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Title: A Driving Cycle Detection Approach Using Map Service API

Abstract

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 showmore » promise for the proposed method's ability to detect vehicle mode accurately.« less

Authors:
ORCiD logo [1]; ORCiD logo [1]
  1. National Renewable Energy Laboratory (NREL), Golden, CO (United States)
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Vehicle Technologies Office (EE-3V); USDOE Office of Energy Efficiency and Renewable Energy (EERE), NREL Laboratory Directed Research and Development (LDRD)
OSTI Identifier:
1452690
Report Number(s):
NREL/JA-5400-68508
DOE Contract Number:
AC36-08GO28308
Resource Type:
Journal Article
Resource Relation:
Journal Name: Transportation Research Part C: Emerging Technologies; Journal Volume: 92
Country of Publication:
United States
Language:
English
Subject:
33 ADVANCED PROPULSION SYSTEMS; wearable GPS data; map service API; travel mode detection; driving cycle detection

Citation Formats

Zhu, Lei, and Gonder, Jeffrey D. A Driving Cycle Detection Approach Using Map Service API. United States: N. p., 2018. Web. doi:10.1016/j.trc.2018.05.010.
Zhu, Lei, & Gonder, Jeffrey D. A Driving Cycle Detection Approach Using Map Service API. United States. doi:10.1016/j.trc.2018.05.010.
Zhu, Lei, and Gonder, Jeffrey D. Sat . "A Driving Cycle Detection Approach Using Map Service API". United States. doi:10.1016/j.trc.2018.05.010.
@article{osti_1452690,
title = {A Driving Cycle Detection Approach Using Map Service API},
author = {Zhu, Lei and Gonder, Jeffrey D},
abstractNote = {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.},
doi = {10.1016/j.trc.2018.05.010},
journal = {Transportation Research Part C: Emerging Technologies},
number = ,
volume = 92,
place = {United States},
year = {Sat May 26 00:00:00 EDT 2018},
month = {Sat May 26 00:00:00 EDT 2018}
}