Developing a Multivariate Time-Series Model to Forecast the Level of Competition in Transportation Projects
Publication: Construction Research Congress 2022
ABSTRACT
Forecasting the level of competition is critical for letting, investing, and pricing strategies for transportation owners and contractors. For instance, it helps owner organizations establish better letting strategies, such as scheduling projects for bid, balancing projects in a letting, and packaging projects into proposals. Moreover, for the contractors, it helps to establish investing and pricing strategies, such as the decision for participating in a bid and adjusting the mark-up (profit) level for the bid. Thus, this paper aims to identify the leading indicators of the level of competition for transportation projects and create multivariate time series models for forecasting the level of competition. The study uses the monthly level of competition of highway projects let in the state of Georgia between 2010 and 2018. Ten potential leading indicators of the level of competition, representing the local highway construction market, the construction market, the energy market, and macroeconomic market conditions, were used in this study. This study found the gross domestic product (GDP) of the Georgia construction industry and the number of hires as the leading indicators of the level of competition through the Granger causality test. Vector Error Correction (VEC) models were then developed for forecasting the level of competition with the identified leading indicators. The predictability of the developed VEC model for the level of competition represents a useful tool for making decisions in letting, investing, and pricing the transportation project.
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Construction Research Congress 2022
Pages: 283 - 291
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Published online: Mar 7, 2022
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