Travel mode estimation for multi-modal journey planner
place - europe, place - urban, mode - bike, mode - bus, mode - car, mode - pedestrian, technology - geographic information systems, technology - passenger information
Travel mode detection, Bayes classifier, Neural network, Feature selection
For route planning and tracking, it is sometimes necessary to know if the user is walking or using some other mode of transport. In most cases, the GPS data can be acquired from the user device. It is possible to estimate user’s transportation mode based on a GPS trace at a sampling rate of once per minute. There has been little prior work on the selection of a set of features from a large number of proposed features, especially for sparse GPS data. This article considers characteristics of distribution, auto- and cross-correlations, and spectral features of speed and acceleration as possible features, and presents an approach to selecting the most significant, non-correlating features from among those. Both speed and acceleration are inferred from changes in location and time between data points. Using GPS traces of buses in the city of Tampere, and of walking, biking and driving from the OpenStreetMap and Microsoft GeoLife projects, spectral bins were found to be among the most significant non-correlating features for differentiating between walking, bicycle, bus and driving, and were used to train classifiers with a fair accuracy. Auto- and cross-correlations, kurtoses and skewnesses were found to be of no use in the classification task. Useful features were found to have a fairly large (>0.4) correlation with each other.
Permission to publish the abstract has been given by Elsevier, copyright remains with them.
Mäenpää, H., Lobov, A., & Lastra, J.L.M. (2017). Travel mode estimation for multi-modal journey planner. Transportation Research Part C: Emerging Technologies, Vol. 82, pp. 273-289.
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