A COMPARATIVE STUDY OF METHODS FOR DRIVE TIME ESTIMATION ON GEOSPATIAL BIG DATA: A CASE STUDY IN USA
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...
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2023-06-01
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| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://isprs-archives.copernicus.org/articles/XLVIII-4-W7-2023/53/2023/isprs-archives-XLVIII-4-W7-2023-53-2023.pdf |
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| author | X. Fu X. Fu D. Kakkar J. Chen K. M. Moynihan T. A. Hegland J. Blossom |
| author_facet | X. Fu X. Fu D. Kakkar J. Chen K. M. Moynihan T. A. Hegland J. Blossom |
| author_sort | X. Fu |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-13T03:50:14Z |
| format | Article |
| id | doaj.art-d71d2696fca84792b53cf4b5ce175b7f |
| institution | Directory Open Access Journal |
| issn | 1682-1750 2194-9034 |
| language | English |
| last_indexed | 2024-03-13T03:50:14Z |
| publishDate | 2023-06-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj.art-d71d2696fca84792b53cf4b5ce175b7f2023-06-22T16:12:26ZengCopernicus PublicationsThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences1682-17502194-90342023-06-01XLVIII-4-W7-2023536010.5194/isprs-archives-XLVIII-4-W7-2023-53-2023A COMPARATIVE STUDY OF METHODS FOR DRIVE TIME ESTIMATION ON GEOSPATIAL BIG DATA: A CASE STUDY IN USAX. Fu0X. Fu1D. Kakkar2J. Chen3K. M. Moynihan4T. A. Hegland5J. Blossom6Center for Geographic Analysis (CGA), Harvard University, Cambridge Massachusetts, USAState Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, ChinaCenter for Geographic Analysis (CGA), Harvard University, Cambridge Massachusetts, USACenter for Geographic Analysis (CGA), Harvard University, Cambridge Massachusetts, USABoston Children’s Hospital and Harvard Medical School, Boston, Massachusetts, USAAgency for Healthcare Research and Quality, Rockville, Maryland, USACenter for Geographic Analysis (CGA), Harvard University, Cambridge Massachusetts, USATravel 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.https://isprs-archives.copernicus.org/articles/XLVIII-4-W7-2023/53/2023/isprs-archives-XLVIII-4-W7-2023-53-2023.pdf |
| spellingShingle | X. Fu X. Fu D. Kakkar J. Chen K. M. Moynihan T. A. Hegland J. Blossom A COMPARATIVE STUDY OF METHODS FOR DRIVE TIME ESTIMATION ON GEOSPATIAL BIG DATA: A CASE STUDY IN USA The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | A COMPARATIVE STUDY OF METHODS FOR DRIVE TIME ESTIMATION ON GEOSPATIAL BIG DATA: A CASE STUDY IN USA |
| title_full | A COMPARATIVE STUDY OF METHODS FOR DRIVE TIME ESTIMATION ON GEOSPATIAL BIG DATA: A CASE STUDY IN USA |
| title_fullStr | A COMPARATIVE STUDY OF METHODS FOR DRIVE TIME ESTIMATION ON GEOSPATIAL BIG DATA: A CASE STUDY IN USA |
| title_full_unstemmed | A COMPARATIVE STUDY OF METHODS FOR DRIVE TIME ESTIMATION ON GEOSPATIAL BIG DATA: A CASE STUDY IN USA |
| title_short | A COMPARATIVE STUDY OF METHODS FOR DRIVE TIME ESTIMATION ON GEOSPATIAL BIG DATA: A CASE STUDY IN USA |
| title_sort | comparative study of methods for drive time estimation on geospatial big data a case study in usa |
| url | https://isprs-archives.copernicus.org/articles/XLVIII-4-W7-2023/53/2023/isprs-archives-XLVIII-4-W7-2023-53-2023.pdf |
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