Analysis and Prediction of Regional Mobility Patterns of Bus Travellers Using Smart Card Data and Points of Interest Data

Mobility patterns at region level can provide more macroscopic and intuitive knowledge on how people gather in or depart from the region. However, the analysis and prediction of regional mobility patterns have yet to be effectively addressed. In light of this, using smart card data (SCD) and points of interest (POI) data, a multi-step methodology which integrates the inner-restricted fuzzy C-means clustering, nonnegative tensor factorization and artificial neural network are proposed and implemented in this paper. It overcomes the difficulties in region division, pattern extraction, and prediction. The bus SCD and POI data in Beijing city are utilized for proving the usefulness of the methodology. The regional mobility patterns of bus travellers in Beijing city are extracted from the third-order tensors involving 1110 regions, 34 time slots, and 7 days of the week. The analyzed results show that the proposed methodology has a good performance on predicting the regional mobility patterns based on the regional properties. Furthermore, by considering both of the regional boarding and alighting patterns, the predictions of the regional aggregation pattern can also be achieved. These research achievements can not only provide a deep insight on the human mobility patterns at region level, but also support the evidence-based and forward-looking urban planning and intelligent transportation management.