We study a dynamic traffic assignment model, where agents base their instantaneous routing decisions on real-time delay predictions. We formulate a mathematically concise model and derive properties of the predictors that ensure a dynamic prediction equilibrium exists. We demonstrate the versatility of our framework by showing that it subsumes the well-known full information and instantaneous information models, in addition to admitting further realistic predictors as special cases. We complement our theoretical analysis by an experimental study, in which we systematically compare the induced average travel times of different predictors, including a machine-learning model trained on data gained from previously computed equilibrium flows, both on a synthetic and a real road network.

中文翻译：

---

动态交通分配的机器学习预测均衡

我们研究了一个动态交通分配模型，其中代理基于实时延迟预测做出即时路由决策。我们制定了一个数学简洁的模型，并推导出了确保动态预测平衡存在的预测变量的属性。我们展示了我们的框架的多功能性，它包含了众所周知的完整信息和瞬时信息模型，此外还允许将进一步的现实预测变量作为特殊情况。我们通过一项实验研究补充了我们的理论分析，其中我们系统地比较了不同预测变量的诱导平均旅行时间，包括一个机器学习模型，该模型根据先前计算的平衡流量获得的数据进行训练，无论是在合成道路网还是真实道路网络上。