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The Model of Correspondence of Passenger Transportation on the Basis of Fuzzy Logic

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Języki publikacji
EN
Abstrakty
EN
The article dwells upon possibilities of the implementation of smooth calculation methods for predicting the demand for passenger transportation. The developed model which is based on fuzzy logic successfully solves the problem with traffic assignment – formation of passenger throughput for each traffic route. The model of correspondence takes into account such defining factors as fare on the route, average headway on it and fullness of the vehicle saloon. Different combination of these factors forms attractiveness as a criterion of an optimal route for a prospective passenger. It is determined that the lower saloon fullness, transportation fare and headway, the higher attractiveness is. Using the reasoned criterion, it is possible to allocate the total number of prospective passengers according to each existing route.
Twórcy
  • Lviv Politechnic National University
autor
  • Lviv Politechnic National University
autor
  • Lviv Politechnic National University
  • demchuk_inna@ukr.net
Bibliografia
  • 1. Shvetsov V., 2010. Problems of Transportation Modeling in Traffic Networks. MFTI works. Vol. 4, 169- v179. (in Russian).
  • 2. Brailovskyi N., Hranovskyi B., 1978. Modeling of Traffic Systems. Moscow: Transport, 124. (in Russian).
  • 3. Horbachov P. 2009. New Conception o Modeling Needs of the Population in Commuting. Dnipropetrovsk national university Bulletin. Vol. 27, 210- 214. (in Ukrainian).
  • 4. Horbachov P., 2009. Modern Scientific Approaches to the Management of Passenger Transport of Fixed-Routes in Cities. Kharkiv, 196. (in Ukrainian).
  • 5. Zablotskyi H., 1968. Methods of Calculations of Passenger and Traffic Flows in Cities, Moskow, 92. (in Russian).
  • 6. Vdovychenko V., 2004. Functioning Efficiency of Urban Passenger Transportation System. Kharkov, 196. (in Russian).
  • 7. Liubyi Ye., Horbachov P., Havrylyshyna O. Siromolot A., 2011. Patterns of Allocation of Transportation Correspondences in Towns. SNU im. V. Dalia Bulletin. Vol. 5 (159), 89-94. (in Ukrainian)
  • 8. Pohrebniak E., Samoilenko N., 2006. Analysis of Methods of Forming the Matrix of Transportation Network Correspondences in a City. Kharkov National Academy of City Household. Vol. 69, 121-126. (in Ukrainian).
  • 9. Hetsovych Ye., Zasiadko D., 2010. Traffic Zoning of Megalopolises and Estimation of the Routes for Correspondence Realization. Minsk, 26-33. (in Russian).
  • 10. Norbert Oppenheim, 1995. Urban Travel Demand Modeling.John Wiley and Sons, 480.
  • 11. Ortuzar J. de D., Willumsen L., 2006. Modelling transport. Third edition. John Wiley & Sons Ltd., 499.
  • 12. Winston C., Small K., 1998. The Demand for Transportation: Models and Applications [Text]. C.: Univesity of California, 51.
  • 13. Drew D., 1972. Theory of Traffic Flows and Their Management M: Transport, 423. (in Russian).
  • 14. Loze D. (2006). Modelling of Traffic Supply and Demand for Passenger and Official Vehicles. SPb: SPb. Household Architect.-Build. University, 170-186. (in Russian).
  • 15. Horbachev P., Dmitriev I., 2012. Fundamentals of Traffic System Theory. Kharkov, 202. (in Russian).
  • 16. Ohay V., 1978. Modeli analiza passazhiropotokov na marshrutah gorodskogo transporta: avtoref. dis. [Models for the Analysis of Passenger Throughputs on Routes of the Public Transport], Tomsk,22. (in Russian).
  • 17. Bilous A., Demchuk I., 2014. Analysis Methods and Models of Calculation of Passenger Correspondence Analiz, Eastern-European Journal of Eenterprise Technologies. Vol. 3/3 (69), 53-57. (in Ukrainian).
  • 18. Krystopchuk M., 2014. Investigation of Impact Factors on the Allocation of Passenger Correspondences in the Route Network, Scientific notes. Vol.45, Lutsk, 317-322. (in Ukrainian).
  • 19. Rutkovskaya D., Pylynskyi L., Rutkovskyi L., 2006. Neural Networks, Genetic Algorithms and Fuzzy Systems, Moscow : Telecom, 382. (in Russian).
  • 20. Shtovba S., 2007. Designing of Fuzzy Systems with MATLAB, Moscow :Telecom, 288. (in Russian).
  • 21. Leonenkov A., 2003. Fuzzy Modeling in MATLAB and fuzzyTECH Envir Environment, Peterburg: Decision Master, 72. (in Russian).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-ca8f57f7-9a88-4241-ab8e-6334efbb36d2
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