Estimation of Hourly Link Population and Flow Directions from Mobile CDR
The rise in big data applications in urban planning and transport management is now widening and becoming a part of local government decision-making processes. Understanding people flow inside the city helps urban and transport planners build a healthy and lively city. Many flow maps are based on or...
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-11-01
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| Series: | ISPRS International Journal of Geo-Information |
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| Online Access: | https://www.mdpi.com/2220-9964/7/11/449 |
| _version_ | 1818135149051641856 |
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| author | Ko Ko Lwin Yoshihide Sekimoto Wataru Takeuchi |
| author_facet | Ko Ko Lwin Yoshihide Sekimoto Wataru Takeuchi |
| author_sort | Ko Ko Lwin |
| collection | DOAJ |
| description | The rise in big data applications in urban planning and transport management is now widening and becoming a part of local government decision-making processes. Understanding people flow inside the city helps urban and transport planners build a healthy and lively city. Many flow maps are based on origin-and-destination points with crossing lines, which reduce the map’s readability and overall appearance. Today, with the emergence of geolocation-enabled handheld devices with wireless communication and networking capabilities, human mobility and the resulting events can be captured and stored as text-based geospatial big data. In this paper, we used one-week mobile-call-detail records (CDR) and a GIS road network model to estimate hourly link population and flow directions, based on mobile-call activities of origin⁻destination pairs with a shortest-path analysis for the whole city. Moreover, to gain the actual population size from the number of mobile-call users, we introduced a home-based magnification factor (h-MF) by integrating with the national census. Therefore, the final output link data have both magnitude (actual population) and flow direction at one-hour intervals between 06:00 and 21:00. The hourly link population and flow direction dataset are intended to optimize bus routes, solve traffic congestion problems, and enhance disaster and emergency preparedness. |
| first_indexed | 2024-12-11T09:19:55Z |
| format | Article |
| id | doaj.art-726b38fd788840d39248e2d9203f0c1c |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-12-11T09:19:55Z |
| publishDate | 2018-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-726b38fd788840d39248e2d9203f0c1c2022-12-22T01:13:16ZengMDPI AGISPRS International Journal of Geo-Information2220-99642018-11-0171144910.3390/ijgi7110449ijgi7110449Estimation of Hourly Link Population and Flow Directions from Mobile CDRKo Ko Lwin0Yoshihide Sekimoto1Wataru Takeuchi2Human Centered Urban Informatics, Institute of Industrial Science, The University of Tokyo, Tokyo 153-8505, JapanHuman Centered Urban Informatics, Institute of Industrial Science, The University of Tokyo, Tokyo 153-8505, JapanRemote Sensing of Environment and Disaster, Institute of Industrial Science, The University of Tokyo, Tokyo 153-8505, JapanThe rise in big data applications in urban planning and transport management is now widening and becoming a part of local government decision-making processes. Understanding people flow inside the city helps urban and transport planners build a healthy and lively city. Many flow maps are based on origin-and-destination points with crossing lines, which reduce the map’s readability and overall appearance. Today, with the emergence of geolocation-enabled handheld devices with wireless communication and networking capabilities, human mobility and the resulting events can be captured and stored as text-based geospatial big data. In this paper, we used one-week mobile-call-detail records (CDR) and a GIS road network model to estimate hourly link population and flow directions, based on mobile-call activities of origin⁻destination pairs with a shortest-path analysis for the whole city. Moreover, to gain the actual population size from the number of mobile-call users, we introduced a home-based magnification factor (h-MF) by integrating with the national census. Therefore, the final output link data have both magnitude (actual population) and flow direction at one-hour intervals between 06:00 and 21:00. The hourly link population and flow direction dataset are intended to optimize bus routes, solve traffic congestion problems, and enhance disaster and emergency preparedness.https://www.mdpi.com/2220-9964/7/11/449big dataCDR origin–destination pairsshortest-path analysishourly link population and flow directionhome-based magnification factor (h-MF) |
| spellingShingle | Ko Ko Lwin Yoshihide Sekimoto Wataru Takeuchi Estimation of Hourly Link Population and Flow Directions from Mobile CDR ISPRS International Journal of Geo-Information big data CDR origin–destination pairs shortest-path analysis hourly link population and flow direction home-based magnification factor (h-MF) |
| title | Estimation of Hourly Link Population and Flow Directions from Mobile CDR |
| title_full | Estimation of Hourly Link Population and Flow Directions from Mobile CDR |
| title_fullStr | Estimation of Hourly Link Population and Flow Directions from Mobile CDR |
| title_full_unstemmed | Estimation of Hourly Link Population and Flow Directions from Mobile CDR |
| title_short | Estimation of Hourly Link Population and Flow Directions from Mobile CDR |
| title_sort | estimation of hourly link population and flow directions from mobile cdr |
| topic | big data CDR origin–destination pairs shortest-path analysis hourly link population and flow direction home-based magnification factor (h-MF) |
| url | https://www.mdpi.com/2220-9964/7/11/449 |
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