Abstract
The methods, which are used for the purpose of passenger demand forecasting by Slovak transportation companies at the present time, are considerably simplified, and what is more, they are not already considered to be accurate. These limitations might be caused by insufficient research in this area over last years. Purpose of this paper is to identify a statistical model of passenger demand for suburban bus transport which satisfies the statistical significance of its parameters and randomness of its residuals. Three different methodologies - exponential smoothing, multiple linear regression and autoregressive models were used in order to identify more accurate and reliable statistical model compared with nowadays used ones.
Highlights
Statistical modelling and forecasting of passenger demand by using univariate time series theory is probably one of the most common forecasting methods used for work with periodic time series data
The main goal of this paper is to introduce method of the statistical modelling of passenger demand by using univariate time series theory which appears to be more accurate and reliable alternative to automated forecasting procedures published in the literature [3]
In accordance with the main goal of the paper there was designed a statistical model which is suitable for short-term forecasting of passenger demand for suburb bus transport in Zilina region
Summary
Statistical modelling and forecasting of passenger demand by using univariate time series theory is probably one of the most common forecasting methods used for work with periodic time series data This methodology has been successfully applied in the sphere of urban transport [1, 2] and in recently published models of passenger (carried per school reduced [3, 4] and normal fare [3, 5, 6]) demand for suburban bus transport. The main goal of this paper is to introduce method of the statistical modelling of passenger (carried per school reduced fare) demand by using univariate time series theory which appears to be more accurate and reliable alternative to automated forecasting procedures published in the literature [3]. The most of analyses, modelling and forecasting procedures of the time series mentioned in this paper were worked out by using SAS LE 4.1 [7] and SAS 9.3.1 [8] software
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