Estimating Uncertainty of Work Zone Capacity using Neural Network Models

Abstract

This study aims to develop a neural network model to predict work zone capacity including various uncertainties stemming from traffic and operational conditions. The neural network model is formulated in terms of the number of total lanes, number of open lanes, heavy vehicle percentage, work intensity, and work duration. The data used in this paper are obtained from previous studies published in open literature. To capture the uncertainty of work zone capacity, this paper provides two recent methods that enable neural network models to generate prediction intervals which are determined by mean work zone capacity and prediction standard error. The research first builds a Bayesian neural network model with the application of black-box variational inference (BBVI) technique. The second model is based on a regular artificial neural network with an application of the recently proposed Monte-Carlo dropout technique. Both of the neural network models construct prediction intervals under various confidence levels and provide the coverage rates of the actual work zone capacities. The statistical accuracy (MAPE, MAE, MSE, and RMSE) of the models is then compared with traditional estimation methods in predicted mean work zone capacity. BBVI produces better statistical results than the other three models. Both of the models provide predicted work zone capacity distribution and prediction intervals, whereas traditional models only provide a single estimate.