Abstract
In order to increase the patronage of urban transit systems, improvement of customer satisfaction is a key element. As the factors affecting urban transit ridership are several, conflicting, and incommensurable; a multi criteria decision modeling technique is needed for the analysis of these factors. Fuzzy Cognitive Mapping (FCM) is a decision-support tool which gives the researcher opportunity to model complex systems especially in network shape. A FCM can be represented as a signed digraph which consists of factors (nodes) and relations between factors (edges between nodes). value are assigned to relations to represent the strength of impact. The value of factors are updated periodically by formulating the system in an iterative process. This is helpful in giving us the ability to see the system changes caused by the changes in factors.
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Murray A.T. (2001). Strategic analysis of public transport coverage. Socio-Economic Planning Sciences, 35, p. 175–188.
Anderson W., Kanaroglou P., and Miller E. (1996). Urban form, energy and the environment: a review of the issues, evidence and policy. Urban Studies, 33, p. 7–35.
Newman P. and Kenworthy J. (1999). Sustainability and Cities: Overcoming Automobile Dependence, Island Press, Washington, DC.
Stern E. and Tretvik T. (1993). Public transport in Europe: requiem or revival? In: Salomon, I., Bovy, P.H.L., Orfeuil, J.P. (Eds.), A Billion Trips a Day, Kluwer Academic Publishers, The Netherlands, p. 129–148.
Racca D. and Ratledge E. (2004). Project Report for Factors That Affect and/or Can AlterMode Choice, University of Delaware, Newark.
Transit Cooperative Research Program (1996). TCRP Report 16: Transit and Urban Form, Part1: Transit, Urban Form, and the Built Environment: A Summary of Knowledge, Transportation Research Board, National Research Council, Washington D.C.
Parasuraman A., Zeithaml V., and Berry L. (1985). Conceptual Model of Service Quality and Its Implications for Future Research. Journal of Marketing, 49 (4), p. 41–50.
Transit Cooperative Research Program (1999). TCRP Report 47: A Handbook for Measuring Customer Satisfaction and Service Quality, Transportation Research Board, National Research Council, Washington D.C.
Dowling R., Reinke D., Flannery A., Ryus P., Vandehey M., Petritsch T., Landis B., Rouphail N., and Bonneson J. (2008). Multimodal Level of Service Analysis for Urban Streets, Final Report, Transportation Research Board, Washington, DC.
Debrezion G., Pels E., and Rietveld P. (2009). Modelling the joint access mode and railway station choice. Transportation Research Part E, 45, p. 270–283.
Brons M., Givoni M., and Rietveld P. (2009). Access to railway stations and its potential in increasing rail use. Transportation Research Part A, 43, p. 136–149.
Krygsman S., Dijst M., and Arentze T. (2004). Multimodal public transport: an analysis of travel time elements and the interconnectivity ratio. Transport Policy, 11, p. 265–275.
Rietveld P., Bruinsma F., and Vuuren D.J. (2001). Coping with unreliability in public transport chains: A case study for Netherland. Transportation Research Part A, 35, p. 539–559.
Estupinan N. and Rodriguez D. (2008). The relationship between urban form and station boardings for Bogota’s BRT. Transportation Research Part A, 42, p. 296–306.
Parsons, Brinckerhoff and Douglas, (1993). LUTRAQ: The Pedestrian Environment Model Modifications. Vol. 4A.
Arentze T. and Timmermans H. (2003). Measuring impacts of condition variables in rule-based models of space–time choice behavior: method of empirical illustration. Geographical Analysis, 35, p. 24–45.
Transit Cooperative Research Program, (2007). TCRP Report 122 : Understanding How to Motivate Communities to Support and Ride Public Transportation. Transportation Research Board, National Research Council, Washington D.C.
Transit Cooperative Research Program (1999). TCRP Report 63 :Enhancing the Visibility and Image of Transit in the United States and Canada, Transportation Research Board, National Research Council, Washington D.C.
Axelrod R. (1976). Structure of Decision: The Cognitive Maps of Political Elites, Princeton University Press, Princeton, NJ.
Kang I., Lee S., Choi J., 2004. Using fuzzy cognitive map for the relationship management in airline service. Expert Systems with Applications, 26, 545–555.
Kosko B. (1986). Fuzzy Cognitive Maps. International Journal of Man-Machine Studies, 24, p. 65–75.
Aguilar J. (2005). A Survey about Fuzzy Cognitive Maps Papers. International Journal of Computational Cognition, 3 (2), p. 27–33.
Peña, A., Sossa H., and Gutiérrez A. (2007). Cognitive Maps: an Overview and their Application for Student Modeling. Computación y Sistemas, 10 (3), p. 230–250.
Tsadiras A.K. (2007). Inference using Binary, Trivalent and Sigmoid Fuzzy Cognitive Maps, Proceedings of The 10th International Conference on Engineering Applications of Neural Networks, Thessaloniki, Greece.
Kardaras D. and Gregory M. (1999). Using Fuzzy Cognitive Maps to Model and Analyze Business Performance Assessment. In: Jacob Chen and Anil Mital (Eds.), Advances in Industrial Engineering Applications and Practice II, p. 63–68.
Lee K.C. and Lee S. (2003). A cognitive map simulation approach to adjusting the design factors of the electronic commerce web sites. Expert Systems with Applications, 24, p. 1–11.
Potter S., Enoch M., and Smith M. (1997). Vital transportation statistics, Landor Publishing.
Xirogiannisa G., Stefanoua J., and Glykas M. (2004). A fuzzy cognitive map approach to support urban design. Expert Systems with Applications, 26, p. 257–268.
Hsu C.S. (2010). Determinants of passenger transfer waiting time at multi-modal connecting stations. Transportation Research Part E, (46), p. 404–413.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Atlantis Press
About this chapter
Cite this chapter
Ugurlu, S., Ilker Topcu, Y. (2012). Using Fuzzy Cognitive Maps as a Modeling Tool for Traveler Satisfaction in Public Transit Systems. In: Kahraman, C. (eds) Computational Intelligence Systems in Industrial Engineering. Atlantis Computational Intelligence Systems, vol 6. Atlantis Press, Paris. https://doi.org/10.2991/978-94-91216-77-0_18
Download citation
DOI: https://doi.org/10.2991/978-94-91216-77-0_18
Publisher Name: Atlantis Press, Paris
Print ISBN: 978-94-91216-76-3
Online ISBN: 978-94-91216-77-0
eBook Packages: Computer ScienceComputer Science (R0)