Time-dependent origin–destination (OD) demand estimation using link traffic data in a large-scale network is a highly underdetermined problem. As a result, providing an accurate initial solution is crucial for obtaining a more reliable estimated demand. In this paper, we discuss the necessity of having a comprehensive demand profiling model that considers the spatial differences of OD pairs and we demonstrate its application in the calibration of large-scale traffic assignment models. First, we apply a departure choice model that adds a time dimension to the OD demand flows concerning their spatial differences. The time-profiled demand is then fed into the time-dependent OD demand estimation problem for further adjustment. Results show that in addition to reducing the error between simulation outputs and the observed link counts, the estimated demand profile more accurately reflects the spatial correlation of the OD pairs in the large-scale network being studied. Results provide practical insights into deployment and calibration of simulation-based dynamic traffic assignment models.

在大型网络中使用链路流量数据进行时间相关的始发目的地（OD）需求估算是一个尚未充分确定的问题。结果，提供准确的初始解决方案对于获得更可靠的估计需求至关重要。在本文中，我们讨论了具有综合考虑OD对空间差异的需求分析模型的必要性，并展示了其在大规模交通分配模型的标定中的应用。首先，我们应用了一个出发选择模型，该模型将关于其空间差异的OD需求流添加了一个时间维度。然后，将时间配置的需求输入到与时间有关的OD需求估计问题中，以进行进一步调整。结果表明，除了减少仿真输出和观察到的链接数之间的误差外，估计的需求曲线更准确地反映了正在研究的大型网络中OD对的空间相关性。结果为基于仿真的动态流量分配模型的部署和校准提供了实用的见识。