Analysis and forecasting of port logistics using TEI@I methodology
Document Type
Journal article
Source Publication
Transportation Planning and Technology
Publication Date
12-1-2013
Volume
36
Issue
8
First Page
685
Last Page
702
Keywords
artificial neural network, container throughput, econometric models, forecasting, port logistics, TEI@I methodology
Abstract
This paper presents an integrated forecasting model based on the TEI@I methodology for forecasting demand for port logistics services - specifically, port container throughput. The model analyzes port logistics time series data and other information in several steps. In the first step, several econometric models are built to forecast the linear segment of port logistics time series. In the second step, a radial basis function neural network is developed to predict the nonlinear segment of the time series. In the third step, the event-study method and expert system techniques are applied to evaluate the effects of economic and other events that may impact demand for port logistics. In the final step, synthetic forecasting results are obtained, based on the integration of predictions from the above three steps. For an illustration, Hong Kong port's container throughput series is used as a case study. The empirical results show the effectiveness of the TEI@I integrated model for port logistics forecasting.
DOI
10.1080/03081060.2013.851506
Print ISSN
03081060
E-ISSN
10290354
Publisher Statement
Copyright © 2013 Taylor & Francis
Access to external full text or publisher's version may require subscription.
Full-text Version
Publisher’s Version
Language
English
Recommended Citation
Tian, X., Liu, L., Lai, K. K., & Wang, S. (2013). Analysis and forecasting of port logistics using TEI@I methodology. Transportation Planning and Technology, 36(8), 685-702. doi: 10.1080/03081060.2013.851506