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dc.contributor.authorPitchaya Sakamaneeen_US
dc.contributor.authorSanti Phithakkitnukoonen_US
dc.contributor.authorZbigniew Smoredaen_US
dc.contributor.authorCarlo Rattien_US
dc.description.abstract© 2020 by the authors. Licensee MDPI, Basel, Switzerland. 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.en_US
dc.subjectEarth and Planetary Sciencesen_US
dc.subjectSocial Sciencesen_US
dc.titleMethods for inferring route choice of commuting trip from mobile phone network dataen_US
article.title.sourcetitleISPRS International Journal of Geo-Informationen_US
article.volume9en_US Labsen_US Institute of Technologyen_US Mai Universityen_US
Appears in Collections:CMUL: Journal Articles

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