Connections between classical car following models and artificial neural networks

Abstract

This article considers the problem of traffic modeling via modeling at the microscopic (i.e., vehicle) scale. It provides a connection between classical ordinary differential equation based models and data driven artificial neural network (ANN) based models by showing an example of a car following model which can be exactly expressed as an ANN. In a set of numerical experiments, four ANN models (ranging in structure from a model that is able to exactly capture a classical car following model, to a generic neural network model) are proposed and then trained from data and their resulting accuracy is assessed. It is shown that by adding structure into the neural network (i.e., via the architecture and the activation functions), it is possible to outperform generic ANN models to emergent phenomena such as stop and go waves.