Demand estimation is an important step for multiple applications in urban studies. However, the level of accuracy required depends on the objective of the study. In dynamic traffic microsimulation, the estimation of demand needs to be accurate, as it is intended to describe individuals' trips on a very small-scale. In this case, poor estimation of trip initiation, path, and endings could result in the wrong estimation of the city-block scale's traffic state. Estimating demand at a large-scale with high-resolution is not only very challenging because it requires a large volume of data from multiple sources, but the underlying mathematical problem is considerable and thus hard to solve.

In this paper, we address the issue of trip starts and ends when modeling large perimeters. We propose to enhance the location of trip initiation and termination by merging heterogeneous and large public datasets. To do so, we develop a series of algorithms that identify fine-mesh areas where trips could reliably start or end and we share the estimated demand within these sub-areas, following the distribution of trip purposes (Home, Work, Shop, etc.).

The method is deployed in Lyon city, France, and validated on an extraction of it. Micro-simulation results show that the demand, once accurately distributed, changes the overall network's performance, confirming the significant influence of trip endings and starts on the overall traffic dynamics.

需求估算是城市研究中多种应用的重要一步。但是，所需的准确度取决于研究的目的。在动态交通微观模拟中，需求的估计需要准确，因为其目的是在很小的范围内描述个人的出行。在这种情况下，差的行程开始，路径和终点估计可能会导致错误地估计城市规模的交通状态。以高分辨率大规模地估计需求不仅具有很大的挑战性，因为它需要来自多个来源的大量数据，而且潜在的数学问题是相当大的，因此很难解决。

在本文中，我们解决了在对大周长建模时行程起点和终点的问题。我们建议通过合并异构数据集和大型公共数据集来增强旅行起始和终止的位置。为此，我们开发了一系列算法，以识别出可以可靠地开始或结束行程的细网区域，并根据行程目的（房屋，工作，商店等）分配这些子区域中的估计需求。 ）。

该方法已部署在法国里昂市，并在提取后进行了验证。微观仿真结果表明，需求一旦准确分配，便会改变整个网络的性能，从而确认行程终点和起点对整个交通动态的重大影响。