Prediction Methods for the Actual Duration of Greek Highway Projects

Abstract

This study presents an overview of estimation methods to predict the actual project duration of Greek highway projects. Emphasis is given to the selection of the appropriate parameters that correlate with the actual project duration and to compare the performances of the main two methods, the linear regression (LR) with the neural network models (NN) based on data available at the bidding stage. In the context of the current research, thirty-seven highway projects were examined, constructed in Greece with similar available data like the extent, the type of work packages and the significance. Selection and ranking variables through correlation analyses using SPSS 25 has been carried on, in order to identify the most significant project variables. These include archeological findings, type of terrain, land expropriation, the existence of bridge, tunnel and embankment. Next step was the use of WEKA application, that highlighted the most efficient subset of variables. After the definition and grouping of the variables for actual duration prediction, these were used as input data for linear regression models (LR) and neural network models (NN). Various models have been created from each investigated method. While their performance and the comparison of linear regression and neural network models to estimate the actual duration of Greek highway projects are presented in this paper. Results’ discussion and conclusions along with limitations and further research are appropriately analyzed.