ACA-Net: Future Graph Learning for Logistical Demand-Supply Forecasting
This addresses forecasting inefficiencies for on-demand food delivery platforms, though it appears incremental as it builds on existing graph-based approaches.
The paper tackles logistical demand-supply forecasting for on-demand food delivery by proposing a spatiotemporal model that uses only two graphs (ongoing and global) to learn future order distribution, achieving superior performance compared to traditional long-series methods.
Logistical demand-supply forecasting that evaluates the alignment between projected supply and anticipated demand, is essential for the efficiency and quality of on-demand food delivery platforms and serves as a key indicator for scheduling decisions. Future order distribution information, which reflects the distribution of orders in on-demand food delivery, is crucial for the performance of logistical demand-supply forecasting. Current studies utilize spatial-temporal analysis methods to model future order distribution information from serious time slices. However, learning future order distribution in online delivery platform is a time-series-insensitive problem with strong randomness. These approaches often struggle to effectively capture this information while remaining efficient. This paper proposes an innovative spatiotemporal learning model that utilizes only two graphs (ongoing and global) to learn future order distribution information, achieving superior performance compared to traditional spatial-temporal long-series methods. The main contributions are as follows: (1) The introduction of ongoing and global graphs in logistical demand-supply pressure forecasting compared to traditional long time series significantly enhances forecasting performance. (2) An innovative graph learning network framework using adaptive future graph learning and innovative cross attention mechanism (ACA-Net) is proposed to extract future order distribution information, effectively learning a robust future graph that substantially improves logistical demand-supply pressure forecasting outcomes. (3) The effectiveness of the proposed method is validated in real-world production environments.