Investigation of the performance of rail freight: a regression model with panel data
Keywords:Rail freight; Regression analysis; Panel data.
The objective of this paper is to investigate the factors that affect the performance of rail freight and to ascertain the magnitude of the resulting effects. A multiple linear regression model was developed with panel data analysis, considering the fixed effects over time. The data collected correspond to the period from 2011 to 2018 and come from the National Land Transportation Agency (ANTT). The explanatory variables used are: speed, maintenance, accidents and cargo volume (production). After formulating and executing the model, the obtained indices were tested for statistical significance. We attempted to attenuate heteroscedastic errors by calculating robust standard errors and performed a model specification test. It was detected that the volume of cargo transported and speed of the train have a statistically relevant impact on performance. The model developed showed no evidence of poor specification and can assist in the planning of the activities of the observed companies.
Associação Nacional do Transportadores Ferroviários – ANTF (2019). Informações gerais. Retirado em 24 junho, de www.antf.org.br/informacoes-gerais/.
Agência Nacional de Transportes Terrestres – ANTT (2019). Anuário estatístico. Retirado em 29 de junho, de www.antt.gov.br/ferrovias/arquivos/Anuario_Estatistico.html.
Austin, R. D., & Carson, J. L. (2002). An alternative accident prediction model for highway-rail interfaces. Accident Analysis & Prevention, 34(1), 31-42.
Azambuja, A. M. V. (1995). Estimação de modelos comportamentais utilizando a técnica de preferência declarada: o caso de variabilidade dos tempos de viagem no transporte de grãos no Rio Grande do Sul. 1995. Dissertação (Programa de Pós-Graduação em – PPGEP) – Escola de engenharia, Universidade Federal do Rio Grande do Sul, Porto Alegre.
Benishay, H., & Whitaker, G. R. (1966). Demand and supply in freight transportation. The Journal of Industrial Economics, 243-262.
da Silva, G. J. C., Jayme Jr, F. G., & Martins, R. S. (2009). Gasto público com infraestrutura de transporte e crescimento: uma análise para os estados brasileiros (1986-2003) 1. Revista Economia & Tecnologia, 5(1).
Fernandes, R. (2011). A procura de transporte ferroviário de mercadorias na Europa. 2011. Dissertação (Mestrado em economia) – Faculdade de economia, Universidade de Coimbra, Coimbra.
Fleury, M. T. L., & da Costa Werlang, S. R. (2017). Pesquisa aplicada: conceitos e abordagens. Anuário de Pesquisa GVPesquisa.
FitzRoy, F., & Smith, I. (1995). The demand for rail transport in European countries. Transport Policy, 2(3), 153-158.
Gerhardt, T. E., & Silveira, D. T. (2009). Métodos de pesquisa. [e-book]. Porto Alegre, Editora da UFRGS. Retirado em 10 de agosto, de www.ufrgs.br/cursopgdr/downloadsSerie/derad005.pdf.
González, R. M., Marrero, G. A., Rodríguez-López, J., & Marrero, Á. S. (2019). Analyzing CO2 emissions from passenger cars in Europe: A dynamic panel data approach. Energy policy, 129, 1271-1281.
Gu, S., & Lu, X. (2015, July). Analysis of China railway passenger volume's influence factors based on principal component regression. In 2015 International Conference on Logistics, Informatics and Service Sciences (LISS) (pp. 1-5). IEEE.
He, Z. G., Shuai, B., & Liao, W. (2004). Freight Demand Forecasting for Logistics Centers [J]. Logistics Technology, 1.
Jiang, H. Y., & Yang, D. M. (2002). The forecast methods of volume of water freight for comparision. Oper. Res. Manag. Sci, 11(3), 74-79.
Kecman, P., & Goverde, R. M. (2015). Predictive modelling of running and dwell times in railway traffic. Public Transport, 7(3), 295-319.
Lončar, D., Paunković, J., Jovanović, V., & Krstić, V. (2019). Environmental and social responsibility of companies cross EU countries–Panel data analysis. Science of The Total Environment, 657, 287-296.
Mantalos, P., & Shukur, G. (2007). The robustness of the reset test to non-normal error terms. Computational Economics, 30(4), 393-408.
Nuzzolo, A., Coppola, P., & Comi, A. (2013). Freight transport modeling: review and future challenges. International Journal of Transport Economics/Rivista internazionale di economia dei trasporti, 151-181.
McCollister, G. M., & Pflaum, C. C. (2007). A model to predict the probability of highway rail crossing accidents. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of rail and rapid transit, 221(3), 321-329.
Pereira, O. C. (2009). Soluções de otimização da eficiência energética de uma ferrovia de carga. Dissertação (Mestrado em Engenharia Industrial) – Pontifícia Universidade Católica do Rio de Janeiro, Rio de Janeiro, 2009.
Pereira, A.S. et al. (2018). Metodologia da pesquisa científica. [e-book]. Santa Maria/RS, Ed. UAB/NTE/UFSM. Retirado em 6 de agosto, de repositorio.ufsm.br/bitstream/handle/1/15824/Lic_Computacao_Metodologia-Pesquisa-Cientifica.pdf?sequence=1.
Ramalho, E. A., & Ramalho, J. J. (2012). Alternative versions of the RESET test for binary response index models: a comparative study. Oxford bulletin of economics and statistics, 74(1), 107-130.
Reiter, G. R. (2015). Infraestrutura de transportes no Brasil: uma análise com dados em painel no período de 1995 a 2008. 2015. Trabalho de conclusão (Ciências Econômicas), Universidade Federal do Rio Grande do Sul, Porto Alegre.
Smith, P. L. (1974). Forecasting freight transport demand–The State of the Art. Logistics and Transportation Review, 10(4), 311-326.
Wang, X., Tong, D., Gao, J., & Chen, Y. (2019). The reshaping of land development density through rail transit: The stories of central areas vs. suburbs in Shenzhen, China. Cities, 89, 35-45.
Wen, C., Lessan, J., Fu, L., Huang, P., & Jiang, C. (2017, August). Data-driven models for predicting delay recovery in high-speed rail. In 2017 4th International Conference on Transportation Information and Safety (ICTIS) (pp. 144-151). IEEE.
Yang, Y. (2015). Development of the regional freight transportation demand prediction models based on the regression analysis methods. Neurocomputing, 158, 42-47.
Zhao, C., Liu, K., & LI, D. S. (2004). Research on application of the support vector machine in freight volume forecast. Journal of the China Railway society, 26(4), 10-14.
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