Advertisement

KSCE Journal of Civil Engineering

, Volume 23, Issue 9, pp 4124–4131 | Cite as

An Optimal Model based on Multifactors for Container Throughput Forecasting

  • Shuang Tang
  • Sudong XuEmail author
  • Jianwen Gao
Transportation Engineering
  • 24 Downloads

Abstract

Containerization plays an important role in international trade. Container throughput is a key indicator to measure the development level of a port. In this paper, Lianyungang Port and Shanghai Port are chosen to study the method for container throughput forecasting. Gray model, triple exponential smoothing model, multiple linear regression model, and backpropagation neural network model are established. Five factors are selected as influential factors. They are total retail sales of consumer goods, gross domestic product of the local city, import and export trade volume, total output value of the second industry and total fixed assets investment. The growth and the raw datasets are used in the prediction, respectively. The datasets from 1990 to 2011 are chosen to build models and the ones from 2012 to 2017 are used to assess the performance of the models. By comparison, the backpropagation neural network model is applicable to both Shanghai Port and Lianyungang Port for container throughput forecasting. The volume of container throughput at both ports from 2018 to 2020 is predicted.

Keywords

container throughput forecast influential factors neural network Shanghai Port Lianyungang Port 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Notes

Acknowledgements

This study was supported by the Transportation Technology and Achievement Transformation Project of Jiangsu Province (No. 2017T29).

References

  1. Adamowski, J., Chan, H. F., Prasher, S.O., Ozga-Zielinski, B., and Sliusarieva, A. (2012). “Comparison of multiple linear and nonlinear regression, autoregressive integrated moving average, artificial neural network, and wavelet artificial neural network methods for urban water demand forecasting in Montreal, Canada.” Water Resources Research, Vol. 48, DOI: 10.1029/2010wr009945.Google Scholar
  2. Chou, C. C., Chu, C. W., and Liang, G. S. (2008). “A modified regression model for forecasting the volumes of Taiwan's import containers.” Mathematical and Computer Modelling, Vol. 47, Nos. 9–10, pp. 797–807, DOI: 10.1016/j.mcm.2007.05.005.CrossRefGoogle Scholar
  3. Cui, W., Huang, L., and Wang, Y. (2015). “Port throughput influence factors based on neighborhood rough sets: An exploratory study.” Journal of Industrial Engineering and Management, Vol. 8, No. 5, pp. 1396–1408, DOI: 10.3926/jiem.1483.CrossRefGoogle Scholar
  4. Duan, X., Yu, S., and Xu, G. (2012). “Application of attribute theory for container throughput forecast.” Proc. of 2012 IEEE Int. Conf. on Granular Comput., IEEE, Hangzhou, China, pp. 102–107.CrossRefGoogle Scholar
  5. Gao, D. and Wu, S. (1998). “An optimization method for the topological structures of feed-forward multi-layer neural networks.” Pattern Recognition, Vol. 31, No. 9, pp. 1337–1342.CrossRefGoogle Scholar
  6. Geng, J., Li, M., Dong, Z., and Liao, Y. (2015). “Port throughput forecasting by MARS-RSVR with chaotic simulated annealing particle swarm optimization algorithm.” Neurocomputing, Vol. 147, pp. 239–250, DOI: 10.1016/j.neucom.2014.06.070.CrossRefGoogle Scholar
  7. Gosasang, V., Chandraprakaikul, W., and Kiattisin, S. (2011). “A comparison of traditional and neural networks forecasting techniques for container throughput at Bangkok Port.” The Asian Journal of Shipping and Logistics, Vol. 27, No. 3, pp. 463–482, DOI: 10.1016/S2092-5212(11)80022-2.CrossRefGoogle Scholar
  8. Guo, Y., Li, Z., Wu, Y., and Xu, C. (2018). “Evaluating factors affecting electric bike users' registration of license plate in China using Bayesian approach.” Transportation Research Part F-Traffic Psychology and Behaviour, Vol. 59, pp. 212–221, DOI: 10.1016/j.trf.2018.09.008.CrossRefGoogle Scholar
  9. Hou, J., Chen, Y., and Li, T. (2014). “The forecast of port cargo throughput based on combination forecasting model.” Proc. of 7 th Int. Symp. Comput. Intell. Des., ISCID, IEEE, Hangzhou, China, Vol. 1, pp. 585–588.Google Scholar
  10. Huang, A., Lai, K. K., Qiao, H., Wang, S., and Zhang, Z. (2018). “Does interval knowledge sharpen forecasting models? Evidence from China's typical ports.” International Journal of Information Technology & Decision Making, Vol. 17, No. 2, pp. 467–483, DOI: 10.1142/s0219622017500456.CrossRefGoogle Scholar
  11. Huang, A., Qiao, H., and Wang, S. (2014). “Forecasting container throughputs with domain knowledge.” Procedia Comput. Sci., Elsevier, Moscow, Russia, Vol. 31, No. 2014, pp. 648–655.CrossRefGoogle Scholar
  12. Huang, W. and Xu, S. (2009). “Neural network and harmonic analysis for recovering missing extreme water-level data during hurricanes in Florida.” Journal of Coastal Research, Vol. 25, No. 2, pp. 417–426, DOI: 10.2112/07-0863.1.CrossRefGoogle Scholar
  13. Intihar, M., Kramberger, T., and Dragan, D. (2017). “Container throughput forecasting using dynamic factor analysis and ARIMAX model.” Promet-Traffic & Transportation, Vol. 29, No. 5, pp. 529–542, DOI: 10.7307/ptt.v29i5.2334.CrossRefGoogle Scholar
  14. Lam, W. H. K., Ng, P. L. P., Seabrooke, W., and Hu, E. C. M. (2004). “Forecasts and reliability analysis of port cargo throughput in Hong Kong.” Journal of Urban Planning and Development, Vol. 130, No. 3, pp. 133–144, DOI: 10.1061/(ASCE)0733-9488(2004)130:3(133).CrossRefGoogle Scholar
  15. Li, X. and Xu, S. (2011). “A study on port container throughput prediction based on optimal combined forecasting model in Shanghai Port.” Proc. of 11 th Int. Conf. Chin. Transp. Prof., ASCE, Nanjing, China, No. 2011, pp. 3894–3905.CrossRefGoogle Scholar
  16. Lili, and Zhao, Q. (2009). “Application of grey model in forecasting the port of Qinhuangdao's throughput.” Proc. of 2009 IITA Int. Conf. on Serv. Sci., Manage. Eng., SSME IEEE, Zhangjiajie, China, pp. 57–60.Google Scholar
  17. Lin, L. (2013). “Forecast of container throughput for Lianyungang Harbor.” Proc. of 4 th Int. Conf. Transp. Eng., ASCE, Chengdu, China, No. 2013, pp. 594–599.Google Scholar
  18. Mo, L., Xie, L., Jiang, X., Teng, G., Xu, L., and Xiao, J. (2018). “GMDH-based hybrid model for container throughput forecasting: Selective combination forecasting in nonlinear subseries.” Applied Soft Computing, Vol. 62, pp. 478–490, DOI: 10.1016/j.asoc.2017.10.033.CrossRefGoogle Scholar
  19. Nielsen, R. H. (1988). “Theory of the backpropagation neural network.” Neural Networks, Vol. 1, No. 1, pp. 445, DOI: 10.1016/0893-6080(88)90469-8.CrossRefGoogle Scholar
  20. Niu, M., Hu, Y., Sun, S., and Liu, Y. (2018). “A novel hybrid decomposition-ensemble model based on VMD and HGWO for container throughput forecasting.” Applied Mathematical Modelling, Vol. 57, pp. 163–178, DOI: 10.1016/j.apm.2018.01.014.MathSciNetCrossRefGoogle Scholar
  21. Pao, H. T. (2009). “Forecasting energy consumption in Taiwan using hybrid nonlinear models.” Energy, Vol. 34, No. 10, pp. 1438–1446, DOI: 10.1016/j.energy.2009.04.026.CrossRefGoogle Scholar
  22. Peng, W. and Chu, C. (2009). “A comparison of univariate methods for forecasting container throughput volumes.” Mathematical and Computer Modelling, Vol. 50, Nos. 7–8, pp. 1045–1057, DOI: 10.1016/j.mcm.2009.05.027.CrossRefGoogle Scholar
  23. Schulze, P. M. and Prinz, A. (2009). “Forecasting container transshipment in Germany.” Applied Economics, Vol. 41, No. 22, pp. 2809–2815, DOI: 10.1080/00036840802260932.CrossRefGoogle Scholar
  24. Shi, Z. and Li, K. (2008). “Container throughput forecasting based on gray method and exponential smoothing method.” Journal of Chongqing Jiaotong University, No. 2, pp. 302–304+332.Google Scholar
  25. Sudheer, G. and Suseelatha, A. (2015). “Short term load forecasting using wavelet transform combined with Holt-Winters and weighted nearest neighbor models.” International Journal of Electrical Power & Energy Systems, Vol. 64, pp. 340–346, DOI: 10.1016/j.ijepes.2014.07.043.CrossRefGoogle Scholar
  26. Tsai, F.-M. and Huang, L. J. W. (2017). “Using artificial neural networks to predict container flows between the major ports of Asia.” International Journal of Production Research, Vol. 55, No. 17, pp. 5001–5010, DOI: 10.1080/00207543.2015.1112046.CrossRefGoogle Scholar
  27. Twrdy, E. and Batista, M. (2016). “Modeling of container throughput in Northern Adriatic ports over the period 1990–2013.” Journal of Transport Geography, Vol. 52, pp. 131–142, DOI: 10.1016/j.jtrangeo.2016.03.005.CrossRefGoogle Scholar
  28. Vasiliauskas, A. V. and Barysienė, J. (2008). “An economic evaluation model of the logistic system based on container transportation.” Transport, Vol. 23, No. 4, pp. 311–315, DOI: 10.3846/1648-4142. 2008.23.311-315.CrossRefGoogle Scholar
  29. Xie, G., Wang, S., Zhao, Y., and Lai, K.K. (2013). “Hybrid approaches based on LSSVR model for container throughput forecasting: A comparative study.” Applied Soft Computing, Vol. 13, No. 5, pp. 2232–2241, DOI: 10.1016/j.asoc.2013.02.002.CrossRefGoogle Scholar
  30. Xie, G., Zhang, N., and Wang, S. (2017). “Data characteristic analysis and model selection for container throughput forecasting within a decomposition-ensemble methodology.” Transportation Research Part E-Logistics and Transportation Review, Vol. 108, pp. 160–178, DOI: 10.1016/j.tre.2017.08.015.CrossRefGoogle Scholar
  31. Xiong, W., Liu, L., and Xiong, M. (2010). “Application of gray correlation analysis for cleaner production.” Clean Technologies and Environmental Policy, Vol. 12, No. 4, pp. 401–405, DOI: 10.1007/s10098-009-0214-7.MathSciNetCrossRefGoogle Scholar
  32. Yuan, X. (2011). “Based on factor analysis of influencing factors of port throughput.” Proc. of SPIE Int. Soc. for Opt. Eng., SPIE, Guangzhou, China, Vol. 8205, No. 2011.Google Scholar
  33. Zha, X., Chai, Y., Witlox, F., and Ma, L. (2016). “Container throughput time series forecasting using a hybrid approach.” Lect. Notes in Electr. Eng., Springer, Yangzhou, China Vol. 359, No. 2016, pp. 639–650.CrossRefGoogle Scholar
  34. Zhu, Q. and Peng, X. (2012). “The impacts of population change on carbon emissions in China during 1978–2008.” Environmental Impact Assessment Review, Vol. 36, pp. 1–8, DOI: 10.1016/j.eiar.2012.03.003.CrossRefGoogle Scholar

Copyright information

© Korean Society of Civil Engineers 2019

Authors and Affiliations

  1. 1.School of TransportationSoutheast UniversityNanjingChina

Personalised recommendations