Methods for Inferring Route Choice of Commuting Trip From Mobile Phone Network Data
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ijgi-09-00306.pdf
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6.63 MB
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Author(s)
Sakamanee, Pitchaya
Phithakkitnukoon, Santi
Smoreda, Zbigniew
Ratti, Carlo
Date Issued
May 7, 2020
Journal
ISPRS International Journal of Geo-Information
Publisher
Multidisciplinary Digital Publishing Institute
Citation
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.
Version
Final published version
Abstract
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
MIT Department
Massachusetts Institute of Technology. Department of Urban Studies and Planning
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Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.3390/ijgi9050306
