. Liu. Learning Bayesian Networks from Data: An Efficient Approach Based on Information Theory. Proc., Sixth ACM International Conference on Information and Knowledge Management, 1997.
11. Lauritzen, S. L. The EM Algorithm for Graphical Association Models with Missing Data. Computational Statistics and Data Analysis, Vol. 19, 1995, pp. 191-201.
12. Joh, C.-H., T. A. Arentze, and H. J. P. Timmermans. A Position-Sensitive Sequence Alignment Method Illustrated for Space-Time Activity Diary Data. Environment and Planning, Vol. 33A, 2001, pp. 313-338.
13. Wilson, C. Activity Pattern Analysis by Means of Sequence-Alignment Methods. Environment and Planning, Vol. 30A, 1998, pp. 1017-1038.
14. Joh, C.-H., T. A. Arentze, F. Hofman, and H. J. P. Timmermans. Activity Pattern Similarity: A Multidimensional Sequence Alignment Method. Transportation Research, Vol. 36B, 2001, pp. 385-403.
15. Bloemer, J. M. M., T. Brijs, G. Swinnen, and K. Vanhoof. Comparing Complete and Partial Classification for Identifying Customers at Risk. International Journal of Research in Marketing, Vol. 20, No. 2, 2003, pp. 117-131.