A Driving Cycle Detection Approach Using Map Service API

Research output: Contribution to journalArticlepeer-review

14 Scopus Citations


Following advancements in smartphone and portable global positioning system (GPS) data collection, wearable GPS data have realized extensive use in transportation surveys and studies. The task of detecting driving cycles (driving or car-mode trajectory segments) from wearable GPS data has been the subject of much research. Specifically, distinguishing driving cycles from other motorized trips (such as taking a bus) is the main research problem in this paper. Many mode detection methods only focus on raw GPS speed data while some studies apply additional information, such as geographic information system (GIS) data, to obtain better detection performance. Procuring and maintaining dedicated road GIS data are costly and not trivial, whereas the technical maturity and broad use of map service application program interface (API) queries offers opportunities for mode detection tasks. The proposed driving cycle detection method takes advantage of map service APIs to obtain high-quality car-mode API route information and uses a trajectory segmentation algorithm to find the best-matched API route. The car-mode API route data combined with the actual route information, including the actual mode information, are used to train a logistic regression machine learning model, which estimates car modes and non-car modes with probability rates. The experimental results show promise for the proposed method's ability to detect vehicle mode accurately.

Original languageAmerican English
Pages (from-to)349-363
Number of pages15
JournalTransportation Research Part C: Emerging Technologies
StatePublished - 2018

Bibliographical note

Publisher Copyright:
© 2018 Elsevier Ltd

NREL Publication Number

  • NREL/JA-5400-68508


  • Driving cycle detection
  • Map service API
  • Travel mode detection
  • Wearable GPS data


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