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An Optimization Framework for Travel Pattern Interpretation of Cellular Data
Collection of travel data by traditional survey methods is costly, thus limiting the amount of data being collected, as well as their frequency and coverage. Recent technologies offer new types of data collection options. In particular, cellular systems generate substantial amounts of data, including records regarding the connection between handsets (phones) and base stations (antenna). These records, collected by cellular service providers for various internal purposes, may provide an excellent source of information regarding travel, with several critical advantages relative to traditional travel surveys: low cost, large sample, long duration, and high response rate. Cellular data has some limitations too, particularly with respect to accuracy and traveler identity; therefore, such data cannot provide a complete replacement for traditional surveys, but it can complement and enhance them. This paper explores methods for identifying travel patterns from cellular data. A primary challenge in this research is to provide an interpretation of the raw data that distinguishes between activity durations and travel durations. A novel framework is proposed for this purpose, based on a grading scheme for candidate interpretations of the raw data. A genetic algorithm is used to find interpretations with high grades, which are considered as the most reasonable ones. The proposed method is tested on a dataset of records covering 9454 cell-phone users over a period of one week. Preliminary evaluation of the resulting interpretations is presented.
An Optimization Framework for Travel Pattern Interpretation of Cellular Data
Collection of travel data by traditional survey methods is costly, thus limiting the amount of data being collected, as well as their frequency and coverage. Recent technologies offer new types of data collection options. In particular, cellular systems generate substantial amounts of data, including records regarding the connection between handsets (phones) and base stations (antenna). These records, collected by cellular service providers for various internal purposes, may provide an excellent source of information regarding travel, with several critical advantages relative to traditional travel surveys: low cost, large sample, long duration, and high response rate. Cellular data has some limitations too, particularly with respect to accuracy and traveler identity; therefore, such data cannot provide a complete replacement for traditional surveys, but it can complement and enhance them. This paper explores methods for identifying travel patterns from cellular data. A primary challenge in this research is to provide an interpretation of the raw data that distinguishes between activity durations and travel durations. A novel framework is proposed for this purpose, based on a grading scheme for candidate interpretations of the raw data. A genetic algorithm is used to find interpretations with high grades, which are considered as the most reasonable ones. The proposed method is tested on a dataset of records covering 9454 cell-phone users over a period of one week. Preliminary evaluation of the resulting interpretations is presented.
An Optimization Framework for Travel Pattern Interpretation of Cellular Data
Sarit Freund (author) / Hillel Bar-Gera (author)
2013
Article (Journal)
Electronic Resource
Unknown
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