Fuzzy Logic–Based Travel Demand Model to Simulate Public Transport Policies
Publication: Journal of Urban Planning and Development
Volume 141, Issue 4
Abstract
Four-stage travel demand modeling comprises of trip generation, trip distribution, mode choice, and traffic assignment. Limitations of conventional four-stage models are that they do not take into account subjectivity, imprecision, ambiguity, and vagueness involved in human decisions. In this direction, fuzzy logic is found to be the most suitable technique because it considers linguistic variables and expressions. Keeping this in view, the present study proposes to develop a methodology to consider fuzzy logic technique at different stages to develop travel demand models. The fuzzy logic–based travel demand models are developed in MATLAB software considering subtractive clustering technique. Four-stage conventional models are also developed to compare the efficiency of fuzzy logic models. The modeling results in terms , root-mean-square error (RMSE), and average error from both conventional and fuzzy logic models show that fuzzy logic models yield improved results in comparison to the conventional models. Further, to demonstrate the suitability of the developed fuzzy logic travel demand model, selected public transport policies are simulated considering appropriate parameters.
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Acknowledgments
The authors are grateful to the Director, CSIR-Central Road Research Institute for allowing to publish this paper. The authors also acknowledge the help rendered by the staff of CRRI during the data collection.
References
Andaman Public Work Department (APWD). (2009). “Master plan for Port Blair planning area 2030.” Final Draft Rep., Town and Country Planning Unit.
Arslan, T., and Khisty, C. J. (2004). “A rational approach to handling fuzzy perceptions in route choice.” Eur. J. Oper. Res., 168(2), 571–583.
Bataineh, K. M., Naji, M., and Saqer, M. A. (2011). “Comparison study between various fuzzy clustering algorithms.” Jordan J. Mech. Ind. Eng., 5(4), 335–343.
Census of India. (2011). Census of India 2011, 〈http://www.censusindia.gov.in/2011-prov-results/prov_data_products_ani.html〉 (Jun. 23, 2013).
Chopra, S., Mitra, R., and Kumar, V. (2004). “Identification of rules using subtractive clustering with application to fuzzy logic controllers.” Proc., 3rd Int. Conf. on Machine Learning and Cybernetics, IEEE, New York, 4125–4130.
Errampalli, M. (2008). “Fuzzy logic based microscopic traffic simulation model for transport policy evaluation.” Ph.D. thesis, Gifu Univ., Gifu, Japan.
Gupta, R., Kewalramani, M. A., and Ralegaonkar, R. V. (2003). “Environmental impact analysis using fuzzy relation for landfill siting.” J. Urban Plann. Dev., 121–139.
Kalic, Â. M., and Teodorovic, Â. D. (1997). “A soft computing approach to trip generation modeling.” 9th Mini EURO Conf. Fuzzy Sets in Traffic and Transport Systems, EURO, Univ. of Belgrade, Belgrade, Serbia.
Kalic, Â. M., and Teodorovic, Â. D. (2003). “Trip distribution modeling using fuzzy logic and genetic algorithm.” Transp. Plann. Technol., 26(3), 213–238.
Kikuchi, S., and Pursula, M. (1998). “Treatment of uncertainty in study of transportation: Fuzzy set theory and evidence theory.” J. Transp. Eng., 1–8.
Mizutani, K., and Akiyama, T. (2001). “Construction of modal choice model with a descriptive utility function using fuzzy reasoning.” Proc., International Fuzzy System Association (IFSA) World Conf., Vol. 2, IEEE, New York, 852–856.
Murat, Y. Z., and Uludag, N. (2008). “Route choice modelling in urban transport network using fuzzy logic and logistic regression methods.” J. Sci. Ind. Res., 67(1), 19–27.
Orco, M. D., Circella, G., and Sassanelli, D. (2007). “A hybrid approach to combine fuzziness and randomness in travel choice predictions.” Eur. J. Oper. Res., 185(2), 648–658.
Song, B., Hao, S., Murakami, S., and Sadohara, S. (1996). “Comprehensive evaluation method on earthquake damage using fuzzy set theory.” J. Urban Plann. Dev., 1–17.
Statistical Package for Social Sciences (SPSS), version 17 [Computer software]. India, IBM.
Teodorovic, Â. D., and Kikuchi, S. (1990). “Transportation route choice model using fuzzy inference technique.” Proc., ISUMA ‘90, 1st Int. Symp. on Uncertainty Modeling and Analysis, IEEE Computer Press, New York, 140–145.
Teodorovic, D., and Kalic, M. (1996). “Numerical-linguistic approach to the modal split problem.” Proc., ISUMA ‘90, 1st Int. Symp. on Uncertainty Modeling and Analysis, IEEE Computer Press, New York, 140–145.
Visum version 12 [Computer software]. Karlsruhe, Germany, PTV.
Vythoulkas, P. C., and Koutsopoulos, H. N. (2003). “Modelling discrete choice behavior using concepts from fuzzy set theory, approximate reasoning and neural network.” Transp. Res. Part C, 11(1), 51–73.
Wang, L.-X., and Mendel, J. (1992). “Generating fuzzy rules by learning from examples.” IEEE Trans. Syst. Man Cybern., 22(6), 1414–1427.
Zadeh, A. (1965). “Fuzzy sets.” Inf. Control, 8(3), 338–353.
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© 2014 American Society of Civil Engineers.
History
Received: Dec 10, 2013
Accepted: Sep 18, 2014
Published online: Nov 3, 2014
Discussion open until: Apr 3, 2015
Published in print: Dec 1, 2015
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