Modelling the Shifts in Activity Centres along the Subway Stations. The Case Study of Metropolitan Tehran
Abstract
Activity centers are areas of strong development of a particular activity, such as residence, employment, or services. Understanding the subway system impacts on the type, combination, distribution and totally the development of basic activities in these centers, have an important role in managing development opportunities created along the Tehran subway lines. The multi criteria and fuzzy nature of evaluating the activity centers development make the issue as complex as cannot be addressed with conventional logical systems. One of the most important methods of multi criteria evaluation is Fuzzy Inference System. Fuzzy inference system is a popular computing framework based on the concepts of Fuzzy Sets Theory, which is capable of accommodating inherent uncertainty in multi-criteria evaluation process. This paper analyze shifts in activity centers along two lines of the Tehran subway system based on three major criteria by designing a comprehensive fuzzy inference system. The data for the present study were collected through documentary analysis, questionnaires and semi-structured interview. The result revealed that the level of the subway system influence on the pattern and process of the development of activities varied with the location, physical environment and entity of each station. Furthermore, empirical findings indicated that the subway line might weaken residential activities while attracting employment and service activities to the city center. Specifically, residential estates moved away from the city center to the suburbs whereas employment and service activities expanded from the existing central business district (CBD). The results can be applied to suggest planning policies aiming at improving the effects of public transit on property developemnet and land use change in a developing country.
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References
Abraham, A. (2005). Adaptation of fuzzy inference system using neural learning. Fuzzy systems engineering (pp. 53-83). Springer Berlin Heidelberg. doi:http:// 10.1007/11339366_3.
Barlett, J. E., Kotrlik, J. W., & Higgins, C. C. (2001). Organizational research: Determining appropriate sample size in survey research. Information technology, learning, and performance journal, 19(1), 43.
Bonivento, C., Fantuzzi, C., & Rovatti, R. (1998). Fuzzy logic control: Advances in methodology. World Scientific Publishing Co., Inc..
Cervero, R. (2004). Transit-oriented development in the United States: Experiences, challenges, and prospects (Vol. 102). Transportation Research Board.
Cervero, R., & Landis, J. (1997). Twenty years of the Bay Area Rapid Transit system: Land use and development impacts. Transportation Research Part A: Policy and Practice, 31(4), 309-333. doi:http://10.1016/S0965-8564(96)00027-4.
Daisa, J. (2004). Traffic, parking, and transit-oriented development. The New Transit Town: best practices in transit-oriented development, 114-129. doi:http://dx.doi.org/10.5860/choice.42-0424.
Dubois, D., and Prade, H. (1996). What are fuzz rules and how to use them? Fuzzy Sets and Systems, 84(2), 169-185. doi:http://dx.doi.org/10.1016/0165-0114(96)00066-8.
Fejarang, R. A. (1993). Impact on property values: A study of the Los Angeles metro rail. In PUBLIC TRANSPORT PLANNING AND OPERATIONS. PROCEEDINGS OF SEMINAR H HELD AT THE EUROPEAN TRANSPORT, HIGHWAYS AND PLANNING 21ST SUMMER ANNUAL MEETING (SEPTEMBER 13-17, 1993), UMIST. VOLUME P370.
Gatzlaff, D. H., & Smith, M. T. (1993). The impact of the Miami Metrorail on the value of residences near station locations. Land Economics, 54-66. doi:http://dx.doi.org/10.2307/3146278.
Höhle, U., & Rodabaugh, S. E. (1999). Mathematics of Fuzzy Sets: Logic, Topology, and Measure Theory (Vol. 3). Springer Science & Business Media.
Kaur, A., & Kaur, A. (2012). Comparison of fuzzy logic and neuro-fuzzy algorithms for air conditioning system. International journal of soft computing and engineering, 2(1), 417-20. doi: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.648.6698&rep=rep1&type=pdf.
Klir, G. J., & Folger, T. A. (1988). Fuzzy sets, uncertainty, and information. Prentice-Hall International Inc., New Jersey.
Leung, K. S., & Lam, W. (1988). Fuzzy concepts in expert systems. Computer, 21(9), 43-56.
Lin, J. J., Feng, C. M., & Hu, Y. Y. (2006). Shifts in activity centers along the corridor of the blue subway line in Taipei. Journal of urban planning and development, 132(1), 22-28. doi:http://dx.doi.org/10.1061/(asce)0733-9488(2006)132:1(22).
Mamdani, E. H., & Assilian, S. (1975). An experiment in linguistic synthesis with a fuzzy logic controller. International journal of man-machine studies, 7(1), 1-13. doi:http://dx.doi.org/10.1016/s0020-7373(75)80002-2.
Pacheco-Raguz, J. (2010). Assessing the impacts of Light Rail Transit on urban land in Manila. The Journal of Transport and Land use, 3(1), 113-138. doi:http://dx.doi.org/10.5198/jtlu.v3i1.13.
Pedrycz, W. (2001). Fuzzy equalization in the construction of fuzzy sets. Fuzzy sets and systems, 119(2), 329-335. doi:http://dx.doi.org/10.1016/s0165-0114(99)00135-9.
Shariati, S. (2012). Exploring the Spatial Impacts of Tehran Subway System, M.S Thesis in Urban and Regional Planning, supervised by Dr. A. Soltani. Shiraz University.
Strauss, A., & Corbin, J. (1990). Basics of qualitative research (Vol. 15). Newbury Park, CA: Sage.
Tanaka, C. (2002), Fuzzy Set Theory & its Practical Applications, translated by Ali Vahidian Kamyad and Hamed Reza Tareghian, Ferdowsi University Mashhad, publication No.327.
Teodorović, D. (1999). Fuzzy logic systems for transportation engineering: the state of the art. Transportation Research Part A: Policy and Practice, 33(5), 337-364. doi:http://dx.doi.org/10.1016/s0965-8564(98)00024-x.
Transport and Traffic Organization of Tehran Municipality, (2012). Comprehensive studies of transportation and traffic in Tehran metropolitan. No 934.
Wang, L. X., & Mendel, J. M. (1992). Generating fuzzy rules by learning from examples. IEEE Transactions on systems, man, and cybernetics, 22(6), 1414-1427. doi:http://dx.doi.org/10.1109/21.199466.
Zahedy, M. (1999). Fuzzy Set Theory and its Applications, Tehran, University Publication.
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