Q-learning for flexible learning of daily activity plans

Transportation Research Record

Volumn , Issue 1935, 2005, Pages 163-169

  Charypar, David ^a   Nagel, Kai ^a,b  

^a ETH ZURICH   (Switzerland)

^b TECHNISCHE UNIVERSITÄT BERLIN   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL COMPLEXITY; COMPUTER SIMULATION; LEARNING ALGORITHMS; LEARNING SYSTEMS; MOTOR TRANSPORTATION; PROBLEM SOLVING; RANDOM PROCESSES;

ACTIVITY GENERATION; Q-LEARNING; SYNTHETIC POPULATION; TRANSPORTATION SIMULATION;

ARTIFICIAL INTELLIGENCE;

EID: 33646416757     PISSN: 03611981     EISSN: None     Source Type: Journal    
DOI: 10.3141/1935-19     Document Type: Conference Paper

References (13)

1. Axhausen, K., ed. Proc., Meeting of the International Association for Travel Behavior Research (IATBR), Lucerne, Switzerland, 2003. www.ivt.baum.ethz.ch/allgemein/iatbr2003.html.
2. EIRASS Workshop on Progress in Activity-Based Analysis. Maastricht, Netherlands, May 2004.
3. Ben-Akiva, M., and S. R. Lerman. Discrete Choice Analysis. MIT Press, Cambridge, Mass., 1985.
4. Bowman, J. L., M. Bradley, Y. Shiftan, T. K. Lawton, and M. Ben-Akiva. Demonstration of an Activity-Based Model for Portland. In World Transport Research: Selected Proceedings of the 8th World Conference on Transport Research 1998, Volume 3, Elsevier, Oxford, 1999, pp. 171-184.
5. Pendyala, R. M. Phased Implementation of a Multimodal Activity-Based Travel Demand Modeling System in Florida. Project No. 0510812 (BA496 ). www.dot.state.fl.us/research-center/Completed_PTO.htm. Accessed 2004.
6. Bhat, C. R., J. Y. Guo, S. Srinivasan, and A. Sivakumar. A Comprehensive Econometric Microsimulator for Daily Activity-Travel Patterns. In Transportation Research Record: Journal of the Transportation Research Board, No. 1894, Transportation Research Board of the National Academies, Washington, D.C., 2004, pp. 57-66.
7. Arentze, T., F. Hofman, H. van Mourik, and H. Timmermans. ALBATROSS: A Multiagent Rule-Based Model of Activity Pattern Decisions. In Transportation Research Record: Journal of the Transportation Research Board, No. 1706, TRB, National Research Council, Washington, D.C., 2000, pp. 136-144.
8. Miller, E. J., and M. J. Roorda. Prototype Model of Household Activity-Travel Scheduling. In Transportation Research Record: Journal of the Transportation Research Board, No. 1831, Transportation Research Board of the National Academies, Washington, D.C., 2003, pp. 114-121.
9. Russel, S. J., and P. Norvig. Artificial Intelligence: A Modern Approach. Series in Artificial Intelligence. Prentice Hall, Englewood Cliffs, N.J.,1995, pp. 598-624.
10. Watkins, C. J. C. H., and P. Dayan. Q-learning. Machine Learning, Vol. 8, 1992, pp. 279-292.
11. Graf, P. Artificial Intelligence Approaches to the Learning of Humans. Diploma thesis. ETH Zurich, Switzerland, 2003. e-collection.ethbib. ethz.ch/ecol-pool/dipl/dip1_132.pdf. Accessed Oct. 5, 2004.
12. Karlstrom, A. A Dynamic Programming Approach for the Activity Generation and Scheduling Problem. Working Paper Transport and Location Analysis. Royal Institute of Technology, Sweden, 2004.
13. Barrett, L., R. Jacob, and M. V. Marathe. Formal-Language-Constrained Path Problems. SIAM J COMPUT, Vol. 30, No. 3, 2000, pp. 809-837.