Modeling of a Small Transportation Company’s Start-Up with Limited Data during Economic Recession
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, si...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2013-01-01
|
| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2013/802528 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850221219591225344 |
|---|---|
| author | Xiaoping Fang Jake Ansell Weiya Chen |
| author_facet | Xiaoping Fang Jake Ansell Weiya Chen |
| author_sort | Xiaoping Fang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-70b9bef721864fd8a45f6514d246d2f4 |
| institution | OA Journals |
| issn | 1026-0226 1607-887X |
| language | English |
| publishDate | 2013-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-70b9bef721864fd8a45f6514d246d2f42025-08-20T02:06:47ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2013-01-01201310.1155/2013/802528802528Modeling of a Small Transportation Company’s Start-Up with Limited Data during Economic RecessionXiaoping Fang0Jake Ansell1Weiya Chen2School of Traffic and Transportation Engineering, Central South University, Changsha 410075, ChinaBusiness School, University of Edinburgh, Edinburgh EH8 9JS, UKSchool of Traffic and Transportation Engineering, Central South University, Changsha 410075, ChinaThis 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.http://dx.doi.org/10.1155/2013/802528 |
| spellingShingle | Xiaoping Fang Jake Ansell Weiya Chen Modeling of a Small Transportation Company’s Start-Up with Limited Data during Economic Recession Discrete Dynamics in Nature and Society |
| title | Modeling of a Small Transportation Company’s Start-Up with Limited Data during Economic Recession |
| title_full | Modeling of a Small Transportation Company’s Start-Up with Limited Data during Economic Recession |
| title_fullStr | Modeling of a Small Transportation Company’s Start-Up with Limited Data during Economic Recession |
| title_full_unstemmed | Modeling of a Small Transportation Company’s Start-Up with Limited Data during Economic Recession |
| title_short | Modeling of a Small Transportation Company’s Start-Up with Limited Data during Economic Recession |
| title_sort | modeling of a small transportation company s start up with limited data during economic recession |
| url | http://dx.doi.org/10.1155/2013/802528 |
| work_keys_str_mv | AT xiaopingfang modelingofasmalltransportationcompanysstartupwithlimiteddataduringeconomicrecession AT jakeansell modelingofasmalltransportationcompanysstartupwithlimiteddataduringeconomicrecession AT weiyachen modelingofasmalltransportationcompanysstartupwithlimiteddataduringeconomicrecession |