PREDICTING URBAN TRIP GENERATION USING A FUZZY EXPERT SYSTEM

Document Type : Research Paper

Authors

1 Faculty of Engineering, Imam Khomeini International Univer- sity, Qazvin, 34149, Iran

2 Faculty of Engineering, Imam Khomeini International University, Qazvin, 34149, Iran

3 MIT-Portugal Program, Instituto Superior Tcnico, Technical University of Lisbon, Lisbon, Portugal

Abstract

One of the most important stages in the urban transportation
planning procedure is predicting the rate of trips generated by each trac zone.
Currently, multiple linear regression models are frequently used as a prediction
tool. This method predicts the number of trips produced from, or attracted to
each trac zone according to the values of independent variables for that zone.
One of the main limitations of this method is its huge dependency on the exact
prediction of independent variables in future (horizon of the plan). The other
limitation is its many assumptions, which raise challenging questions of its
application. The current paper attempts to use fuzzy logic and its capabilities
to estimate the trip generation of urban zones. A fuzzy expert system is
introduced, which is able to make suitable predictions using uncertain and
inexact data. Results of the study on the data for Mashhad (Lon: 59.37 E,
Lat: 36.19 N) show that this method can be a good competitor for multiple
linear regression method, specially, when there is no exact data for independent
variables.

Keywords


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. Ho mann 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 Scienti c, 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: di erences, 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, arti cial 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, Arti cial 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 arti cial 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.