Document Type
Conference Proceeding
Publication Date
8-2020
Publisher
American Society of Civil Engineers (ASCE)
Source Publication
Resilience and Sustainable Transportation Systems: Selected Papers from the 13th Asia Pacific Transportation Development Conference
Source ISSN
978-0-7844-8290-2
Original Item ID
DOI: 10.1061/9780784482902.011
Abstract
In a design-bid-build infrastructural project, the agency may use a lump-sum, or unit-price for temporary traffic control (TTC) items, while their cost is hard to estimate. This paper presents the research results of developing a machine learning model of the relationship between the TTC items’ cost with the project total cost and non-TTC items in infrastructural projects. In detail, 163 infrastructural projects’ data were collected for analyzing two research questions: first, the relationship between the TTC items with the project total cost and non-TTC items; second, the relationship between the TTC items’ payment option with the project total cost and non-TTC items. The results showed that the proposed feed-forward neural network model outperforms regression methods on classification tasks. It has a 36% accuracy in determining the TTC items’ cost as a percentage range of project total cost. Additionally, the proposed model has 94% accuracy in determining the TTC items’ payment options, when the information of the project total cost and the major non-TTC items’ information are known. With this research, the TTC items’ payment option for a new infrastructural project could be confidently decided, and the TTC items’ cost could be easily estimated as percentage ranges of the project total cost, which helps project owners and agencies to evaluate the quality of contractors’ bids.
Recommended Citation
Jiang, Yuhan; Han, Sisi; and Bai, Yong, "Machine Learning-Based Temporary Traffic Control Cost Analysis" (2020). Civil and Environmental Engineering Faculty Research and Publications. 274.
https://epublications.marquette.edu/civengin_fac/274
Comments
Accepted version. Resilience and Sustainable Transportation Systems, published as part of the proceedings of the13th Asia Pacific Transportation Development Conference, 2020: 86-96. DOI. © 2020 American Society of Civil Engineers. Used with permission.