论文标题
估计美国邮政编码之间的较大驱动时间矩阵：一种差异采样方法
Estimating a Large Drive Time Matrix between Zip Codes in the United States: A Differential Sampling Approach
论文作者
论文摘要
估计位置之间的大量驱动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（包括流量）数据，编程专业知识以及获得高性能计算资源的可用性。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开车时间矩阵的方法 - 一个地理单位，许多国家数据集（例如健康信息）被编译和分发。方法（1）不依赖于数据准备或访问高级计算资源的密集努力，（2）使用不同的复杂性和计算时间的算法来估计不同行程长度的驱动时间，（3）分区间和内部驱动器的时间均考虑。核心设计样本根据行程长度具有各种强度的邮政编码对，并通过Google Maps API得出了驱动时间，然后使用Google Times来调整和改善一些原始估计，以低计算成本来调整驱动时间。结果为研究人员提供了宝贵的资源。
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.
