Methods for Inferring Route Choice of Commuting Trip From Mobile Phone Network Data
For billing purposes, telecom operators collect communication logs of our mobile phone usage activities. These communication logs or so called CDR has emerged as a valuable data source for human behavioral studies. This work builds on the transportation modeling literature by introducing a new appro...
| Main Authors: | , , , |
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| Format: | Article |
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Multidisciplinary Digital Publishing Institute
2020
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| Online Access: | https://hdl.handle.net/1721.1/125632 |
| _version_ | 1811078934170697728 |
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| author | Sakamanee, Pitchaya Phithakkitnukoon, Santi Smoreda, Zbigniew Ratti, Carlo |
| author2 | Massachusetts Institute of Technology. Department of Urban Studies and Planning |
| author_facet | Massachusetts Institute of Technology. Department of Urban Studies and Planning Sakamanee, Pitchaya Phithakkitnukoon, Santi Smoreda, Zbigniew Ratti, Carlo |
| author_sort | Sakamanee, Pitchaya |
| collection | MIT |
| description | For billing purposes, telecom operators collect communication logs of our mobile phone usage activities. These communication logs or so called CDR has emerged as a valuable data source for human behavioral studies. This work builds on the transportation modeling literature by introducing a new approach of crowdsource-based route choice behavior data collection. We make use of CDR data to infer individual route choice for commuting trips. Based on one calendar year of CDR data collected from mobile users in Portugal, we proposed and examined methods for inferring the route choice. Our main methods are based on interpolation of route waypoints, shortest distance between a route choice and mobile usage locations, and Voronoi cells that assign a route choice into coverage zones. In addition, we further examined these methods coupled with a noise filtering using Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and commuting radius. We believe that our proposed methods and their results are useful for transportation modeling as it provides a new, feasible, and inexpensive way for gathering route choice data, compared to costly and time-consuming traditional travel surveys. It also adds to the literature where a route choice inference based on CDR data at this detailed level - i.e., street level - has rarely been explored. Keywords: commuting trip; route choice inference; mobile phone network data; CDR; call detail
records |
| first_indexed | 2024-09-23T11:07:33Z |
| format | Article |
| id | mit-1721.1/125632 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T11:07:33Z |
| publishDate | 2020 |
| publisher | Multidisciplinary Digital Publishing Institute |
| record_format | dspace |
| spelling | mit-1721.1/1256322022-10-01T01:24:50Z Methods for Inferring Route Choice of Commuting Trip From Mobile Phone Network Data Sakamanee, Pitchaya Phithakkitnukoon, Santi Smoreda, Zbigniew Ratti, Carlo Massachusetts Institute of Technology. Department of Urban Studies and Planning For billing purposes, telecom operators collect communication logs of our mobile phone usage activities. These communication logs or so called CDR has emerged as a valuable data source for human behavioral studies. This work builds on the transportation modeling literature by introducing a new approach of crowdsource-based route choice behavior data collection. We make use of CDR data to infer individual route choice for commuting trips. Based on one calendar year of CDR data collected from mobile users in Portugal, we proposed and examined methods for inferring the route choice. Our main methods are based on interpolation of route waypoints, shortest distance between a route choice and mobile usage locations, and Voronoi cells that assign a route choice into coverage zones. In addition, we further examined these methods coupled with a noise filtering using Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and commuting radius. We believe that our proposed methods and their results are useful for transportation modeling as it provides a new, feasible, and inexpensive way for gathering route choice data, compared to costly and time-consuming traditional travel surveys. It also adds to the literature where a route choice inference based on CDR data at this detailed level - i.e., street level - has rarely been explored. Keywords: commuting trip; route choice inference; mobile phone network data; CDR; call detail records 2020-06-02T19:02:51Z 2020-06-02T19:02:51Z 2020-05-07 2020-03 2020-05-14T13:55:40Z Article http://purl.org/eprint/type/JournalArticle 2220-9964 https://hdl.handle.net/1721.1/125632 Sakamanee, Pitchaya et al. “Methods for Inferring Route Choice of Commuting Trip From Mobile Phone Network Data.” ISPRS International Journal of Geo-Information 9, 5 (May 2020): 306. http://dx.doi.org/10.3390/ijgi9050306 ISPRS International Journal of Geo-Information Creative Commons Attribution https://creativecommons.org/licenses/by/4.0/ application/pdf Multidisciplinary Digital Publishing Institute Multidisciplinary Digital Publishing Institute |
| spellingShingle | Sakamanee, Pitchaya Phithakkitnukoon, Santi Smoreda, Zbigniew Ratti, Carlo Methods for Inferring Route Choice of Commuting Trip From Mobile Phone Network Data |
| title | Methods for Inferring Route Choice of Commuting Trip From Mobile Phone Network Data |
| title_full | Methods for Inferring Route Choice of Commuting Trip From Mobile Phone Network Data |
| title_fullStr | Methods for Inferring Route Choice of Commuting Trip From Mobile Phone Network Data |
| title_full_unstemmed | Methods for Inferring Route Choice of Commuting Trip From Mobile Phone Network Data |
| title_short | Methods for Inferring Route Choice of Commuting Trip From Mobile Phone Network Data |
| title_sort | methods for inferring route choice of commuting trip from mobile phone network data |
| url | https://hdl.handle.net/1721.1/125632 |
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