Integrating trajectory data and demographic characteristics: a trajectory semantic model for predicting travel flow and conducting interaction analysis

With urbanisation and population growth, understanding spatial interactions in cities is increasingly vital for urban management. In recent decades, spatial interactions could be predicted accurately with the support of large GPS data, but anonymous trajectory data lacks semantic details, limiting predictions and behaviour understanding. To address this, we proposed a Semantic-Integrated Mobility Trajectory Model (SMTM), integrating social media check-in data, remote sensing imagery, and taxi trajectory data capable of accurately predict travel flow. Specifically, Convolutional Neural Networks (CNN) and Long Short Term Memory (LSTM) extract demographic insights, Graph Convolutional Networks (GCN) and Gate Recurrent Units (GRU) are incorporated to predict spatial interaction intensity. We conducted two case studies in New York City, U.S., and Ningbo, China, using taxi trips (over three million trips in New York and nearly one million trips in Ningbo) and social media check-in data (around 60,000 records for each city). Results demonstrate excellent performance over baselines. Furthermore, the integration of travel trajectories and census data revealed diverse travel preferences at various scales, including intra-region, inter-region, and inter-urban. The SMTM model contributes to optimising the design of public spaces and personalised recommendations.