American Society of Civil Engineers


Empirical Assessment of the Likelihood and Duration of Highway Project Time Delays


by Panagiotis Ch. Anastasopoulos, (corresponding author), A.M.ASCE, (Visiting Assistant Professor, School of Civil Engineering, Research Program Manager, Center for Road Safety, Purdue Univ., West Lafayette, IN 47907. E-mail: panast@purdue.edu), Samuel Labi, A.M.ASCE, (Associate Professor of Civil Engineering, Purdue Univ., West Lafayette, IN 47907. E-mail: labi@purdue.edu), Abhishek Bhargava, (Project Associate, Agile Assets Inc., 6212 Shanda Dr., Raleigh, NC 27609. E-mail: bhargava.abhishek@gmail.com), and Fred L. Mannering, M.ASCE, (Charles Pankow Professor of Civil Engineering, Associate Director, Center for Road Safety, Purdue Univ., West Lafayette, IN 47907. E-mail: flm@purdue.edu)

Journal of Construction Engineering and Management, Vol. 138, No. 3, March 2012, pp. 390-398, (doi:  http://dx.doi.org/10.1061/(ASCE)CO.1943-7862.0000437)

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Document type: Journal Paper
Section Heading: Project Planning and Design
Abstract: Delays in the completion of highway construction and maintenance projects are important concerns to state highway agencies and contractors alike because such time delays can have a number of adverse consequences, such as extending the duration of active work zones, contributing to road-user dissatisfaction and increasing the risk of litigation regarding delay responsibility. In this paper, using data from 1,722 highway projects in Indiana, random-parameter statistical models are estimated to study the factors that contribute to the likelihood of encountering a project time delay and its duration. The model estimation results show that the likelihood and duration of project time delays are significantly influenced by factors such as project cost (contract bid amount), project type, planned project duration, and the likelihood of adverse weather.


ASCE Subject Headings:
Delay time
Project management
Statistics
Parameters
Highways and roads

Author Keywords:
Time delay
Project planning
Statistical models
Random parameters