This paper intends to transform the transportation network equilibrium modeling paradigm via an “end-to-end” framework that directly learns travel choice preferences and the equilibrium state from multi-day link flow observations. The centerpiece of the proposed framework is to use deep neural networks to represent travelers’ route choice preferences and then encapsulate the neural networks in a variational inequality that prescribes the user equilibrium flow distribution. The proposed neural network architecture ensures the existence of equilibrium and accommodates future changes in road network topology. The variational inequality is then embedded as an implicit layer in a learning framework, which takes the context features (e.g., road network and traveler characteristics) as input and outputs the user equilibrium flow distribution. By comparing computed equilibrium flows with observed flows, the neural networks can be trained. The proposed end-to-end framework is demonstrated and validated using synthesized data for the Sioux Falls network.

本文旨在通过“端到端”框架转变交通网络均衡建模范式，该框架直接从多天的链路流量观察中学习出行选择偏好和均衡状态。拟议框架的核心是使用深度神经网络来表示旅行者的路线选择偏好，然后将神经网络封装在规定用户均衡流量分布的变分不等式中。所提出的神经网络架构确保了平衡的存在，并适应了道路网络拓扑结构的未来变化。然后将变分不等式作为隐式层嵌入到学习框架中，该学习框架将上下文特征（例如，道路网络和旅行者特征）作为输入并输出用户均衡流量分布。通过将计算出的平衡流量与观察到的流量进行比较，可以训练神经网络。使用 Sioux Falls 网络的合成数据演示和验证所提议的端到端框架。