مدل سازی فرایند پیش بینی سفر در برنامه ریزی حمل و نقل درون شهری مبتنی بر رویکرد ترکیبی استنتاج فازی
محورهای موضوعی : مدیریت بازرگانی
1 - دانشیار، واحد علوم و تحقیقات تهران ،دانشگاه آزاد اسلامی،تهران،ایران
2 - دانشجوی دکترای مدیریت صنعتی، واحد علوم و تحقیقات تهران، دانشگاه آزاد اسلامی،تهران،ایران
کلید واژه: برنامه ریزی حمل و نقل درون شهری, سیستم استنتاج فازی, پیش بینی سفر,
چکیده مقاله :
برنامه ریزی حمل و نقل درون شهری در دوره های اخیر همواره یکی از تصمیمات مهم در حوزه برنامه ریزی شهری در کلان شهرها بوده است. در این بین، پیش بینی حجم سفرهای آتی بین دو منطقه کلان شهر، کلید موفقیت در این امر برنامه ریزی صحیح حمل و نقل به شمار می رود. به دلیل اهمیت برنامه ریزی حمل و نقل درون شهری، مدل های مختلفی توسط محققین در این زمینه توسعه داده شده که بعضی از این مدل ها علیرغم قدمت زیاد، همچنان در حوزه های عملیاتی به کار گرفته می شوند. مساله اساسی در توسعه این مدل ها، پیچیدگی مساله است که از ماهیت رفتار انسانی در انتخاب ناشی می شود. این پیچیدگی سبب می شود تا همواره توسعه مدلی که خطای پیش بینی قابل قبولی داشته باشد، با مشکلات فراوان محاسباتی و عملیاتی روبرو باشد. این مساله در کشورهای در حال توسعه و یا توسعه نیافته که در آن ها داده های تاریخی به شکل مناسب در اختیار نیستند و ظرفیت های محاسباتی رایانه ای نیز به طور کامل در دسترس نمی باشد، از اهمیت بسیار بیشتری برخوردار است. در این پژوهش، یک مدل سه مرحله ای فازی برای مدل سازی فرایند سفر بین دو ناحیه مفروض از یک کلان شهر و در نهایت چارچوبی برای پیش بینی آتی این کمیت پیشنهاد شده است تا بر اساس آن بتوان برای نگاشت بین حجم سفرهای انجام شده بین دو ناحیه به عنوان متغیر خروجی و متغیرهای جمعیت شناختی و اجتماعی به عنوان متغیر ورودی، تابعی را تقریب زد که بتواند فرایند انجام سفر را مدل کند. در این مدل، پایگاه قواعد فازی در حقیقت در پی انتقال الگوی ذهنی متخصصین حمل و نقل به مدل ریاضی تشکیل شده است.
Urban Transportation Planning (UTP) has been one of the most important decisions in urban planning and development procedures in recent years. Meanwhile, accurate trip forecasting between two given regions of the city could be considered as the key success factor of urban transportation planning. Due to the importance of the problem, different models have been developed in the field. The overall problem of trip forecasting and transportation planning could be complicated because of its nature that results from the complicated nature of human behavior. Due to the complexity of the problem, it is always hard to develop forecasting models with acceptable forecasting errors and also low computational expenses particularly in developing countries in which historical data are not fully available. In this paper, a three phase fuzzy model is proposed to forecast trips flow between two given regions of a metropolitan based on mapping demographical and social variables to total number of trips flow. The overall model is to explore the subjective pattern of transportation experts and transfer the subjective model to a mathematical framework.
Celikoglu, B., Hilmi, Cigizoglu, K., Hekmet, (2007), Public transportation trip flow modeling with generalized regression neural networks, Advances in Engineering Software 38. pp. 71–79.
Chanas, S., Delgado, M., Verdegay, J.L., Vila, M.A., (1993), Interval and fuzzy extensions of classical transportation problems. Transportation Planning and Technology 17. pp. 203-218.
Chang, Y.-H., Shyu, T.-H., (1993), Traffic signal installation by the expert system using fuzzy set theory for inexact reasoning. Transportation Planning and Technology 17. pp. 191-202.
Chen H, Muller SG., (2001), Use of sequential learning for short-term traffic flow forecasting. Trans Res Part C Emerg Technol; 9(5). pp. 319–36.
Chen, L., May, A., Auslander, D., (1990), Freeway ramp control using fuzzy set theory for inexact reasoning. Transportation Research 24A. pp. 15-25.
Deb, S.K., (1993), Fuzzy set approach in mass transit mode choice. In: Ayyub, B.M. (Ed.), Proceedings of ISUMA '93, Second International Symposium on Uncertainty Modeling and Analysis. IEEE Computer Press, College Park, Maryland, pp. 262-268.
Dougherty MS. (1995), A review of neural networks applied to transport. Transp Res Part C Emerg Technol 1995;3(4). pp. 247–60.
Golob F T., (2000), A simultaneous model of household activity participation and trip chain generation. In: Transportation Research Record, Journal of the Transportation Research Board, No. 34, TRB. Washington, USA. pp. 355-376.
Jang, J.-S. R. and C.-T. (1997), Sun, Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence, Prentice Hall.
Lin CT, Chiu H, Chu PY, (2006). Agility index in supply chain. Int. J. Prod. Econ. 100. pp. 285-299.
Lotan, T., Koutsopoulos, H., (1993). Route choice in the presence of information using concepts from fuzzy control and approximate reasoning. Transportation Planning and Technology 17. pp. 113-126.
Lotan, T., Koutsopoulos, H., (1993), Models for route choice behaviour in the presence of information using concepts from fuzzy set theory and approximate reasoning. Transportation 20. pp. 129-155.
Makvandi, p., Alavi, S., H., Hajiha, A., (2006), An Exploration of Experts' Subjective Patterns in Behavioral Based Job Qualification Using Choquet Integral, Proceedings of the 6th WSEAS Int. Conf. on Systems Theory & Scientific Computation, Elounda, Greece, August 21-23. pp. 14-18.
Mamdani, E.H. and S. Assilian, (1975), An experiment in linguistic synthesis with a fuzzy logic controller," International Journal of Man-Machine Studies, Vol. 7, No. 1, pp. 1-13.
Messai N, Thomas P, Lefebvre D, El Moudni A., (2002), Optimal neural networks architectures for the flow-density relationships of traffic models. Math. Comput Simul 2002; 60(3–5). pp. 401–9.
Milosavljevic, N., Teodorovic, D., Papic, V., Pavkovic, G., (1996). A fuzzy approach to the vehicle assignment problem. Transportation Planning and Technology 20, pp. 33-47.
Murat, H., Celik, (2010), Sample size needed for calibrating trip distribution and behavior of the gravity model, Journal of Transport Geography 18. pp. 83–190
Nanda, R., Kikuchi, S., (1993), Estimation of trip O-D matrix when input and output are fuzzy. In: Ayyub, B.M. (Ed.), Proceedings of ISUMA '93, Second International Symposium on Uncertainty Modeling and Analysis. IEEE Computer Press, College Park, Maryland, pp. 104-111.
Novák, V., Perfilieva, I. and Močkoř, J. (1999), Mathematical principles of fuzzy logic Dodrecht: Kluwer Academic. ISBN 0-7923-8595-0
Ortuzar, J.D., Willumsen, L.G., (2001), the Traffic Assignment Problem: Models and Methods. VSP, Wiley, New York. Utrecht, the Netherlands.
Ross TJ, (2004), Fuzzy Logic with Engineering Applications. Second Edition. John Wiley and Sons Ltd. The Atrium, Southern Gate, Chichester, West Sussex PO19 8SQ, England.
Ruiter, E.R., Ben-Akiva, M.E., (1978), Disaggregate travel demand models for the San Francisco area: system structure, component models and application procedures. Transportation Research Record 673. pp. 121–128.
Smiller J, Hoel L A., (2006), assessing the utility of private information in transportation planning studies: A case study of trip generation analysis. Journal of Socio-Economic Planning Sciences, 40(3). pp. 94-118.
Sugeno, M., (1985), Industrial applications of fuzzy control, Elsevier Science Pub. Co.
Susilo, Y.O., Kitamura, R., (2007), Structural changes in commuters’ daily travel: the case of auto and transit commuters in the Osaka metropolitan area of Japan, 1980–2000. Transportation Research Part A: Policy and Practice 42. pp. 95–115.
Teodorovic D., (1999), Fuzzy logic systems for transportation engineering: the state of the art, Transportation Research Part A 33. pp. 337-364
Teodorovic, D., Babic, O., (1993), Fuzzy inference approach to the flow management problem in air traffic control. Transportation Planning and Technology 17. pp. 165-178.
Teodorovic, D., Kalic, M., (1995), A fuzzy route choice model for air transportation networks. Transportation Planning and Technology 19. pp. 109-119.
Vukadinovic, K., Teodorovic, D., (1994), A fuzzy approach to the vessel dispatching problem. European Journal of Operational Research 76. pp. 155-164.
Xu, W., Chan, Y., (1993), estimating an origin-destination matrix with fuzzy weights. Part 1: Methodology. Transportation Planning and Technology 17.pp. 127-144.
Yao Liya, GUAN Hongzhi, YAN Hai, (2008), Trip Generation Model Based on Destination Attractiveness, TSINGHUA SCIENCE and Technology,Volume 13,Number 5,pp632-635
Yun SY, Namkoong S, Rho JH, Shin SW, Choi JU., (1998), a performance evaluation of neural network models in traffic volume forecasting. Math Comput Model. 27(9–11). pp. 293–310.
Zhang H, Ritchie SG, Lo ZP., (1997), Macroscopic modeling of freeway traffic using an artificial neural network. Transp Res Record 1997; 1588. pp. 110–9.
_||_Celikoglu, B., Hilmi, Cigizoglu, K., Hekmet, (2007), Public transportation trip flow modeling with generalized regression neural networks, Advances in Engineering Software 38. pp. 71–79.
Chanas, S., Delgado, M., Verdegay, J.L., Vila, M.A., (1993), Interval and fuzzy extensions of classical transportation problems. Transportation Planning and Technology 17. pp. 203-218.
Chang, Y.-H., Shyu, T.-H., (1993), Traffic signal installation by the expert system using fuzzy set theory for inexact reasoning. Transportation Planning and Technology 17. pp. 191-202.
Chen H, Muller SG., (2001), Use of sequential learning for short-term traffic flow forecasting. Trans Res Part C Emerg Technol; 9(5). pp. 319–36.
Chen, L., May, A., Auslander, D., (1990), Freeway ramp control using fuzzy set theory for inexact reasoning. Transportation Research 24A. pp. 15-25.
Deb, S.K., (1993), Fuzzy set approach in mass transit mode choice. In: Ayyub, B.M. (Ed.), Proceedings of ISUMA '93, Second International Symposium on Uncertainty Modeling and Analysis. IEEE Computer Press, College Park, Maryland, pp. 262-268.
Dougherty MS. (1995), A review of neural networks applied to transport. Transp Res Part C Emerg Technol 1995;3(4). pp. 247–60.
Golob F T., (2000), A simultaneous model of household activity participation and trip chain generation. In: Transportation Research Record, Journal of the Transportation Research Board, No. 34, TRB. Washington, USA. pp. 355-376.
Jang, J.-S. R. and C.-T. (1997), Sun, Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence, Prentice Hall.
Lin CT, Chiu H, Chu PY, (2006). Agility index in supply chain. Int. J. Prod. Econ. 100. pp. 285-299.
Lotan, T., Koutsopoulos, H., (1993). Route choice in the presence of information using concepts from fuzzy control and approximate reasoning. Transportation Planning and Technology 17. pp. 113-126.
Lotan, T., Koutsopoulos, H., (1993), Models for route choice behaviour in the presence of information using concepts from fuzzy set theory and approximate reasoning. Transportation 20. pp. 129-155.
Makvandi, p., Alavi, S., H., Hajiha, A., (2006), An Exploration of Experts' Subjective Patterns in Behavioral Based Job Qualification Using Choquet Integral, Proceedings of the 6th WSEAS Int. Conf. on Systems Theory & Scientific Computation, Elounda, Greece, August 21-23. pp. 14-18.
Mamdani, E.H. and S. Assilian, (1975), An experiment in linguistic synthesis with a fuzzy logic controller," International Journal of Man-Machine Studies, Vol. 7, No. 1, pp. 1-13.
Messai N, Thomas P, Lefebvre D, El Moudni A., (2002), Optimal neural networks architectures for the flow-density relationships of traffic models. Math. Comput Simul 2002; 60(3–5). pp. 401–9.
Milosavljevic, N., Teodorovic, D., Papic, V., Pavkovic, G., (1996). A fuzzy approach to the vehicle assignment problem. Transportation Planning and Technology 20, pp. 33-47.
Murat, H., Celik, (2010), Sample size needed for calibrating trip distribution and behavior of the gravity model, Journal of Transport Geography 18. pp. 83–190
Nanda, R., Kikuchi, S., (1993), Estimation of trip O-D matrix when input and output are fuzzy. In: Ayyub, B.M. (Ed.), Proceedings of ISUMA '93, Second International Symposium on Uncertainty Modeling and Analysis. IEEE Computer Press, College Park, Maryland, pp. 104-111.
Novák, V., Perfilieva, I. and Močkoř, J. (1999), Mathematical principles of fuzzy logic Dodrecht: Kluwer Academic. ISBN 0-7923-8595-0
Ortuzar, J.D., Willumsen, L.G., (2001), the Traffic Assignment Problem: Models and Methods. VSP, Wiley, New York. Utrecht, the Netherlands.
Ross TJ, (2004), Fuzzy Logic with Engineering Applications. Second Edition. John Wiley and Sons Ltd. The Atrium, Southern Gate, Chichester, West Sussex PO19 8SQ, England.
Ruiter, E.R., Ben-Akiva, M.E., (1978), Disaggregate travel demand models for the San Francisco area: system structure, component models and application procedures. Transportation Research Record 673. pp. 121–128.
Smiller J, Hoel L A., (2006), assessing the utility of private information in transportation planning studies: A case study of trip generation analysis. Journal of Socio-Economic Planning Sciences, 40(3). pp. 94-118.
Sugeno, M., (1985), Industrial applications of fuzzy control, Elsevier Science Pub. Co.
Susilo, Y.O., Kitamura, R., (2007), Structural changes in commuters’ daily travel: the case of auto and transit commuters in the Osaka metropolitan area of Japan, 1980–2000. Transportation Research Part A: Policy and Practice 42. pp. 95–115.
Teodorovic D., (1999), Fuzzy logic systems for transportation engineering: the state of the art, Transportation Research Part A 33. pp. 337-364
Teodorovic, D., Babic, O., (1993), Fuzzy inference approach to the flow management problem in air traffic control. Transportation Planning and Technology 17. pp. 165-178.
Teodorovic, D., Kalic, M., (1995), A fuzzy route choice model for air transportation networks. Transportation Planning and Technology 19. pp. 109-119.
Vukadinovic, K., Teodorovic, D., (1994), A fuzzy approach to the vessel dispatching problem. European Journal of Operational Research 76. pp. 155-164.
Xu, W., Chan, Y., (1993), estimating an origin-destination matrix with fuzzy weights. Part 1: Methodology. Transportation Planning and Technology 17.pp. 127-144.
Yao Liya, GUAN Hongzhi, YAN Hai, (2008), Trip Generation Model Based on Destination Attractiveness, TSINGHUA SCIENCE and Technology,Volume 13,Number 5,pp632-635
Yun SY, Namkoong S, Rho JH, Shin SW, Choi JU., (1998), a performance evaluation of neural network models in traffic volume forecasting. Math Comput Model. 27(9–11). pp. 293–310.
Zhang H, Ritchie SG, Lo ZP., (1997), Macroscopic modeling of freeway traffic using an artificial neural network. Transp Res Record 1997; 1588. pp. 110–9.
