Commuting flow prediction using OpenStreetMap data
Abstract Accurately predicting commuting flows is crucial for sustainable urban planning and preventing disease spread due to human mobility. While recent advancements have produced effective models for predicting these recurrent flows, the existing methods rely on datasets exclusive to a few study...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Springer
2025-01-01
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| Series: | Computational Urban Science |
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| Online Access: | https://doi.org/10.1007/s43762-025-00161-5 |
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| _version_ | 1832585993737207808 |
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| author | Kuldip Singh Atwal Taylor Anderson Dieter Pfoser Andreas Züfle |
| author_facet | Kuldip Singh Atwal Taylor Anderson Dieter Pfoser Andreas Züfle |
| author_sort | Kuldip Singh Atwal |
| collection | DOAJ |
| description | Abstract Accurately predicting commuting flows is crucial for sustainable urban planning and preventing disease spread due to human mobility. While recent advancements have produced effective models for predicting these recurrent flows, the existing methods rely on datasets exclusive to a few study areas, limiting the transferability to other locations. This research broadens the utility of state-of-the-art commuting flow prediction models with globally available OpenStreetMap data while achieving prediction accuracy comparable to location-specific and proprietary data. We show that the types of buildings, residential and non-residential, are a strong indicator for predicting commuting flows. Consistent with theoretical and analytical models, our experiments indicate that building types, distance, and population are the determining characteristics for mobility related to commuting. Our experiments show that predicted flows closely match ground truth flows. Our work enables accurate flow prediction using building types to support applications such as urban planning and epidemiology. |
| format | Article |
| id | doaj-art-73023f7e9d6041eaa1ed546317d2267a |
| institution | Kabale University |
| issn | 2730-6852 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Springer |
| record_format | Article |
| series | Computational Urban Science |
| spelling | doaj-art-73023f7e9d6041eaa1ed546317d2267a2025-01-26T12:20:09ZengSpringerComputational Urban Science2730-68522025-01-015111410.1007/s43762-025-00161-5Commuting flow prediction using OpenStreetMap dataKuldip Singh Atwal0Taylor Anderson1Dieter Pfoser2Andreas Züfle3Geography and Geoinformation Science, George Mason UniversityGeography and Geoinformation Science, George Mason UniversityGeography and Geoinformation Science, George Mason UniversityDepartment of Computer Science, Emory UniversityAbstract Accurately predicting commuting flows is crucial for sustainable urban planning and preventing disease spread due to human mobility. While recent advancements have produced effective models for predicting these recurrent flows, the existing methods rely on datasets exclusive to a few study areas, limiting the transferability to other locations. This research broadens the utility of state-of-the-art commuting flow prediction models with globally available OpenStreetMap data while achieving prediction accuracy comparable to location-specific and proprietary data. We show that the types of buildings, residential and non-residential, are a strong indicator for predicting commuting flows. Consistent with theoretical and analytical models, our experiments indicate that building types, distance, and population are the determining characteristics for mobility related to commuting. Our experiments show that predicted flows closely match ground truth flows. Our work enables accurate flow prediction using building types to support applications such as urban planning and epidemiology.https://doi.org/10.1007/s43762-025-00161-5Commuting flowsOSMFlow predictionGraph attention networks |
| spellingShingle | Kuldip Singh Atwal Taylor Anderson Dieter Pfoser Andreas Züfle Commuting flow prediction using OpenStreetMap data Computational Urban Science Commuting flows OSM Flow prediction Graph attention networks |
| title | Commuting flow prediction using OpenStreetMap data |
| title_full | Commuting flow prediction using OpenStreetMap data |
| title_fullStr | Commuting flow prediction using OpenStreetMap data |
| title_full_unstemmed | Commuting flow prediction using OpenStreetMap data |
| title_short | Commuting flow prediction using OpenStreetMap data |
| title_sort | commuting flow prediction using openstreetmap data |
| topic | Commuting flows OSM Flow prediction Graph attention networks |
| url | https://doi.org/10.1007/s43762-025-00161-5 |
| work_keys_str_mv | AT kuldipsinghatwal commutingflowpredictionusingopenstreetmapdata AT tayloranderson commutingflowpredictionusingopenstreetmapdata AT dieterpfoser commutingflowpredictionusingopenstreetmapdata AT andreaszufle commutingflowpredictionusingopenstreetmapdata |