Semi-supervised Mode Classification of Inter-city Trips from Cellular Network Data

Abstract

Abstract Good knowledge of travel patterns is essential in transportation planning. Cellular network data as a large-scale passive data source provides billions of daily location updates allowing us to observe human mobility with all travel modes. However, many transport planning applications require an understanding of travel patterns separated by travel mode, requiring the classification of trips by travel mode. Most previous studies have used rule-based or geometric classification, which often fails when the routes for different modes are similar or supervised classification, requiring labelled training trips. Sufficient amounts of labelled training trips are unfortunately often unavailable in practice. We propose semi-supervised classification as a novel approach of classifying large sets of trips extracted from cellular network data in inter-city origin–destination pairs as either using road or rail. Our methods require no labelled trips which is an important advantage as labeled data is often not available in practice. We propose three methods which first label a small share of trips using geometric classification. We then use structures in a large set of unlabelled trips using a supervised classification method (geometric-labelling), iterative semi-supervised training (self-labelling) and by transferring information between origin–destination pairs (continuity-labelling). We apply the semi-supervised classification methods on a dataset of 9545 unlabelled trips in two inter-city origin–destination pairs. We find that the methods can identify structures in the cells used during trips in the unlabelled data corresponding to the available route alternatives. We validate the classification methods using a dataset of 255 manually labelled trips in the two origin–destination pairs. While geometric classification misclassifies 4.2% and 5.6% of the trips in the two origin–destination pairs, all trips can be classified correctly using semi-supervised classification.

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