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Discrete Dynamics in Nature and Society
Volume 2013, Article ID 802528, 10 pages
Research Article

Modeling of a Small Transportation Company’s Start-Up with Limited Data during Economic Recession

1School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China
2Business School, University of Edinburgh, Edinburgh EH8 9JS, UK

Received 9 October 2013; Revised 11 November 2013; Accepted 25 November 2013

Academic Editor: Wuhong Wang

Copyright © 2013 Xiaoping Fang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This paper presents a modeling method for analyzing a small transportation company’s start-up and growth during a global economic crisis which had an impact on China which is designed to help the owners make better investment and operating decisions with limited data. Since there is limited data, simple regression model and binary regression model failed to generate satisfactory results, so an additive periodic time series model was built to forecast business orders and income. Since the transportation market is segmented by business type and transportation distance, a polynomial model and logistic curve model were constructed to forecast the growth trend of each segmented transportation market, and the seasonal influence function was fitted by seasonal ratio method. Although both of the models produced satisfactory results and showed very nearly the same of goodness-of-fit in the sample, the logistic model presented better forecasting performance out of the sample therefore closer to the reality. Additionally, by checking the development trajectory of the case company’s business and the financial crisis in 2008, the modeling and analysis suggest that the sample company is affected by national macroeconomic factors such as GDP and import & export, and this effect comes with a time lag of one to two years.