Freeway Travel Time Prediction with State-Space Neural Networks: Modeling State-Space Dynamics with Recurrent Neural Networks

Abstract

An approach to freeway travel time prediction based on recurrent neural networks is presented. Travel time prediction requires a modeling approach that is capable of dealing with complex nonlinear spatio-temporal relationships among flows, speeds, and densities. Based on the literature, feedforward neural networks are a class of mathematical models well suited for solving this problem. A drawback of the feed-forward approach is that the size and composition of the input time series are inherently design choices and thus fixed for all input. This may lead to unnecessarily large models. Moreover, for different traffic conditions, different sizes and compositions of input time series may be required, a requirement not satisfied by any feedforward data-driven method. The recurrent neural network topology presented is capable of dealing with the spatiotemporal relationships implicitly. The topology of this neural net is derived from a state-space formulation of the travel time prediction problem, which is in line with traffic flow theory. The performance of several versions of this state-space neural network was tested on synthetic data from a densely used highway stretch in the Netherlands. The neural network models were capable of accurately predicting travel times experienced, producing about zero mean normally distributed residuals, rarely outside 10% of the real expected travel times. Moreover, analyses of the internal states and weight configurations revealed that the neural networks could develop an internal model linked to the underlying traffic processes.