Developing Cost Estimation Models for Road Rehabilitation and Reconstruction
The average unit costs of road works vary substantially between countries, and even between projects in the same country, due to a number of factors. In this paper an effort is made to develop prediction models for the unit costs of road works that could be applied for a wide range of conditions in different countries. A specialized dataset was used, which was generated under a World Bank study that included road works contracts from 14 countries in Europe and Central Asia (ECA). Two techniques were used for model development: multiple regression analysis and artificial neural networks. As the major problem found with the data set was missing or incomplete data, classification trees were used as an intermediate step to evaluate the correctness of the selected parameters. Three models were developed using regression analysis, two for the unit cost of asphalt concrete and one for the cost per km of rehabilitation and reconstruction works. The models include as independent variables the price of diesel fuel, country Gross National Income, World Governance Index, Transparency International Corruption Perception Index, percent of local bidders participating in the tender, and climate conditions. The analysis using classification trees confirmed the appropriateness of the variables selected in the regression analysis. The models developed using artificial neural networks were superior compared to the regression models, using mostly the same parameters. The resulting models could be particularly useful at the strategic level, for planning and optimization of works on road networks in ECA countries.
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Supplemental Notes:
- This paper was sponsored by TRB committee ABJ80 Statistical Methods.
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Corporate Authors:
500 Fifth Street, NW
Washington, DC United States 20001 -
Authors:
- Cirilovic, Jelena
- Vajdic, Nevena
- Mladenovic, Goran
- Queiroz, Cesar
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Conference:
- Transportation Research Board 92nd Annual Meeting
- Location: Washington DC, United States
- Date: 2013-1-13 to 2013-1-17
- Date: 2013
Language
- English
Media Info
- Media Type: Digital/other
- Features: Figures; References; Tables;
- Pagination: 15p
- Monograph Title: TRB 92nd Annual Meeting Compendium of Papers
Subject/Index Terms
- TRT Terms: Asphalt concrete; Data files; Neural networks; Regression analysis; Rehabilitation; Unit costs
- Geographic Terms: Central Asia; Europe
- Subject Areas: Highways; Maintenance and Preservation; I60: Maintenance;
Filing Info
- Accession Number: 01475581
- Record Type: Publication
- Report/Paper Numbers: 13-2037
- Files: TRIS, TRB, ATRI
- Created Date: Mar 14 2013 12:46PM