An Integrated Approach towards Building a Simulation Model Supporting the Management of the Passenger Transportation System. Part 2 – Case Study

• Autorzy: Gąbka J. , Susz S. , Rosienkiewicz M.
• Języki publikacji: EN

Abstrakty

EN

This article presents a simulation model designated as an advising and forecasting tool for designing, redesigning and managing ground-based transportation systems. It considers both public and private transport means. It enables visualisation of the results of changes in the transportation network such as a new transportation mode, schedule adjustment, technology improvements on shuttle speed and other modifications that can influence the effectiveness of the transportation network. The simulation tool enables predictions of future passenger flow size for different means of transport. The simulation tool was developed after thorough analysis of interdependencies between variables in the transportation network model built upon an econometric model, artificial neural network and mathematical model. The simulation model was tested on the real data and determined to be very effective, useful and flexible in use. Successive phases of the model development proved that development of a reliable advising and forecasting tool requires a combination of different methods.

Słowa kluczowe

EN

decision supporting tool; transportation system; algorithm verification; simulation model

• Wydawca: Międzynarodowa Wyższa Szkoła Logistyki i Transportu we Wrocławiu
• Czasopismo: Logistics and Transport
• Rocznik: 2016
• Tom: Vol. 32, No. 4
• Strony: 51--64
• Opis fizyczny: Bibliogr. 11 poz., rys., tab.

Twórcy

autor

Gąbka J.

• Wroclaw University of Technology, Poland

autor

Susz S.

• Wroclaw University of Technology, Poland

autor

Rosienkiewicz M.

• Wroclaw University of Technology, Poland

Bibliografia

• [1] R. Balcombe (edit.), The demand for public transport: a practical guide, TRL Report TRL593, 2004.
• [2] D. Besanko, R. Braeutigam, Microeconomics 4th ed., Wiley, 2010.
• [3] A. Daly, Estimating Choice Models Containing Attraction Variables, Transportation Research, Part B: Methodological, Vol. 16, No. 1 (1982) pp. 5-15.
• [4] R. M. Deshabrata, K. R. Sankar, P. B. Mahendra, Multi-choice stochastic transportation problem involving extreme value distribution, Applied Mathematical Modelling, Volume 37, Issue 4 (2013) pp. 2230-2240.
• [5] J. Fessard, Transportation Generalized Cost Functions for Railroads and Inland Waterways. Massachusetts Institute of Technology, 1979.
• [6] B. Jourquin, L. Tavasszy, L. Duan, On the generalized cost - demand elasticity of intermodal container transport. European Journal of Transport and Infrastructure Research, Issue 14(4) (2014) pp. 362-374.
• [7] T. Nagatani, Nonlinear-map model for bus schedule in capacity-controlled transportation, Applied Mathematical Modelling, Volume 37, Issue 4, 2013, pp. 1823-1835.
• [8] C. V. Phani Kumar, D. Basu, B. Maitra, Modeling Generalized Cost of Travel for Rural Bus Users: A Case Study. Journal of Public Transportation, Vol. 7(No. 2) (2004) pp.59-67.
• [9] L. Tavasszy, I. Davydenko, K. Ruijgrok, The Extended Generalized Cost Concept and its application in Freight Transport and General Equilibrium Modeling, in: s.n. (ed.) Integration of Spatial Computable General Equilibrium and Transport Modelling, Tokyo, 2009.
• [10] M. West, Statistical Inference for Gravityels in Transportation Flow Forecasting, Technical Report Number 60: National Institute of Statistical Sciences, 1997.
• [11] A. Żurkowski, Modelowanie przewozów międzyaglomeracyjnych (eng. Interagglomeration Transport Modelling), Problemy kolejnictwa (eng. Railway Issues), 53(148) (2009) pp. 5-47.