An Optimal Model based on Multifactors for Container Throughput Forecasting
- 35 Downloads
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.
Keywordscontainer throughput forecast influential factors neural network Shanghai Port Lianyungang Port
Unable to display preview. Download preview PDF.
This study was supported by the Transportation Technology and Achievement Transformation Project of Jiangsu Province (No. 2017T29).
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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