STDFN: A Taxi Flow Prediction Model Based on Spatial-Temporal Data Fusion

The prediction of taxi flow has significant research value for the optimization of overall traffic management, enhancing urban transportation efficiency, effectively reducing carbon emission, as well as improving the convenience for both passengers and drivers. It is manifest that the complexity of taxi flow data is not merely due to its inherent spatial-temporal correlation, but also impacted by various external multi-source spatial-temporal data. Despite numerous existing research efforts attempting to capture the spatial-temporal characteristics of taxi flow prediction, there are scarce results in feature integration harvested from multi-source spatial-temporal data. Hence, this research mainly explores the relationship between different multi-source spatial -temporal data and taxi flow, aiming to convert these data into external spatial-temporal correlation impact factors. Subsequently, a specially designed spatial-temporal data fusion model, Spatial-Temporal Data Fusion Network (STDFN), is employed, which categorizes these various external spatial-temporal impact factors into spatial and temporal impact factors according to their specific characteristics. Spatial and temporal influence factors are respectively processed through graph convolution and temporal convolution for feature extraction and fusion. By comparing the experimental results on real-world data with the baseline model established in this study, rigorous testing on multiple TaxiBJ datasets demonstrated an average improvement of 13.340/0 across several evaluation metrics. Furthermore, the proposed method excelled in long-term prediction tasks, showcasing higher accuracy and robustness.