1] A. R. Arabpour and M. Tata, Estimating the parameters of a fuzzy linear regression model,
Iranian Journal of Fuzzy Systems, 5(2) (2008), 1-19.
[2] T. Arslan and C. J. Khisty, A rational reasoning method from fuzzy perceptions in route
choice, Fuzzy Sets and Systems, 150 (2005), 419-435.
[3] L. Caggiani, M. Ottomanelli and D. Sassanelli, A xed point approach to origin-destination
matrices estimation using uncertain data and fuzzy programming on congested networks,
Transportation Research Part C: Emerging Technologies, In Press, Corrected Proof, 2011.
[4] P. Chakroborty and S. Kikuchi, Calibrating the membership functions of the fuzzy infer-
ence system: instantiated by car-following data, Transportation Research Part C: Emerging
Technologies, 11(2) (2003), 91-119.
[5] P. Chakroborty and S. Kikuchi, Evaluation of the general motors based car-following models
and a proposed fuzzy inference model, Transportation Research Part C, 7 (1999), 209-235.
[6] S. Chattefuee and A. S. Hadi, Regression analysis by example, Fourth Edition, In: W. A.
Shewhart and S. S. Wilks, eds., Wiley Series in Probability and Statistics: John Wiley and
Sons, Inc., 2006.
[7] O. Cordon, F. Herrera, F. Homann and L. Magdalena, Genetic fuzzy systems evolutionary
tuning and learning of fuzzy knowledge bases, In: K. Hirota, et al., eds., Advances in Fuzzy
Systems - Applications and Theory, Sinqgapore: World Scientic, 19 (2001).
[8] I. Derbel, N. Hachani and H. Ounelli, Membership Functions Generation Based on Density
Function, In: IEEE International Conference on Computational Intelligence and Security,
IEEE, 2008.
[9] L. Dimitriou, T. Tsekeris and A. Stathopoulos, Adaptive hybrid fuzzy rule-based system
approach for modeling and predicting urban trac
ow, Transportation Research Part C:
Emerging Technologies, 16(5) (2008), 554-573.
[10] T. A. Domencich and D. Mcfadden, Urban travel demand: a behavioral analysis, In: D.
W. Jorgenson and J. Waelbroeck, eds., Contributions to Economic Analysis, Amsterdam,
Oxford: North-Holland Publishing Company, 1975.
[11] L. R. Foulds, H. A. D. D. Nascimento, I. C. A. C. Calixto, B. R. Hall and H. Longo, A fuzzy
set approach to estimating od matrices in congested brazilian trac networks, In: XLIII
Simposio Brasileiro de Pesquisa Operacional, XLIIISBPO, 2011.
[12] M. Ghatee and S. M. Hashemi, Trac assignment model with fuzzy level of travel demand an
ecient algorithm based on quasi-logit formulas, European Journal of Operational Research,
194 (2009), 432-451.
[13] A. Golnarkar, A. A. Ale Sheykh and M. R. Malek, Solving best path problem on multimodal
transportation networks with fuzzy costs, Iranian Journal Of Fuzzy Systems, 7(3) (2010),
1-13.
[14] H. Hassanpour, H. R. Maleki and M. A. Yaghoobi, A note on evaluation of fuzzy linear
regression models by comparing membership functions, Iranian Journal of Fuzzy Systems,
6(2) (2009), 1-6.
[15] H. Hassanpour, H. R. Maleki and M. A. Yaghoobi, Fuzzy linear regression model with crisp
coecients: a goal programming approach, Iranian Journal of Fuzzy Systems, 7(2) (2010),
19-39.
[16] Y. E. Hawas, Development and calibration of route choice utility models: neuro-fuzzy ap-
proach, ASCE Journal of Transportation Engineering, 130(2) (2004), 171-182.
[17] R. L. Hurst, Qualitative variables in regression analysis, American Educational Research
Journal, 7(4) (1970), 541-552.
[18] Institute for Transportation Research and Studies, Mashad comprehensive transportation
study, Sharif University of Technology: Tehran, Iran, 2000.
[19] P. Jain, Automatic trac signal controller for roads by exploiting fuzzy logic, In: V. V. Das,
J. Stephen, and Y. Chaba, eds., Computer Networks and Information Technologies, Springer
Berlin Heidelberg, (2011), 273-277.
[20] M. Kaczmarek, Fuzzy group model of trac
ow in street networks, Transportation Research
Part C: Emerging Technologies, 13(2) (2005), 93-105.
[21] A. K. Kanafani, transportation demand analysis, Mcgraw-Hill Series in Transportation, New
York: McGraw-Hill, 1983.
[22] M. G. Karlaftis and E. I. Vlahogianni, Statistical methods versus neural networks in trans-
portation research: dierences, similarities and some insights, Transportation Research Part
C: Emerging Technologies, 19(3) (2011), 387-399.
[23] N. K. Kasabov, Foundations of neural networks, fuzzy systems, and knowledge engineering,
Cambridge, Mass.: MIT Press, 1996.
[24] S. Kikuchi, Fuzzy sets theory approach to transportation problems, articial intelligence in
transportation, Transportation Research Board: Washington DC, 2007.
[25] S. Kikuchi and P. Chakroborty, Place of possibility theory in transportation analysis, Transportation
Research Part B: Methodological, 40(8) (2006), 595-615.
[26] S. Kikuchi and M. Pursula, Treatment of uncertainty in study of transportation: fuzzy set
theory and evidence theory, ASCE Journal of Transportation Engineering, 124(1) (1998).
[27] K. Kim, D. Kim and H. Seo, Neural network architecture for the estimation of drivers' route
choice, KSCE Journal of Civil Engineering, 6(3) (2002), 329-336.
[28] G. J. Klir and B. Yuan, Fuzzy sets and fuzzy logic: theory and applications, New Delhi:
Prentice-Hall, 2002.
[29] I. Kosonen, Multi-agent fuzzy signal control based on real-time simulation, Transportation
Research Part C, 11 (2003), 389-403.
[30] C. T. Leondes, ed., Fuzzy logic and expert systems applications, Neural Network Systems:
Techniques and Applications, Academic Press, 6 (1998).
[31] K. K. Lim and S. Srinivasan, A comparative analysis of alternate econometric structures
for trip-generation models, In: Transportation Research Board Annual Meeting, Washington
DC, USA: Transportation Research Board, 2011.
[32] M. D. Meyer and E. J. Miller, Urban transportation planning: a decision-oriented approach,
2nd edition, Mcgraw-Hill Series in Transportation, Boston: McGraw-Hill, 2001.
[33] J. L. Mwakalonge and D. A. Badoe, Data collected in single and repeated cross-sectional
surveys, In: Transportation Research Board Annual Meeting, Washington DC, USA: Transportation
Research Board, 2011.
[34] S. M. A. Nayeem and M. Pal, The p-center problem on fuzzy networks and reduction of cost,
Iranian Journal of Fuzzy Systems, 5(1) (2008), 1-26.
[35] J. Niittymaki and M. Pursula, Signal control using fuzzy logic, Fuzzy Sets and Systems,
22(2) (2000), 11-22.
[36] I. Nosoohi and S. N. Shetab-Boushehri, A conceptual methodology for transportation projects
selection, International Journal of Industrial Engineering and Production Research, 22(2)
(2011), 83-90.
[37] J. D. D. Ortuzar and L. G. Willumsen, Modelling transport, Fourth edition, Chichester, West
Sussex, United Kingdom: John Wiley and Sons, 2011.
[38] S. Pourahmad, S. M. T. Ayatollahi and S. M. Taheri, Fuzzy logistic regression: a new possi-
bilistic model and its application in clinical vague status, Iranian Journal of Fuzzy Systems,
8(1) (2011), 1-17.
[39] A. K. Prokopowicz and V. Sotnikov, An application of a hybrid fuzzy logic and expert sys-
tem to transportation modal split evaluation, In: IEEE Intelligent Transportation Systems
Conference Proceedings, Oakland (CA), USA: IEEE, 2001
[40] J. O. Rawlings, S. G. Pantula and D. A. Dickey, Applied regression analysis: a research tool,
Springer, 1998.
[41] Y. Shafahi and E. S. Abrishami, School trip attraction modeling using neural and fuzzy-
neural approaches, In: Proceedings of the 8th International IEEE Conference on Intelligent
Transportation Systems, Vienna, Austria, 2005.
[42] Y. Shafahi and R. Faturechi, A new fuzzy approach to estimate the o-d matrix from link
volumes, Transportation Planning and Technology, 32(6) (2009), 499-526.
[43] Y. Shafahi, S. M. Nourbakhsh and S. Seyedabrishami, Fuzzy trip distribution models for
discretionary trips, In: Proceedings of the 11th International IEEE Conference on Intelligent
Transportation Systems, Beijing, China: IEEE, 2008.
[44] W. Siler and J. J. Buckley, Fuzzy expert systems and fuzzy reasoning, John Wiley and Sons,
Inc., 2005.
[45] S. N. Sivanandam, S. Sumathi and S. N. Deepa, Introduction to fuzzy logic using matlab,
Berlin , New York, Springer, 2007.
[46] J. Smart, Wxclips user manual, Articial Intelligence Applications Institute, University of
Edinburgh, 1995.
[47] D. Teodorovic, Fuzzy sets theory applications in trac and transportation, European Journal
of Operational Research, 74 (1994), 379-390.
[48] D. Teodorovic and K. Vukadinovic, Trac control and transport planning: a fuzzy sets and
neural networks approach, International Series in Intelligent Technologies, Boston: Kluwer
Academic Publishers, 1998.
[49] A. Tortum, N. Yayla and M. Gokdag, The modeling of mode choices of intercity freight
transportation with the articial neural networks and adaptive neuro-fuzzy inference system,
Expert Systems with Applications, 36 (2009), 6199-6217.
[50] G. H. Tzeng and J. Y. Teng, Transportation investment project selection with fuzzy multiob-
jectives, Transportation Planning and Technology, 17(2) (1993), 91-112.
[51] S. P. Washington, M. G. Karlaftis and F. L. Mannering, Statistical and econometric methods
for transportation data, CRC Press, 2003.
[52] S. Weisberg, Applied linear regression, In: D. J. Balding, et al., eds., Wiley Series in Probability
and Statistics, Hoboken, New Jersey: John Wiley and Sons, Inc., 2005.
[53] G. Yaldi and M. a. P. Taylor, Examining the possibility of fuzzy set theory application in
travel demand modelling, Journal of the Eastern Asia Society for Transportation Studies, 8
(2010).
[54] H. Yin, S. C. Wong, J. Xu and C. K. Wong, Urban trac
ow prediction using a fuzzy-neural
approach, Transportation Research Part C: Emerging Technologies, 10(2) (2002), 85-98.
[55] F. Young, J. D. Leeuw and Y. Takane, Regression with qualitative and quantitative variables:
an alternating least squares method with optimal scaling features, Psychometrika, 41(4)
(1976), 505-529.
[56] L. Zhang, H. Li and P. D. Prevedouros, Signal control for oversaturated intersections using
fuzzy logic, In: Transportation Research Board Annual Meeting, Washington DC, USA, 2005.
[57] H. J. Zimmermann, Fuzzy set theory and its applications, Third Edition: Kluwer Academic
Publishers, 1996.