A New Methodology to Estimate Capacity for Freeway Work Zones
Tae-Hyung Kim, David J. Lovell
Abstract
Tae-Hyung Kim, David J. Lovell
Abstract
The objectives of this study were to investigate various independent factors that contribute to capacity reduction in work zones and to suggest a new methodology to estimate the work zone capacity. To develop the new capacity estimation model, traffic and geometric data were collected at 12 work zone sites with lane closures on four normal lanes in one direction, mainly after the peak-hour during daylight and night. The multiple regression model was developed to estimate capacity on work zones for establishing a functional relationship between work zone capacity and several key independent factors such as the number of closed lanes, the proportion of heavy vehicles, grade and the intensity of work activity. The proposed model was compared with other existing capacity models, and showed improved performance for all of the validation data.
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The objectives of this study were to investigate various independent factors that contribute to capacity reduction in work zones and to suggest a new methodology to estimate the work zone capacity. To develop the new capacity estimation model, traffic and geometric data were collected at 12 work zone sites with lane closures on four normal lanes in one direction, mainly after the peak-hour during daylight and night. The multiple regression model was developed to estimate capacity on work zones for establishing a functional relationship between work zone capacity and several key independent factors such as the number of closed lanes, the proportion of heavy vehicles, grade and the intensity of work activity. The proposed model was compared with other existing capacity models, and showed improved performance for all of the validation data.
Key concepts: Work (physics), Work zone, Regression analysis, Estimation, Environmental science, Regression, Transport engineering, Computer science