Vassilios A. Profillidis

ECONOMETRIC AND FUZZY MODELS FOR THE FORECAST OF DEMAND IN THE AIRPORT OF RHODES

In this paper, econometric and fuzzy methods of forecasting airport demand are suggested, in relation to the airport of Rhodes. First, the relationship between transport and economic activity is analyzed. The appropriate models for demand forecast for tourist airports with high seasonal demands are analyzed. Market surveys, statistical methods, econometric models and the fuzzy method are used to establish relationships important of the future demand for airport services.

Applications of finite element analysis in the rational design of track bed structures

Abstract Applications of finite element analysis in the study of the mechanical behaviour of track and track bed structures are presented. First are examined the conceptualization of the mesh and the effect of three-dimensional or bidimensional approaches. The appropriate constitutive law and the contact conditions between two layers are discussed afterwards. This method was used to predict stress and strain at the subgrade, sleeper and rail level, by taking into account: • • the form of the sleeper (wooden sleepers, prestresssed concrete sleepers, reinforced concrete sleepers) • • the thickness of the track bed structures • • the quality of the soil of the subgrade. The flexibility of the various forms of sleepers was also studied. Practical use of the results obtained has been made in the rational design of track bed structures.

Econometric models for the forecast of passenger demand in Greece

Abstract The structure of models of forecast of passenger demand is first discussed. Parameters affecting modal split and mobility are analyzed. After many trial and errors procedures, three econometric models have been developed for the forecast of passenger demand in Greece: one for total demand, one for rail demand and one for private car demand. The validity of each model is tested by means of statistical and diagnostic tests, which are: Estimation of Coefficient of Determination (R 2), Collinearity test of independent variables, Statistical test of the F -statistics, Statistical test of the standard error, Model function form test, First degree self-correlation test to residuals, Residuals correlation, Heteroscedasticity and normality test, Model stability test and Forecasting ability of the models through U-Theil Statistics. The validity of the models has been also corroborated through the study of elasticities of independent variables. Once checked the forecasting ability, the models can be used for the forecast of future demand and modal split for passenger demand in Greece.

A Comparative Analysis of Performances of Econometric, Fuzzy and Time-series Models for the Forecast of Transport Demand

The paper attempts to evaluate how the fuzzy method can improve the range of forecast achieved by classic econometric and time-series models. Based on a survey of factors affecting rail passenger demand and using data of rail passenger demand of Greek Railways, the parameters affecting demand are identified with the use of the appropriate statistical testes. Using these parameters, econometric, fuzzy and time-series models are developed. A comparative analysis of the models developed and of their forecasting ability makes clear the range of applicability of each method and the accuracy and the reduction of ambiguity that can be reached with the use of the fuzzy method.

Forecast of tourism demand with the use of fuzzy and cointegration econometric techniques

Econometric models for the forecast of tourism demand are developed in this paper. In order to assess the long-run trends concerning principal tourism generating countries, the Johansens maximum likelihood techniques are applied. For a better assessment of the short-run trends, the estimated error correction terms are introduced to the first difference models to estimate the short-run relationships (Error Correction Models, ECMs). Based on the results given by the ECMs, fuzzy regression models are suggested and then compared in order to provide the forecasting ability of both techniques (Fuzzy and ECMs). Finally, for the evaluation of forecasting performance, the Theils Inequality Coefficient is applied.