OPTIMIZING URBAN TRANSPORTATION USING ENTROPY MODEL

Authors: 
Lebedeva O. A.
Gozbenko V. E.
Kargapoltsev S. K.
Receipt date: 
16.09.2019
Bibliographic description of the article: 

Lebedeva O. A., Gozbenko V. E., Kargapol’tsev S. V. Opredeleniye vneshnikh silovykh faktorov, deystvuyushchikh na bespilotnyy letatel'nyy apparat na kriticheskikh rezhimakh poleta [Optimizing urban transportation using entropy model]. Sovremennye tekhnologii. Sistemnyi analiz. Modelirovanie [Modern Technologies. System Analysis. Modeling], 2019. Vol. 64, No. 4. Pp. 131–137. DOI: 10.26731/1813-9108.2019.4(64). 131-137

Section: 
Transport
Year: 
2019
Journal number: 
№4(64)
УДК: 
656.2
DOI: 

10.26731/1813-9108.2019.4(64).131–137

Article File: 
PDF icon 131-137.pdf
Pages: 
131
137
Abstract: 

Trucks make long trips consisting of several tours that are not connected by logistic solutions. To predict the demand for urban transportation, it is necessary to develop and test alternative models, since not all options for transporting goods follow the traditional four-stage approach. The article presents two main applications of entropy in transport modeling, indicating the aspects and limitations that should be taken into account when developing the algorithm. Of all the methods for distributing traffic flows, the most likely ones will be those that make it possible to generate the greatest number of solutions, taking into account limitations. The limitations include the total number of vehicle rides at each node. The paper considers a model of entropy maximization based on the trip, designed to predict freight flows taking into account information about aggregate demand (the number of trips made or attracted to each node). It is tested on real data. The calculated results show the accuracy and efficiency of the approach. The estimated flows correspond to the observed values. It is equally important that the model parameters and the resulting distribution of the trip length were conceptually justified. The longer the trip, the less likely it is that the flows will be distributed on this trip, which is consistent with the aspect that routes with a minimum length are economically more profitable. As a result of applying the entropy model to forecast the demand for freight trips in urban areas, it is possible to find vehicle flows that correspond to real freight traffic patterns.

Keywords: 
transport network planning; freight transportation; the model of maximization of entropy; entropy; transport
List of references: 
  1. Lebedeva O.A. Transportnaya infrastruktura kak osnovopolagayushchii faktor effektivnogo funktsionirovaniya ekonomiki strany [Transport infrastructure as a fundamental factor in the effective functioning of the country's economy].  Sbornik nauchnykh trudov Angarskogo gosudarstvennogo tekhnicheskogo universiteta [Collection of research papers of the Angarsk State Technical University], 2018, pp. 125-130.
  2. Lebedeva O.A., Antonov D.V. Modelirovanie gruzovykh matrits korrespondentsii gravitatsionnym i entropiinym metodami [Modeling of freight correspondence matrices by gravity and entropy methods]. Vestnik Irkutskogo gosudarstvennogo tekhnicheskogo universiteta [Proceedings of Irkutsk State Technical University], 2015. No.5 (100), pp. 118-122.
  3. Kripak M.N., Lebedeva O.A. Modelirovanie gruzovykh perevozok v transportnoi seti [Modeling freight transportation in the transport network]. Vestnik Angarskogo gosudarstvennogo tekhnicheskogo universiteta [The bulletin of the Angarsk State Technical University], 2016. No. 10, pp. 182-184.
  4. Poltavskaya Yu.O. Teoreticheskie aspekty segmentatsii ulichno-dorozhnoi seti pri provedenii transportnykh obsledovanii [Theoretical aspects of the segmentation of the road network during transport surveys]. Vestnik Angarskogo gosudarstvennogo tekhnicheskogo universiteta [The bulletin of the Angarsk State Technical University], 2018. No. 12, pp. 199-201.
  5. Gozbenko V.E., Kripak M.N., Pashkova A.S., Ivankov A.N. Metody prognozirovaniya i optimizatsii transportnoi seti s uchetom moshchnosti passazhiro i gruzopotokov [Methods of forecasting and optimization of the transport network taking into account the capacity of passenger and cargo flows]. IrGUPS – Irkutsk, 2008, pp. 76. Dep. in VINITI. 15.04.2008, No.330-V2008.
  6. Kripak M. N. Optimizatsiya transportnogo obsluzhivaniya gruzovladel'tsev v predelakh krupnogo goroda (gorodskoi aglomeratsii). Avtoreferat [Optimization of transport services for cargo owners within a large city (urban agglomeration). Abstract]. Angarsk State Technical Academy Publ., Irkutsk. 2009.
  7. Holguín-Veras J., Thorson E. Modeling Commercial Vehicle Empty Trips with a First Order Trip Chain Model. Transportation Research, 2003, Part B, Vol. 37, pp. 129-148.
  8. Holguín-Veras J., Patil G.R. Observed Trip Chain Behavior of Commercial Vehicles, Transportation Research Record. Journal of the Transportation Research Board, 2005,  No. 1906, pp. 74-80.
  9. Comi A., Delle Site P., Filippi F., Nuzzolo A. Urban freight transport demand modelling: a state of the art. European Transport 51, paper 7, 2012.
  10. Russo F., Comi A, A modelling system to simulate goods movements at an urban scale. Transportation 37(6), 987-1009, 2010.
  11. Wilson A.G. The use of the concept of entropy in system modelling. Operational Research Quarterly 21(2), 247-265, 1970.
  12. Jaynes E.T. Information theory and statistical mechanics. Physical Review 106(4), 620-630, 1957.
  13. Van Zuylen H. J., Willumsen H.G. The most likely trip matrices estimated from traffic counts. Transportation Research B 14(3), 281-293, 1980.
  14. Fisk C.S. On combining maximum entropy trip matrix estimation with user optimal assignment. Transportation Research B 22(1), 69-79, 1988.
  15. Wang Q., Holguín-Veras J. Tour-based entropy maximization formulations of urban commercial vehicle movements. Proceedings of the Association for European Transport (AET) Conference 2008, Leeuwenhorst Conference Centre, The Netherlands, 6-8 October 2008.
  16. Fang S.-C., Rajasekera J.R., Tsao H.-S. J. Entropy Optimization and Mathematical Programming. Kluwer Academic Publishers, Boston, 1997.