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    Hazard-Based Duration Models for Predicting Actual Duration of Highway Projects Using Nonparametric and Parametric Survival Analysis

    Source: Journal of Management in Engineering:;2019:;Volume ( 035 ):;issue: 006
    Author:
    Yu Qiao
    ,
    Samuel Labi
    ,
    Jon D. Fricker
    DOI: 10.1061/(ASCE)ME.1943-5479.0000700
    Publisher: American Society of Civil Engineers
    Abstract: At the preconstruction phases of project development, facility stakeholders typically seek reliable estimates of project duration. This paper shows how hazard-based duration models could be used to forecast the actual (final) duration of a highway construction project based on variables known at the time of the project letting phase. To account for the large uncertainty in project duration, a probabilistic method known as survival analysis was used. First, a nonparametric estimation method (Kaplan–Meier) was applied to develop survival curves for project types in various work categories and identified project groups that had similar survival distributions using the log-rank test. Parametric time-to-event models were then developed to relate project duration to the external covariates. The methodology was applied to multiple project types in five work categories. The results describe the nature of the nonlinear relationship between project duration and the external covariates. The results also show how project duration can be influenced by how projects are bundled, project location, letting season, design work source, and contractor experience. The developed models can help project stakeholders improve their estimates of project duration and gain insight into the role played by the factors that affect project duration.
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      Hazard-Based Duration Models for Predicting Actual Duration of Highway Projects Using Nonparametric and Parametric Survival Analysis

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4260655
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    contributor authorYu Qiao
    contributor authorSamuel Labi
    contributor authorJon D. Fricker
    date accessioned2019-09-18T10:43:03Z
    date available2019-09-18T10:43:03Z
    date issued2019
    identifier other%28ASCE%29ME.1943-5479.0000700.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4260655
    description abstractAt the preconstruction phases of project development, facility stakeholders typically seek reliable estimates of project duration. This paper shows how hazard-based duration models could be used to forecast the actual (final) duration of a highway construction project based on variables known at the time of the project letting phase. To account for the large uncertainty in project duration, a probabilistic method known as survival analysis was used. First, a nonparametric estimation method (Kaplan–Meier) was applied to develop survival curves for project types in various work categories and identified project groups that had similar survival distributions using the log-rank test. Parametric time-to-event models were then developed to relate project duration to the external covariates. The methodology was applied to multiple project types in five work categories. The results describe the nature of the nonlinear relationship between project duration and the external covariates. The results also show how project duration can be influenced by how projects are bundled, project location, letting season, design work source, and contractor experience. The developed models can help project stakeholders improve their estimates of project duration and gain insight into the role played by the factors that affect project duration.
    publisherAmerican Society of Civil Engineers
    titleHazard-Based Duration Models for Predicting Actual Duration of Highway Projects Using Nonparametric and Parametric Survival Analysis
    typeJournal Paper
    journal volume35
    journal issue6
    journal titleJournal of Management in Engineering
    identifier doi10.1061/(ASCE)ME.1943-5479.0000700
    page04019024
    treeJournal of Management in Engineering:;2019:;Volume ( 035 ):;issue: 006
    contenttypeFulltext
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