RAIL TRAFFIC VOLUME ESTIMATION BASED ON WORLD DEVELOPMENT INDICATORS

Luka Lazarević, Miloš Kovačević, Zdenka Popović

DOI Number
-
First page
133
Last page
141

Abstract


European transport policy, defined in the White Paper, supports shift from road to rail and waterborne transport. The hypothesis of the paper is that changes in the economic environment influence rail traffic volume. Therefore, a model for prediction of rail traffic volume applied in different economic contexts could be a valuable tool for the transport planners. The model was built using common Machine Learning techniques that learn from the past experience. In the model preparation, world development indicators defined by the World Bank were used as input parameters.

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References


Abdulhai B., Porwal H., Recker W., 2002, Short-Term Traffic Flow Prediction Using Neuro-Genetic Algorithms, Journal of Intelligent Transportation Systems, 7(1), pp. 3-41.

Celikoglua H.B., Cigizoglu H.K., 2007, Public transportation trip flow modeling with generalized regression neural networks, Advances in Engineering Software, 38(2), pp. 71–79.

Çetiner B.G., Sari M., Borat O., 2010, A neural network based traffic-flow prediction model, Mathematical and Computational Applications, 15(2), pp. 269-278.

Guo F., Krishnan R., Polak J., 2013, A computationally efficient two-stage method for short-term traffic prediction on urban roads, Transportation Planning and Technology, 36(1), pp. 62-75.

Griskeviciene D., Griskevicius A., Griskeviciute-Geciene A., 2010, Providences and projections regarding the prognostication of railway transport volumes from a long-term perspective, Proc. Tenth International Conference “Reliability and Statistics in Transportation and Communication”, Riga, Latvia, pp. 25-33.

Gao S., Zhang Z., Cao C., 2011, Road Traffic Freight Volume Forecast Using Support Vector Machine Combining Forecasting, Journal of Software, 6(9), pp. 1680-1687.

Li Z., Zhang Q., Wang L., 2011, Flow prediction research of urban rail transit based on support vector machine, Proc. First International Conference on Transportation Information and Safety (ICTIS), Wuhan, China, pp. 2276-2282.

Fang H., Yaqiang S., Siyu T., 2007, The application of combined forecast method in predicting freight volume of railway, Proc. First International Conference on Transportation Engineering, Southwest Jiaotong University, Chengdu, China, pp. 3347-3352.

Zhuo W., Li-Min J., Yong Q., Yan-Hui W., 2007, Railway passenger traffic volume prediction based on neural network, Applied Artificial Intelligence, 21(1) , pp. 1-10.

Qi F., Liu X., Ma Y., 2009, Prediction of Railway Passenger Traffic Volume Based on Neural Tree Model, Proc. Second International Conference on Intelligent Computation Technology and Automation, Washington, USA, pp. 370-373.

Celikoglua H.B., Cigizoglu H.K., 2007, Modelling public transport trips by radial basis function neural networks, Mathematical and Computer Modelling, 45(3-4), pp. 480-489.

Özuysal M., Tayfur G., Tanyel S., 2012, Passenger flows estimation of light rail transit (LRT) system in Izmir, Turkey using multiple regression and ANN methods, Promet - Traffic&Transportation, 24(1), pp. 1-14.

Karlaftis M.G., Vlahogianni E.I., 2011, Statistical methods versus neural networks in transportation research: Differences, similarities and some insights, Transportation Research Part C: Emerging Technologies, 19(3), pp. 387-399.

http://data.worldbank.org/ (Accessed on December 26, 2015)

Popović Z., Lazarević L., Ižvolt L., 2013, Potential of the railway infrastructure in Serbia, Railway transport and logistics, 3, pp. 9-22.

Hall M., Frank E., Holmes G., Pfahringer B., Reutemann M., Witten I.H., 2009, The WEKA Data Mining Software: An Update, SIGKDD Explorations, 1(11), pp. 10-18.

http://www.cs.waikato.ac.nz/ml/weka/ (Accessed on December 26, 2015)

Mitchel T., 1997, Machine Learning, McGraw Hill, Columbus, Ohio, 414 p.

Quinlan J.R., 1986, Induction of Decision Trees, Machine Learning, 1(1), pp. 81-106.

Haykin S., 1998, Neural Networks: A Comprehensive Foundation, Prentice Hall, New Jersey, 842 p.


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ISSN: 0354-2025 (Print)

ISSN: 2335-0164 (Online)

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