Radial Basis Function Neural Network for Work Zone Capacity and Queue Estimation

Abstract

An adaptive computational model is presented for estimating the work zone capacity and queue length and delay, taking into account the following factors: number of lanes, number of open lanes, work zone layout, length, lane width, percentage trucks, grade, speed, work intensity, darkness factor, and proximity of ramps. The model integrates judiciously the mathematical rigor of traffic flow theory with the adaptability of neural network analysis. A radial-basis function neural network model is developed to learn the mapping from quantifiable and nonquantifiable factors describing the work zone traffic control problem to the associated work zone capacity. This model exhibits good generalization properties from a small set of training data, a specially attractive feature for estimating the work zone capacity where only limited data is available. Queue delays and lengths are computed using a deterministic traffic flow model based on the estimated work zone capacity. The result of this research is being used to develop an intelligent decision support system to help work zone engineers perform scenario analysis and create traffic management plans consistently, reliably, and efficiently.