A Unified Framework for End-to-End Learning of User Equilibrium

Abstract

This paper establishes an end-to-end learning framework for constructing transportation network equilibrium models. The proposed framework directly learns supply and demand components as well as equilibrium states from multi-day traffic state observations. Specifically, it parametrizes unknown model components with neural networks and embeds them in an implicit layer to enforce user equilibrium conditions. By minimizing the differences between the predicted and observed traffic states, parameters for supply and demand components are simultaneously estimated. For efficient training, we design an auto-differentiation-based gradient descent algorithm that handles link- and path-based user equilibrium constraints. The proposed framework is demonstrated using synthesized data on Sioux Falls.