Using Map Service API for Driving Cycle Detection for Wearable GPS Data: Preprint
- National Renewable Energy Laboratory (NREL), Golden, CO (United States)
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.
- Research Organization:
- National Renewable Energy Lab. (NREL), Golden, CO (United States)
- Sponsoring Organization:
- USDOE; U.S. Department of Transportation; Transportation Secure Data Center (TSDC) Project
- DOE Contract Number:
- AC36-08GO28308
- OSTI ID:
- 1412835
- Report Number(s):
- NREL/CP-5400-70474
- Resource Relation:
- Conference: To be presented at the Transportation Research Board (TRB) 97th Annual Meeting, 7-11 January 2018, Washington, D.C.
- Country of Publication:
- United States
- Language:
- English
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