- The paper introduces MetaST, a novel model using meta-learning to improve spatial-temporal prediction accuracy across cities.
- It combines CNNs and LSTMs with a spatial-temporal memory module to capture both spatial dependencies and temporal patterns.
- Experiments on taxi, bike, and water quality data demonstrate significant error reductions over standard transfer learning methods.
Meta-Learning for Spatial-Temporal Predictions: A Multi-City Approach
This paper presents a meta-learning strategy for enhancing spatial-temporal predictions across cities, addressing the challenge of data scarcity in smart city objectives such as traffic management and environmental monitoring. The authors tackle the common issue where some urban areas have extensive datasets while others have limited temporal data. They propose utilizing longer-term datasets from multiple cities to bolster prediction capabilities in these data-scarce areas through transfer learning. Unlike traditional transfer learning, which typically relies on data from a single source city, this research leverages multiple sources to improve transfer stability and reduce the risks associated with data variation and biased predictions.
Methodology
The paper introduces a novel model named MetaST, which combines a spatial-temporal network (ST-net) with a meta-learning paradigm. The ST-net consists of convolutional neural networks (CNNs) for spatial dependency and long short-term memory networks (LSTMs) for capturing temporal dynamics within urban regions. The major advancement in this research lies in the application of meta-learning to spatial-temporal networks, allowing the model to create a well-generalized initialization from multiple source tasks, thus enabling easy adaptation to target cities. An added layer, the pattern-based spatial-temporal memory, is used to encapsulate and transfer long-term temporal information like periodicity.
Results
Extensive experiments conducted on datasets for taxi and bike traffic prediction, as well as water quality prediction, show that MetaST consistently outperforms several competitive baseline models. When evaluating taxi volume predictions in cities like Chicago and Boston, and bike volume predictions in Chicago, the approach demonstrated significant error reductions compared to non-transfer methods and other transfer learning techniques. In water quality predictions, the model maintained its edge, illustrating versatility across different domains of spatial-temporal data.
Implications and Future Directions
The practical implications of this research are broad, impacting urban planning and management systems by providing more reliable data analytics tools for smart cities. Theoretically, this paper contributes to the development of meta-learning techniques in non-traditional domains such as spatial-temporal prediction. The successful application of meta-learning and the leveraging of multi-city data source can be extended to other fields where data insufficiency challenges exist.
Looking forward, the integration of more complex city structures like road networks through graph neural networks could enhance the ST-net's capability further. Additionally, exploring the transferability of specific features within urban metadata could improve understanding of intra-city variations and enhance model specificity. As this work exemplifies a promising intersection of meta-learning and urban computing, similar methodological expansions could find applications in environmental science, infrastructure management, and broader smart city initiatives.