Deriving Public Transportation Timetables with Large-Scale Cell Phone Data | Zendy

Christopher Horn | Zendy; Roman Kern | Zendy

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Deriving Public Transportation Timetables with Large-Scale Cell Phone Data

Author(s) -

Christopher Horn,

Roman Kern

Publication year - 2015

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2015.05.026

Subject(s) - computer science , phone , public transport , global positioning system , scale (ratio) , work (physics) , mode (computer interface) , data mining , operations research , transport engineering , telecommunications , philosophy , linguistics , physics , quantum mechanics , engineering , mechanical engineering , operating system

In this paper, we propose an approach to deriving public transportation timetables of a region (i.e. country) based on (i) large- scale, non-GPS cell phone data and (ii) a dataset containing geographic information of public transportation stations. The presented algorithm is designed to work with movements data, which are scarce and have a low spatial accuracy but exists in vast amounts (large-scale). Since only aggregated statistics are used, our algorithm copes well with anonymized data. Our evaluation shows that 89% of the departure times of popular train connections are correctly recalled with an allowed deviation of 5minutes. The timetable can be used as feature for transportation mode detection to separate public from private transport when no public timetable is available

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