Probability distribution‐based model for work zone capacity prediction

Abstract

SummaryThis study aims to develop a truncated lognormal distribution model to predict work zone capacity. The distribution model parameters are formulated as linear functions of road type, number of closed lanes, number of opened lanes, lane closure location, work duration, work intensity, work time, heavy‐vehicle percentage, and capacity measurement method. To reflect the uncertainty, a prediction band is constructed for the mean work zone capacity. The maximum likelihood estimation technique is used to determine the coefficients of the variables included in the probability distribution‐based capacity model. The sensitivity analysis results confirm that the proposed model has the capability of accurately predicting the mean and prediction band of work zone capacity at any given confidence level. In addition, it is found that work zones located in urban roads, with a large number of opened lanes, low heavy‐vehicle percentage, or long‐term work duration, have larger mean work zone capacity. A work zone with the closure of the right lane has bigger variability of work zone capacity than a work zone with the left‐lane closure. In addition, the measured work zone capacity is a little bigger if the hourly traffic volume converted from the maximum 3‐/5‐/15‐minute flow rate is considered as the work zone capacity. The proposed probability distribution‐based capacity model can help traffic engineers to evaluate the variability of work zone capacity and travel delay range. Copyright © 2015 John Wiley & Sons, Ltd.