Transportation mode inference from anonymized and aggregated mobile phone call detail records

Abstract

Transportation mode inference is an important research direction and has many applications. Existing methods are usually based on fine-grained sampling - collecting position data from mobile devices at high frequency. These methods can achieve high accuracy, but also incur cost and complexity in terms of the computational resource and system implementation. Finally, fine-grained sampling is not always available, especially for large-scale deployment. This paper proposes a novel method to infer transportation mode based on coarse-grained call detail records. The method allows estimating the transportation mode share from a given origin to a given destination, looking also at how the share changes over time. The method can achieve acceptable accuracy with trivial cost and complexity. It is suitable for the statistical analysis on transportation modes of a large population. The method can also be used as a complementary tool in situations where fine-grained sampling is unavailable or the balance between accuracy and complexity is critical. A case study using real call detail records data for the city of Boston shows the performance of the proposed method.