The fundamental challenge of the origin-destination (OD) matrix estimation problem is that it is severely under-determined. In this paper we propose a new data driven OD estimation method for cases where a supply pattern in the form of speeds and flows is available. We show that with these input data, we do not require an iterative dynamic network loading procedure that results in an equilibrium assignment, nor do we need an assumption on the kind of equilibrium that emerges from this process. The minimal number of ingredients which are needed are (a) a method to estimate/predict production and attraction time series; (b) a method to compute the N shortest paths from each OD zone to the next; and (c) two—possibly OD-specific—assumptions on the magnitude of N; and on the proportionality of path flows between these origins and destinations, respectively. The latter constitutes the most important behavioral assumption in our method, which relates to how we assume travelers have chosen their routes between OD pairs. We choose a proportionality factor that is inversely proportional to realized travel time, where we incorporate a penalty for path overlap. For large networks, these ingredients may be insufficient to solve the resulting system of equations. We show how additional constraints can be derived directly from the data by using principal component analysis, with which we exploit the fact that temporal patterns of production and attraction are similar across the network. Experimental results on a toy network and a large city network (Santander, Spain) show that our OD estimation method works satisfactorily, given a reasonable choice of N, and the use of so-called 3D supply patterns, which provide a compact representation of the supply dynamics over the entire network. Inclusion of topological information makes the method scalable both in terms of network size and for different topologies. Although we use a neural network to predict production and attraction in our experiments (which implies ground-truth OD data were needed), there are straight-forward paths to improve the method using additional data, such as demographic data, household survey data, social media and or movement traces, which could support estimating such ground-truth baseline production and attraction patterns. The proposed framework would fit very nicely in an online traffic modeling and control framework, and we see many paths to further refine and improve the method.

起点-终点（OD）矩阵估计问题的根本挑战在于，它的确定性严重不足。在本文中，我们针对速度和流量形式的供应模式可用的情况，提出了一种新的数据驱动的OD估算方法。我们证明，使用这些输入数据，我们不需要导致平衡分配的迭代动态网络加载过程，也不需要对从此过程中出现的平衡类型进行假设。该成分的最小数量是需要的是（a）至估计的方法/预测的生产和吸引时间系列; （b）一种计算从每个OD区域到下一个OD区域的N条最短路径的方法；（c）关于N大小的两个假设（可能是特定于OD的）; 以及这些起点和终点之间的路径流动的比例。后者构成了我们方法中最重要的行为假设，与我们假设旅行者如何选择OD对之间的路线有关。我们选择与实现的旅行时间成反比的比例因子，其中我们对路径重叠进行了惩罚。对于大型网络，这些成分可能不足以解决所得的方程组。我们展示了如何通过使用主成分分析直接从数据中得出其他约束，从而利用这一事实，即整个网络中生产和吸引力的时间模式相似。在玩具网络和大型城市网络（桑坦德，N以及所谓的3D供应模式的使用，它们提供了整个网络上供应动态的紧凑表示。包含拓扑信息使该方法在网络规模和不同拓扑方面均可扩展。尽管我们在实验中使用神经网络来预测产量和吸引力（这意味着需要真实的OD数据），但仍存在直接途径可以使用其他数据（例如人口统计数据，家庭调查数据，媒体和/或运动轨迹，可以支持估算这种真实的基线生产和吸引方式。所提出的框架非常适合在线交通建模和控制框架，并且我们看到了许多进一步完善和改进方法的途径。