TourSense: A Framework for Tourist Identification and Analytics Using Transport Data

We advocate for and present TourSense, a framework for tourist identification and preference analytics using city-scale transport data (bus, subway, etc.). Our work is motivated by the observed limitations of utilizing traditional data sources (e.g., social media data and survey data) that commonly suffer from the limited coverage of tourist population and unpredictable information delay. TourSense demonstrates how the transport data can overcome these limitations and provide better insights for different stakeholders, typically including tour agencies, transport operators, and tourists themselves. Specifically, we first propose a graph-based iterative propagation learning algorithm to recognize tourists from public commuters. Taking advantage of the trace data from the identified tourists, we then design a tourist preference analytics model to learn and predict their next tour, where an interactive user interface is implemented to ease the information access and gain the insights from the analytics results. Experiments with real-world datasets (from over 5.1 million commuters and their 462 million trips) show the promise and effectiveness of the proposed framework: the Macro and Micro F1 scores of the tourist identification system achieve 0.8549 and 0.7154, respectively, whereas the tourist preference analytics system improves the baselines by at least 23.53 and 11.44 percent in terms of precision and recall.