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.
翻译:估算不同地点之间的大规模驱动时间矩阵是一项实际但具有挑战性的任务。挑战包括提供可靠的公路网络(包括交通)数据、程序设计专门知识和高性能计算资源。这项研究提出了一种方法,用于估算美国地理单位ZIP代码区之间的全国性驱动时间矩阵,该地理单位汇编和分发许多国家数据集,如健康信息。方法(1)不依赖在数据编制或获取先进计算机资源方面作出大量努力,(2)使用不同复杂程度和计算时间的算法来估计不同行程长度的驱动时间,(3)核算区域间和区内驱动时间。核心设计样本ZIP代码按行程长度分列,通过谷歌地图API得出驱动时间,然后使用谷歌时间调整和改进一些计算成本低的原始驱动时间估计。结果为研究人员提供了宝贵的资源。