A driving cycle detection approach using map service API

• Published in: Transportation research. Part C, Emerging technologies Vol. 92; no. C; pp. 349 - 363
• Main Authors: Zhu, Lei, Gonder, Jeffrey D.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.07.2018; Elsevier
• Subjects: ADVANCED PROPULSION SYSTEMS; Driving cycle detection; Map service API; Travel mode detection; Wearable GPS data; Travel mode detection; Driving cycle detection; Map service API; Wearable GPS data
• ISSN: 0968-090X; 1879-2359
• DOI: 10.1016/j.trc.2018.05.010

•The driving cycle detection leverages map service API and does need GIS data.•A trajectory segmentation algorithm finding the best-matched car mode API route.•A logistic regression built by selected features provides an accurate prediction. 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.