01659939

Component

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.

This paper was sponsored by TRB committee ABJ60 Standing Committee on Geographic Information Science and Applications.

18-00236

English

8p

2018

Transportation Research Board 97th Annual Meeting

Digital/other

Figures; References; Tables

Algorithms; Application programming interfaces; Automatic vehicle detection and identification systems; Automobile driving; Detection and identification technologies; Geographic information systems; Global Positioning System; Logistic regression analysis; Smartphones; Traffic data; Transportation modes; Travel patterns; Vehicle trajectories

Wearable computers

Data and Information Technology; Highways; Planning and Forecasting; Vehicles and Equipment

Transportation Research Board Annual Meeting 2018 Paper #18-00236

TRIS, TRB, ATRI

Jan 8 2018 10:04AM