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Abstract
Transport sector is very important for development of local economies, so it is intensively studied in different countries. Road infrastructure construction projects in many European countries are mainly carried out through various forms of Public–Private Partnership (PPP). Financial evaluation, private partner selection criteria, technical characteristics and very important focus of sustainable development components (environmental, social and economic) of PPP road infrastructure development projects are widely analysed in the scientific literature. Although many research studies were published for PPP road infrastructure projects efficiency assessment from different aspects, there have not been created assessment methodology with all key areas incorporated altogether. The authors provide an integrated PPP road infrastructure projects effectiveness modelling methodology by applying Random Forest technique. The developed methodology is recommended to be applied for PPP road infrastructure projects effectiveness prediction from the private and public sector perspectives.
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Notes on contributors
Rūta Rudžianskaitė–Kvaraciejienė
Rūta RUDŽIANSKAITĖ–KVARACIEJIENĖ. PhD Student of Kaunas University of Technology, Faculty of Civil Engineering and Architecture, Department of Civil Technology. She received the M.S. degree in civil engineering from Kaunas University of Technology, Lithuania, in 2007. Main research interests: construction project management, investment projects, effectiveness modelling, random forests.
Rasa Apanavičienė
Rasa APANAVIČIENĖ. Assoc. Prof. of the Department of Civil Engineering Technologies at Kaunas University of Technology. PhD in Civil Engineering from Kaunas University of Technology in 2002. Main research interests: construction project management, investment projects, strategic management and effectiveness modelling, neural networks.
Adas Gelžinis
Adas GELŽINIS. Adas Gelzinis received the M.S. degree in electrical engineering from Kaunas University of Technology, Lithuania, in 1995. He received the PhD degree in computer science from the same university, in 2000. He is a professor in the Department of Electrical Power Systems at Kaunas University of Technology. His research interests include artificial neural networks, kernel methods, pattern recognition, signal and image processing, texture classification.