ISPRS-Archives The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ISPRS-Archives Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. 2194-9034 Copernicus Publications Göttingen, Germany 10.5194/isprs-archives-XLVIII-4-W7-2023-53-2023 A COMPARATIVE STUDY OF METHODS FOR DRIVE TIME ESTIMATION ON GEOSPATIAL BIG DATA: A CASE STUDY IN USA Fu X. https://orcid.org/0000-0002-3396-6720 1 2 Kakkar D. 1 Chen J. 1 Moynihan K. M. 3 Hegland T. A. 4 Blossom J. 1 Center for Geographic Analysis (CGA), Harvard University, Cambridge Massachusetts, USA State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, China Boston Children’s Hospital and Harvard Medical School, Boston, Massachusetts, USA Agency for Healthcare Research and Quality, Rockville, Maryland, USA 22 06 2023 XLVIII-4/W7-2023 53 60 Copyright: © 2023 X. Fu et al. 2023 This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/ This article is available from https://isprs-archives.copernicus.org/articles/XLVIII-4-W7-2023/53/2023/isprs-archives-XLVIII-4-W7-2023-53-2023.html The full text article is available as a PDF file from https://isprs-archives.copernicus.org/articles/XLVIII-4-W7-2023/53/2023/isprs-archives-XLVIII-4-W7-2023-53-2023.pdf

Travel time estimation is crucial for several geospatial research studies, particularly healthcare accessibility studies. This paper presents a comparative study of six methods for drive time estimation on geospatial big data in the USA. The comparison is done with respect to the cost, accuracy, and scalability of these methods. The six methods examined are Google Maps API, Bing Maps API, Esri Routing Web Service, ArcGIS Pro Desktop, OpenStreetMap NetworkX (OSMnx), and Open Source Routing Machine (OSRM). Our case study involves calculating driving times of 10,000 origin-destination (OD) pairs between ZIP code population centroids and pediatric hospitals in the USA. We found that OSRM provides a low-cost, accurate, and efficient solution for calculating travel time on geospatial big data. Our study provides valuable insight into selecting the most appropriate drive time estimation method and is a benchmark for comparing the six different methods. Our open-source scripts are published on GitHub (https://github.com/wybert/Comparative-Study-of-Methods-for-Drive-Time-Estimation) to facilitate further usage and research by the wider academic community.