Activity location recognition from mobile phone data using improved HAC and Bi‐LSTM

Existing studies on activity location recognition based on mobile phone data has made great progresses. However, current studies generally assume constant distance threshold when performing activity location clustering, and ignore the influence of base station layout on positioning accuracies of mobile phone data. Given different recognition accuracy requirements, the authors propose two methods to recognise activity locations: (1) An improved hierarchical agglomerative clustering algorithm that integrates a genetic algorithm component to search and dynamically adjust optimal distance thresholds based on base station densities; (2) The recognition method based on Bi-directional long short-term memory network that classifies travel statuses of mobile phone traces. Results show that, compared with existing methods, the activity location recognition accuracy of the proposed hierarchical agglomerative clustering algorithm increases by about 5%. The Bi-directional long short-term memory network model further outperforms the improved hierarchical agglomerative clustering, especially in the aspect of recognising non-commuting activity locations. However, the Bi-directional long short-term memory network model training requires the users’ actual travel information, so there are certain obstacles in popularising Bi-directional long short-term memory network in practice.