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.
翻译:我们研究一种动态交通分配模式,在这种模式下,代理商根据实时延迟预测作出即时路线决定;我们设计一个数学简洁的模型,并得出预测器的特性,以确保动态预测平衡;我们通过显示我们的框架包含众所周知的完整信息和瞬时信息模型,并承认更多现实的预测器为特殊情况,来证明我们的框架的多功能性;我们通过一项实验性研究来补充我们的理论分析,我们通过实验性研究系统地比较不同预测器的引出的平均旅行时间,包括一个在合成和真实公路网络上就以前计算平衡流获得的数据进行的培训的机器学习模型。