Urban Functional Areas Identification Based on Crowd Travel Behavior Trajectory

Urban functional areas identification plays an important supporting role in urban planning and management. Most studies mainly rely on images and Point of Interest（POI）distribution data for identification，but mostly mix people with different travel behaviors in the region，without considering the different effects of different groups on the region. Basis that birds of a feather flock together and people flock together，an urban functional areas identification model， UFAI，is constructed to identify the corresponding functional areas by learning the different characteristics of people’s travel activities in different functional areas.Based on large sample，coarse-grained anonymous trajectory data，individual travel characteristics are characterized and extracted，and population types are divided according to individual travel characteristics. A multitask deep learning model is constructed and trained to identify urban functional areas. The 10-month mobile signaling data of twenty million anonymous users in Beijing were selected as the crowd travel trajectory data，calculated by the UFAI model，and compared with seven traditional classification models，such as the decision tree，random forest，and integrated learning gradient boosting classifier.The results show that the F1 value of the UFAI model reaches 0.95，which is improved by 0.10~0.29，compared with the comparison model，and has better recognition performance.