Development and Calibration of Route Choice Utility Models: Neuro-Fuzzy Approach
This article is a reply.
VIEW THE ORIGINAL ARTICLEPublication: Journal of Transportation Engineering
Volume 130, Issue 2
Abstract
The neuro-fuzzy refers to the recent technology that couples the traditional fuzzy logic developments with neural nets training capabilities to compose the fuzzy logic’s knowledge base and fuzzy sets’ parameters optimally. This paper discusses the calibration methodology of a neuro-fuzzy logic for modeling the route choice behavior. The logic accounts for the various factors of potential effect on the route choice utility perceived by the traveler. The structure of the fuzzy control stages, the calibration of the membership functions, and the composition of the knowledge base are discussed in detail. Logic training is based on data extracted from a factorial experimental design model. The results of the fuzzy logic model are utilized for in-depth analyses of the travelers’ perceptions of the route utility in response to the various traffic states.
Get full access to this article
View all available purchase options and get full access to this article.
References
Abdel-Aty, M. A., Kitamura, R., and Jovanis, P. P. (1995a). “Exploring route choice behavior using geographic information system-based alternative routes and hypothetical travel time information input.” Transportation Research Record 1493, Transportation Research Board, Washington, D.C., 74–80.
Abdel-Aty, M. A., Kitamura, R., and Jovanis, P. P.(1997). “Using stated preference data for studying the effect of advanced traffic information on drivers’ route choice.” Transp. Res., Part C: Emerg. Technol., 5(1), 39–50.
Abdel-Aty, M. A., Kitamura, R., Jovanis, P. P., Reddy, P., and Vaughn, K. M. (1995b). “New approach to route choice data collection: Multiphase, computer-aided telephone interview panel surveys using geographic information systems data base.” Transportation Research Record 1493, Transportation Research Board, Washington, D.C., 159–169.
Abdel-Aty, M. A., Vaughn, K. M., Kitamura, R., Jovanis, P. P., and Mannering, F. L. (1994). “Models of commuters’ information use and route choice: Initial results based on southern California commuter route choice survey.” Transportation Research Record 1453, Transportation Research Board, Washington, D.C., 46–55.
Adler, J. L.(1993). “A conflict model and interactive simulator (FASTCARS) for predicting en-route driver behavior in response to real-time traffic condition information.” Transportation, 20(2), 83–106.
Bonsall, P. W.(1992). “The influence of route guidance advance on route choice in urban networks.” Transportation, 19(1), 1–23.
Bonsall, P., Firmin, P., Anderson, M., Palmer, I., and Balmforth, P.(1997). “Validating the results of route choice simulator.” Transp. Res., Part C: Emerg. Technol., 5(6), 371–387.
Bonsall, P. W., and Parry, T. (1991). “Using an interactive route-choice simulator to investigate drivers’ compliance with route guidance advice.” Transportation Research Record 1306, Transportation Research Board, Washington, D.C., 59–68.
Chen, P. S., and Mahmassani, H. S. (1993). “Dynamic interactive simulator for studying commuter behavior under real-time traffic information supply strategies.” Transportation Research Record 1413, Transportation Research Board, Washington, D.C., 12–21.
Emmerink, R. H. M., Nijkamp, P., Rietveld, P., and Van Ommeren, J. N.(1996). “Variable message signs and radio traffic information: An integrated empirical analysis of drivers’ route choice behavior.” Transp. Res., Part A: Policy Pract., 30(2), 135–153.
Hawas, Y. E.(2002). “Calibrating simulation models for advanced traveler information systems/advanced traffic management systems applications.” J. Transp. Eng., 128(1), 80–88.
Hawas, Y. E.(2004). “Development and calibration of route choice utility models: Factorial experimental design approach.” J. Transp. Eng., 130(2), 159–170.
Henn, V.(2000). “Fuzzy route choice model for traffic assignment.” Fuzzy Sets Syst., 116(1), 77–101.
Hoogendoorn, S., Lanser, S. H., and Schuurman, H. (1999). “Perspectives of fuzzy logic in traffic engineering.” CD-ROM Proc., Transportation Research Board 78th Annual Meeting, Washington, D.C.
INFORM. (2001). FuzzyTECH 5.5: User’s manual. GmbH/Inform Software Corporation, Germany.
Katsikopoulos, K. V., Duse-Anthony, Y., Fisher, D. L., and Duffy, S. A.(2000). “The framing of drivers’ route choices when travel time information is provided under varying degrees of cognitive load.” Hum. Factors, 42(3), 470–481.
Kikuchi, S., and Pursula, M.(1998). “Treatment of uncertainty in study of transportation: Fuzzy set theory and evidence theory.” J. Transp. Eng., 124(1), 1–8.
Koppleman, F. S., and Wen, C.-H.(2000). “The paired combinatorial logit model: Properties, estimation and application.” Transp. Res., Part B: Methodol., 34(2), 75–89.
Koutsopoulos, H. N., Lotan, T., and Yang, Q.(1994). “A driving simulator and its application for modeling route choice in the presence of information.” Transp. Res., Part C: Emerg. Technol., 2(2), 91–107.
Koutsopoulos, H. N., Polydoropoulou, A., and Ben-Akiva, M.(1995). “Travel simulators for data collection on driver behavior in the presence of information.” Transp. Res., Part C: Emerg. Technol., 3(3), 143–159.
Kuri, J., and Pursula, M. (2001). “Modeling car route choice with non-dynamic stated preference data: A comparison between fuzzy similarity and random utility models.” CD-ROM Proc., 80th Annual Meeting of the Transportation Research Board, Washington, D.C.
Lanser, S. H., and Hoogendoorn, S. (2000). “A fuzzy genetic approach to travel choice behavior in public transport networks.” CD-ROM Proc., Transportation Research Board 79th Annual Meeting, Washington, D.C.
Lotan, T.(1997). “Effects of familiarity on route choice behavior in the presence of information.” Transp. Res., Part C: Emerg. Technol., 5(3), 225–243.
Lotan, T.(1998). “Modeling discrete choice behavior based on explicit information.” IEEE Trans. Syst. Man Cybern., 28(1), 100–114.
Mahmassani, H. S., and Herman, R. (1990). “Interactive experiments for the study of trip-maker behavior dynamics in congested commuting systems.” Developments in dynamic and activity-based approaches to travel analysis, P. Jones, ed., Gower, Aldershot, Hampshire, U.K., 272–298.
Ozbay, K., Datta, A., and Kachroo, P. (2001). “Modeling route choice behavior using stochastic learning automata.” CD-ROM Proc., 80th Annual Meeting of the Transportation Research Board, Washington, D.C.
Peeta, S., Ramos, J. L., and Pasupathy, R. (2000). “Content of variable message signs and on-line driver behavior.” Transportation Research Record 1725, Transportation Research Board, Washington, D.C., 102–108.
Polydoropoulou, A., Ben-Akiva, M., and Kaysi, E. (1994). “Influence of traffic information on drivers’ route choice behavior.” Transportation Research Record 1453, Transportation Research Board, Washington, D.C., 56–65.
Rilett, L. R., and Park, D.(2001). “Incorporating uncertainty and multiple objectives in real-time route selection.” J. Transp. Eng., 127(6), 531–539.
Ross, T. J. (1995). Fuzzy logic with engineering applications. McGraw-Hill, New York.
Sayers, T., and Anderson, J. (1999). “Optimization of fuzzy logic traffic signal controller with multiple objectives.” CD-ROM Proc., Transportation Research Board 78th Annual Meeting, Washington, D.C.
Srinvasan, K. K., and Mahmassani, H. S. (2000). “Modeling inertia and compliance mechanisms in route choice behavior under real-time information.” Transportation Research Record 1725, Transportation Research Board, Washington, D.C., 45–53.
Teodorovic, D., Vukanovic, S., and Obradovic, K.(1998). “Modeling route choice with advanced traveler information by fuzzy logic.” Transp. Plan. Technol., 22(1), 1–25.
Trabia, M. B., Kaseko, M. S., and Ande, M. A.(1999). “Two-stage fuzzy logic controller for traffic signals.” Transp. Res., Part C: Emerg. Technol., 7, 353–367.
Vaughn, K. M., Abdel-Aty, M. A., Kitamura, R., Jovanis, P. P., Yang, H., Kroll, N. E. A., Post, R. B., and Oppy, B. (1993). “Experimental analysis and modeling of sequential route choice under an advanced traveler information system in a simplistic traffic network.” Transportation Research Record 1408, Transportation Research Board, Washington, D.C., 75–82.
Vaughn, K. M., Kitamura, R., and Jovanis, P. P. (1995b). “Experimental analysis and modeling of advice compliance: Results from advanced traveler information system simulation experiments.” Transportation Research Record 1485, Transportation Research Board, Washington, D.C., 18–26.
Vaughn, K. M., Reddy, P., Abdel-Aty, M. A., Kitamura, R., and Jovanis, P. P. (1995a). “Route choice and information use: Initial results from simulation experiments.” Transportation Research Record 1516, Transportation Research Board, Washington, D.C., 61–69.
Information & Authors
Information
Published In
Journal of Transportation Engineering
Volume 130 • Issue 2 • March 2004
Pages: 171 - 182
Copyright
Copyright © 2004 American Society of Civil Engineers.
History
Received: May 16, 2002
Accepted: Mar 6, 2003
Published online: Feb 19, 2004
Published in print: Mar 2004
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.