PREDICTING URBAN TRIP GENERATION USING A FUZZY EXPERT SYSTEM

سال انتشار: 1391
نوع سند: مقاله ژورنالی
زبان: انگلیسی
مشاهده: 207

فایل این مقاله در 20 صفحه با فرمت PDF قابل دریافت می باشد

این مقاله در بخشهای موضوعی زیر دسته بندی شده است:

استخراج به نرم افزارهای پژوهشی:

لینک ثابت به این مقاله:

شناسه ملی سند علمی:

JR_IJFS-9-3_009

تاریخ نمایه سازی: 7 تیر 1401

چکیده مقاله:

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: ۵۹.۳۷ E, Lat: ۳۶.۱۹ 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.

نویسندگان

Amir Abbas Rassafi

Faculty of Engineering, Imam Khomeini International Univer- sity, Qazvin, ۳۴۱۴۹, Iran

Roohollah Rezaei

Faculty of Engineering, Imam Khomeini International University, Qazvin, ۳۴۱۴۹, Iran

Mehdi Hajizamani

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

مراجع و منابع این مقاله:

لیست زیر مراجع و منابع استفاده شده در این مقاله را نمایش می دهد. این مراجع به صورت کاملا ماشینی و بر اساس هوش مصنوعی استخراج شده اند و لذا ممکن است دارای اشکالاتی باشند که به مرور زمان دقت استخراج این محتوا افزایش می یابد. مراجعی که مقالات مربوط به آنها در سیویلیکا نمایه شده و پیدا شده اند، به خود مقاله لینک شده اند :
  • ۱] A. R. Arabpour and M. Tata, Estimating the parameters ...
  • T. Arslan and C. J. Khisty, A rational reasoning method ...
  • L. Caggiani, M. Ottomanelli and D. Sassanelli, A xed point ...
  • P. Chakroborty and S. Kikuchi, Calibrating the membership functions of ...
  • P. Chakroborty and S. Kikuchi, Evaluation of the general motors ...
  • S. Chattefuee and A. S. Hadi, Regression analysis by example, ...
  • Shewhart and S. S. Wilks, eds., Wiley Series in Probability ...
  • O. Cordon, F. Herrera, F. Ho mann and L. Magdalena, Genetic ...
  • I. Derbel, N. Hachani and H. Ounelli, Membership Functions Generation ...
  • L. Dimitriou, T. Tsekeris and A. Stathopoulos, Adaptive hybrid fuzzy ...
  • T. A. Domencich and D. Mcfadden, Urban travel demand: a ...
  • W. Jorgenson and J. Waelbroeck, eds., Contributions to Economic Analysis, ...
  • L. R. Foulds, H. A. D. D. Nascimento, I. C. ...
  • M. Ghatee and S. M. Hashemi, Trac assignment model with ...
  • A. Golnarkar, A. A. Ale Sheykh and M. R. Malek, ...
  • H. Hassanpour, H. R. Maleki and M. A. Yaghoobi, Fuzzy ...
  • R. L. Hurst, Qualitative variables in regression analysis, American Educational ...
  • Institute for Transportation Research and Studies, Mashad comprehensive transportationstudy, Sharif ...
  • P. Jain, Automatic trac signal controller for roads by exploiting ...
  • M. Kaczmarek, Fuzzy group model of tracow in street networks, ...
  • A. K. Kanafani, transportation demand analysis, Mcgraw-Hill Series in Transportation, ...
  • M. G. Karlaftis and E. I. Vlahogianni, Statistical methods versus ...
  • N. K. Kasabov, Foundations of neural networks, fuzzy systems, and ...
  • S. Kikuchi, Fuzzy sets theory approach to transportation problems, arti cial ...
  • S. Kikuchi and P. Chakroborty, Place of possibility theory in ...
  • S. Kikuchi and M. Pursula, Treatment of uncertainty in study ...
  • K. Kim, D. Kim and H. Seo, Neural network architecture ...
  • G. J. Klir and B. Yuan, Fuzzy sets and fuzzy ...
  • I. Kosonen, Multi-agent fuzzy signal control based on real-time simulation, ...
  • C. T. Leondes, ed., Fuzzy logic and expert systems applications, ...
  • K. K. Lim and S. Srinivasan, A comparative analysis of ...
  • M. D. Meyer and E. J. Miller, Urban transportation planning: ...
  • J. L. Mwakalonge and D. A. Badoe, Data collected in ...
  • S. M. A. Nayeem and M. Pal, The p-center problem ...
  • J. Niittymaki and M. Pursula, Signal control using fuzzy logic, ...
  • I. Nosoohi and S. N. Shetab-Boushehri, A conceptual methodology for ...
  • J. D. D. Ortuzar and L. G. Willumsen, Modelling transport, ...
  • S. Pourahmad, S. M. T. Ayatollahi and S. M. Taheri, ...
  • A. K. Prokopowicz and V. Sotnikov, An application of a ...
  • Y. Shafahi and E. S. Abrishami, School trip attraction modeling ...
  • Y. Shafahi and R. Faturechi, A new fuzzy approach to ...
  • Y. Shafahi, S. M. Nourbakhsh and S. Seyedabrishami, Fuzzy trip ...
  • W. Siler and J. J. Buckley, Fuzzy expert systems and ...
  • S. N. Sivanandam, S. Sumathi and S. N. Deepa, Introduction ...
  • J. Smart, Wxclips user manual, Arti cial Intelligence Applications Institute, University ...
  • D. Teodorovic, Fuzzy sets theory applications in trac and transportation, ...
  • D. Teodorovic and K. Vukadinovic, Trac control and transport planning: ...
  • A. Tortum, N. Yayla and M. Gokdag, The modeling of ...
  • G. H. Tzeng and J. Y. Teng, Transportation investment project ...
  • S. P. Washington, M. G. Karlaftis and F. L. Mannering, ...
  • S. Weisberg, Applied linear regression, In: D. J. Balding, et ...
  • G. Yaldi and M. a. P. Taylor, Examining the possibility ...
  • F. Young, J. D. Leeuw and Y. Takane, Regression with ...
  • L. Zhang, H. Li and P. D. Prevedouros, Signal control ...
  • H. J. Zimmermann, Fuzzy set theory and its applications, Third ...
  • نمایش کامل مراجع