Assessing the role of geographic context in transportation mode detection from GPS data
The increasing availability of health monitoring devices and smartphones has created an opportunity for researchers to access high-resolution (spatial and temporal) mobility data for understanding travel behavior in cities. Although information from GPS data has been used in several studies to detect transportation modes, there is a research gap in understanding the role of geographic context in transportation mode detection. Integrating the geography in which mobility occurs, provides context clues that may allow models predicting transportation modes to be more generalizable. The authors goals are first, to develop a data-driven modeling framework for transportation mode detection using GPS mobility data along with geographic context, and second, to assess how model accuracy and generalizability varies upon adding geographic context. To this extent they extracted features from raw GPS mobility data (speed, altitude, turning angle and net displacement) and integrated context in the form of geographic features to classify active (i.e. walk/bike), public (i.e. bus/train), and private (i.e. car) transportation modes in three different Canadian cities - Montreal, St. Johns, and Vancouver. To assess the role of integrating geographic context in mode detection, they adopted two different modeling approaches – generalized and context-specific, and compared results using random forests, extreme gradient boost, and multilayer perceptron classifiers. Their results indicate that for context-specific models the highest classification accuracy improved by 64% for Montreal, by 74% for St. John's and by 77% for Vancouver compared to the generalized model. They also found that the multilayer perceptron (96%) achieved the highest classification accuracy upon adding contextual variables compared to random forests (94.6%) and extreme gradient boost (93.3%) classifier. The authors' study highlights that adding contextual information specific to a city's geography can improve the predictive accuracy of transportation mode detection models, however, in case of limited knowledge about the geographic setting of a study area, a generalized model combining GPS data from several cities may still be useful for predicting modes from trip data.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/09666923
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Supplemental Notes:
- © 2022 Avipsa Roy et al. Published by Elsevier Ltd. Abstract reprinted with permission of Elsevier.
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Authors:
- Roy, Avipsa
- Fuller, Daniel
- Nelson, Trisalyn
- Kedron, Peter
- Publication Date: 2022-4
Language
- English
Media Info
- Media Type: Web
- Features: Appendices; Figures; References; Tables;
- Pagination: 103330
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Serial:
- Journal of Transport Geography
- Volume: 100
- Issue Number: 0
- Publisher: Elsevier
- ISSN: 0966-6923
- Serial URL: http://www.elsevier.com/locate/jtrangeo
Subject/Index Terms
- TRT Terms: Data; Geography; Global Positioning System; Mobility; Nonmotorized transportation; Travel modes
- Geographic Terms: Canada
- Subject Areas: Data and Information Technology; Highways; Pedestrians and Bicyclists; Public Transportation;
Filing Info
- Accession Number: 01847330
- Record Type: Publication
- Files: TRIS
- Created Date: May 26 2022 9:47AM