STA: Enhancing Spatio-temporal Crowd Flow Prediction Using Attention-based Deep Learning and Feature Similarity

STA: Enhancing Spatio-temporal Crowd Flow Prediction Using Attention-based Deep Learning and Feature Similarity

연구 분야: Verification

학회: International Conference on Advanced Data Mining and Applications

Crowd flow prediction has significant implications for public safety, transportation resource scheduling, and urban transportation planning. This problem involves forecasting future crowd flow by analyzing spatio-temporal features from historical data. In this paper, we present an attention-based deep learning model called STA for city crowd flow prediction. By utilizing the attention mechanism, STA effectively extracts spatio-temporal features from historical data, with its effectiveness confirmed through the MCTP trend evaluation index. Through contrast learning, we demonstrate STA’s ability to effectively capture spatio-temporal dependencies in historical data. Extensive experiments on two real-world datasets highlight STA’s advancements. It exceeds the state-of-the-art baseline, demonstrating a performance improvement of 6%-12%.