Transportation mode-based segmentation and classification of movement trajectories

Abstract

The knowledge of the transportation mode used by humans (e.g. bicycle, on foot, car and train) is critical for travel behaviour research, transport planning and traffic management. Nowadays, new technologies such as the Global Positioning System have replaced traditional survey methods (paper diaries, telephone) because they are more accurate and problems such as under reporting are avoided. However, although the movement data collected (timestamped positions in digital form) have generally high accuracy, they do not contain the transportation mode. We present in this article a new method for segmenting movement data into single-mode segments and for classifying them according to the transportation mode used. Our fully automatic method differs from previous attempts for five reasons: (1) it relies on fuzzy concepts found in expert systems, that is membership functions and certainty factors; (2) it uses OpenStreetMap data to help the segmentation and classification process; (3) we can distinguish between 10 transportation modes (including between tram, bus and car) and propose a hierarchy; (4) it handles data with signal shortages and noise, and other real-life situations; (5) in our implementation, there is a separation between the reasoning and the knowledge, so that users can easily modify the parameters used and add new transportation modes. We have implemented the method and tested it with a 17-million point data set collected in the Netherlands and elsewhere in Europe. The accuracy of the classification with the developed prototype, determined with the comparison of the classified results with the reference data derived from manual classification, is 91.6%.

Keywords:

• movement trajectory
• GPS track
• travel behaviour research
• OpenStreetMap

Acknowledgements

The authors thank the Department of Urban and Regional Development and the Department of Urbanism at the Delft University of Technology for sharing the test data sets.

Notes

1. With the use of newer devices with a sampling rate of 1s, acceleration could however be used.

2. http://wiki.openstreetmap.org/wiki/3dShapes and https://rejo.zenger.nl/inzicht/aanvullende-informatie-over-het-3d-shapes-bestand (in Dutch).

3. http://wiki.openstreetmap.org/wiki/AND_Data