Abstract
Many applications in transport planning require an understanding of travel patterns separated by travel mode. To use cellular network data as observations of human mobility in these applications, classification by travel mode is needed. Existing classification methods for GPS-trajectories are often inefficient for cellular network data, which has lower resolution in space and time than GPS data.In this study, we compare three geometry-based mode classification methods and three supervised methods to classify trips extracted from cellular network data in intercity origin-destination pairs as either road or train. To understand the difficulty of the problem, we use a labeled dataset of 255 trips in two OD-pairs to train the supervised classification methods and to evaluate the classification performance. For an OD-pair where the road and train routes are not separated by more than four kilometers, the geometry-based methods classify 4.5% - 7.1% of the trips wrong, while two of the supervised methods can classify all trips correctly. Using a large-scale dataset of 29037 trips, we find that separation between classes is less evident than in the labeled dataset and show that the choice of classification methods impacts the aggregated modal split estimate.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.