Passenger Flows Estimation of Light Rail Transit (LRT) System in Izmir, Turkey Using Multiple Regression and ANN Methods

  • Mustafa Özuysal
  • Gökmen Tayfur
  • Serhan Tanyel
Keywords: light rail transit, multiple regression, artificial neural networks, public transportation

Abstract

Passenger flow estimation of transit systems is essential for new decisions about additional facilities and feeder lines. For increasing the efficiency of an existing transit line, stations which are insufficient for trip production and attraction should be examined first. Such investigation supports decisions for feeder line projects which may seem necessary or futile according to the findings. In this study, passenger flow of a light rail transit (LRT) system in Izmir, Turkey is estimated by using multiple regression and feed-forward back-propagation type of artificial neural networks (ANN). The number of alighting passengers at each station is estimated as a function of boarding passengers from other stations. It is found that ANN approach produced significantly better estimations specifically for the low passenger attractive stations. In addition, ANN is found to be more capable for the determination of trip-attractive parts of LRT lines.

 

Keywords: light rail transit, multiple regression, artificial neural networks, public transportation

References

Gercek, H., Karpak B., Kilincaslan, T.A.: Multiple Criteria Approach for the Evaluation of the Rail Transit Networks in Istanbul, Transportation, Vol. 31, No. 2, 2004, pp. 203-28

Li, J.P.: Train Station Passenger Flow Study, Proceedings of the 2000 Winter Simulation Conference, eds.: J. A. Joines, R. R. Barton, K. Kang, and P. A. Fishwick, Orlando, Florida, U.S.A., December 2000, pp. 1173-1176

Harris, N.G., Anderson, N.J.: An International Comparison of Urban Rail Boarding and Alighting Rates, Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail & Rapid Transit, Vol. 221, No. 4, 2007, pp. 521-526

Takagi, R., Goodman, C., Roberts, C.: Optimization of Train Departure Times at an Interchange Considering Passenger Flows, Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail & Rapid Transit, Vol. 220, No. 2, 2006, pp. 113-120

Lee, K., Jung, W.S., Park, J.S., Choi, M.Y.: Statistical Analysis of the Metropolitan Seoul Subway System: Network Structure and Passenger Flow, Physica A, Vol. 387, No. 24, 2008, pp. 6231-6234

Celikoglu, H.B., Cigizoglu, H.K.: Public Transportation Trip Flow Modeling with Generalized Regression Neural Networks, Advances in Engineering Software, Vol. 38, 2007, pp. 71-79

Celikoglu, H.B., Cigizoglu, H.K.: Modelling Public Transport Trips by Radial Basis Function Neural Networks,Mathematical and Computer Modelling, Vol. 45, 2007, pp. 480-489.

Ham, F.M., Kostanic, I.: Principles of Neurocomputing for Science and Engineering, McGraw Hill, New York, 2001

Jang, J.R., Sun, C.T., Mizutani, E.: Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence, Prentice Hall, Upper Saddle River NJ, 1997

Murat, Y.S., Ceylan, H.: Use of Artificial Neural Networks for Transport Energy Demand Modeling, Energy Policy, Vol. 34, No. 17, 2006, pp. 3165-3172

Zhang, X., Jin, X., Qi, W., Guo, Y.: Vehicle Crash Accident Reconstruction Based on the Analysis 3D Deformation of the Auto-Body, Advances in Engineering Software, Vol. 39, 2008, pp. 459-465

Murat, Y.S.: Comparison of Fuzzy Logic and Artificial Neural Networks Approaches in Vehicle Delay Modeling. Transportation Research Part C, Vol. 14, No. 5, 2006, pp. 316-334

Tayfur, G., Swiatek, D., Wita, A., Singh, V.P.: Case Study: Finite Element Method and Artificial Neural Network Models for Flow Through Jeziorsko Earthfill Dam in Poland, ASCE Journal of Hydraulic Engineering, Vol. 131, No. 6, 2005, pp. 431-440

Tayfur, G., Moramarco, T., Singh, V.P.: Predicting and Forecasting Flow Discharge at Sites Receiving Significant Lateral Inflow, Hydrological Processes, Vol. 21, No. 14, 2007, pp. 1848-1859

Tayfur, G.: Soft Computing Approaches in Hydrology. In: Hydrology and Hydraulics, Ed.: V. P. Singh, Water Resources Publications, Colorado, 2008, pp. 113-144

How to Cite
1.
Özuysal M, Tayfur G, Tanyel S. Passenger Flows Estimation of Light Rail Transit (LRT) System in Izmir, Turkey Using Multiple Regression and ANN Methods. Promet - Traffic&Transportation. 1;24(1):1-14. DOI: 10.7307/ptt.v24i1.264
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Articles