Estimating a Large Drive Time Matrix between Zip Codes in the United States: A Differential Sampling Approach

TL;DR

This paper introduces a scalable, data-efficient method for estimating nationwide drive time matrices between ZIP codes in the U.S., leveraging Google Maps API and differential sampling to improve accuracy without high computational demands.

Contribution

It presents a novel approach that combines differential sampling with API data to estimate large-scale drive time matrices efficiently and accurately.

Findings

Effective estimation of drive times across all ZIP code pairs.

Reduced reliance on extensive data preparation and high-performance computing.

Improved accuracy in drive time estimates using the proposed method.

Abstract

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.