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

Abstract

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.

Understanding Attitudes towards Proenvironmental Travel: An Empirical Study from Tangshan City in China

“…Consequently, an alternative to pseudo R -square is proposed to estimate the goodness of fit. It can be seen as a rough approximation of model prediction accuracy [ 28 ]. Three pseudo R -squares calculated by SPSS are shown in Table 4 .…”

Forecasting inter-urban transport demand for a logistics company: A combined grey–periodic extension model with remnant correction

“…The approach proposed by Fang et al 16 can cope with the effect of macroeconomic fluctuation on small-sized enterprises. However, data pre-processing eliminates the irregular data, which helps to improve the stability of the observed data series.…”

“…McCarthy 14 and Lee et al 15 acknowledge that these two kinds of factors are continually changing, and the connections between them are difficult to accurately express, showing a kind of grey system relation. Fang et al 16 argued that to some extent, the micro transportation demand of a firm or company will rise and fall along with macroeconomic fluctuations, but its fluctuation period and development trend are often unclear and fuzzy.…”

“…In terms of processing the seasonal fluctuations and trends of a transportation demand time series with a fixed period, it has been suggested to separate the time series by smoothing the data into a trend component, fluctuation component and error component and then using different models to fit the period and the trend, respectively, and combine them, according to Barrow. 17 This method was successfully applied to forecast the transport demand of a small logistics company (an export container transportation business near the port area) developed by Fang et al 16 Seasonal Autoregressive Integrated Moving Average (SARIMA) model is another forecasting method usually used to analyse non-stationary time series with trend and seasonality. 18 However, when the seasonal fluctuation period is not very stable or clear, this method may confuse the subsequent analysis.…”

“…For a time series with trend and periodic waves, it can neither reveal the periodic feature very well by only using grey system models nor can it reveal the trend by only using periodic extension models (PEMs). The approach proposed by Fang et al 16 can cope with the effect of macroeconomic fluctuation on small-sized enterprises. However, data pre-processing eliminates the irregular data, which helps to improve the stability of the observed data series.…”