Estimating a massive drive time matrix between locations is a practical but challenging task. The challenges include availability of reliable road network (including traffic) data, programming expertise, and access to high-performance computing resources. This research proposes a method for estimating a nationwide drive time matrix between ZIP code areas in the U.S.-a geographic unit at which many national datasets such as health information are compiled and distributed. The method (1) does not rely on intensive efforts in data preparation or access to advanced computing resources, (2) uses algorithms of varying complexity and computational time to estimate drive times of different trip lengths, and (3) accounts for both interzonal and intrazonal drive times. The core design samples ZIP code pairs with various intensities according to trip lengths and derives the drive times via Google Maps API, and the Google times are then used to adjust and improve some primitive estimates of drive times with low computational costs. The result provides a valuable resource for researchers.

中文翻译：

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估计美国邮政编码之间的大型行驶时间矩阵：差分采样方法


估计地点之间的大量行驶时间矩阵是一项实用但具有挑战性的任务。挑战包括可靠的道路网络（包括交通）数据的可用性、编程专业知识以及高性能计算资源的访问。这项研究提出了一种估计美国邮政编码区域之间的全国性驾驶时间矩阵的方法，邮政编码区域是一个地理单位，在该地理单位上编译和分发了许多国家数据集（例如健康信息）。该方法 (1) 不依赖于数据准备或访问高级计算资源方面的大量工作，(2) 使用不同复杂度和计算时间的算法来估计不同行程长度的驾驶时间，以及 (3) 考虑了区域间和区域间的情况。区域内驾驶时间。核心设计根据行程长度对不同强度的邮政编码对进行采样，并通过 Google Maps API 导出行驶时间，然后使用 Google 时间以较低的计算成本调整和改进一些原始的行驶时间估计。该结果为研究人员提供了宝贵的资源。