Neuro-Fuzzy Logic Model for Freeway Work Zone Capacity Estimation

Abstract

The work zone capacity cannot be described by any mathematical function because it is a complicated function of a large number of interacting variables. In this paper, a novel adaptive neuro-fuzzy logic model is presented for estimation of the freeway work zone capacity. Seventeen different factors impacting the work zone capacity are included in the model. A neural network is employed to estimate the parameters associated with the bell-shaped Gaussian membership functions used in the fuzzy inference mechanism. An optimum generalization strategy is used in order to avoid over-generalization and achieve accurate results. Comparisons with two empirical equations demonstrate that the new model in general provides a more accurate estimate of the work zone capacity, especially when the data for factors impacting the work zone capacity are only partially available. Further, it provides two additional advantages over the existing empirical equations. First, it incorporates a large number of factors impacting the work zone capacity. Second, unlike the empirical equations, the new model does not require selection of various adjustment factors or values by the work zone engineers based on prior experience.