# Learning from Multiple Cities: A Meta-Learning Approach for Spatial-Temporal Prediction

arXiv: https://arxiv.org/abs/1901.08518 · HTML: https://mlsanity.com/paper/1901.08518/

## Key facts

- Cited by 64 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jan 2019.

## Built on by (most cited first, 30 of 64)

- [Spatio-Temporal Meta-Graph Learning for Traffic Forecasting](https://mlsanity.com/paper/2211.14701/) (2022)
- [UniST: A Prompt-Empowered Universal Model for Urban Spatio-Temporal Prediction](https://mlsanity.com/paper/2402.11838/) (2024)
- [Few-Shot Graph Learning for Molecular Property Prediction](https://mlsanity.com/paper/2102.07916/) (2021)
- [UrbanGPT: Spatio-Temporal Large Language Models](https://mlsanity.com/paper/2403.00813/) (2024)
- [Spatio-Temporal Graph Few-Shot Learning with Cross-City Knowledge Transfer](https://mlsanity.com/paper/2205.13947/) (2022)
- [Domain Adversarial Spatial-Temporal Network: A Transferable Framework for Short-term Traffic Forecasting across Cities](https://mlsanity.com/paper/2202.03630/) (2022)
- [OpenCity: Open Spatio-Temporal Foundation Models for Traffic Prediction](https://mlsanity.com/paper/2408.10269/) (2024)
- [Binocular Mutual Learning for Improving Few-shot Classification](https://mlsanity.com/paper/2108.12104/) (2021)
- [COLA: Cross-city Mobility Transformer for Human Trajectory Simulation](https://mlsanity.com/paper/2403.01801/) (2024)
- [Cross-city Few-Shot Traffic Forecasting via Traffic Pattern Bank](https://mlsanity.com/paper/2308.09727/) (2023)
- [Towards Urban General Intelligence: A Review and Outlook of Urban Foundation Models](https://mlsanity.com/paper/2402.01749/) (2024)
- [Disentangling Structured Components: Towards Adaptive, Interpretable and Scalable Time Series Forecasting](https://mlsanity.com/paper/2305.13036/) (2023)
- [Semi-Supervised Graph-to-Graph Translation](https://mlsanity.com/paper/2103.08827/) (2021)
- [Event-Aware Multimodal Mobility Nowcasting](https://mlsanity.com/paper/2112.08443/) (2021)
- [Spatio-Temporal Few-Shot Learning via Diffusive Neural Network Generation](https://mlsanity.com/paper/2402.11922/) (2024)
- [Graph Neural Networks for Road Safety Modeling: Datasets and Evaluations for Accident Analysis](https://mlsanity.com/paper/2311.00164/) (2023)
- [STUaNet: Understanding uncertainty in spatiotemporal collective human mobility](https://mlsanity.com/paper/2102.06027/) (2021)
- [Multimodality in Meta-Learning: A Comprehensive Survey](https://mlsanity.com/paper/2109.13576/) (2021)
- [Rethinking Sensors Modeling: Hierarchical Information Enhanced Traffic Forecasting](https://mlsanity.com/paper/2309.11284/) (2023)
- [A Contextual Master-Slave Framework on Urban Region Graph for Urban Village Detection](https://mlsanity.com/paper/2211.14633/) (2022)
- [Prompt-Based Spatio-Temporal Graph Transfer Learning](https://mlsanity.com/paper/2405.12452/) (2024)
- [Dynamic Spatiotemporal Graph Convolutional Neural Networks for Traffic Data Imputation with Complex Missing Patterns](https://mlsanity.com/paper/2109.08357/) (2021)
- [Quantifying Spatial Homogeneity of Urban Road Networks via Graph Neural Networks](https://mlsanity.com/paper/2101.00307/) (2021)
- [Doing More with Less: Overcoming Data Scarcity for POI Recommendation via Cross-Region Transfer](https://mlsanity.com/paper/2201.06095/) (2022)
- [Deep trip generation with graph neural networks for bike sharing system expansion](https://mlsanity.com/paper/2303.11977/) (2023)
- [Multi-scale Traffic Pattern Bank for Cross-city Few-shot Traffic Forecasting](https://mlsanity.com/paper/2402.00397/) (2024)
- [LITE: Modeling Environmental Ecosystems with Multimodal Large Language Models](https://mlsanity.com/paper/2404.01165/) (2024)
- [Traffic Prediction with Transfer Learning: A Mutual Information-based Approach](https://mlsanity.com/paper/2303.07184/) (2023)
- [NodeTrans: A Graph Transfer Learning Approach for Traffic Prediction](https://mlsanity.com/paper/2207.01301/) (2022)
- [Entity Aware Modelling: A Survey](https://mlsanity.com/paper/2302.08406/) (2023)

## Abstract (arXiv)

Spatial-temporal prediction is a fundamental problem for constructing smart city, which is useful for tasks such as traffic control, taxi dispatching, and environmental policy making. Due to data collection mechanism, it is common to see data collection with unbalanced spatial distributions. For example, some cities may release taxi data for multiple years while others only release a few days of data; some regions may have constant water quality data monitored by sensors whereas some regions only have a small collection of water samples. In this paper, we tackle the problem of spatial-temporal prediction for the cities with only a short period of data collection. We aim to utilize the long-period data from other cities via transfer learning. Different from previous studies that transfer knowledge from one single source city to a target city, we are the first to leverage information from multiple cities to increase the stability of transfer. Specifically, our proposed model is designed as a spatial-temporal network with a meta-learning paradigm. The meta-learning paradigm learns a well-generalized initialization of the spatial-temporal network, which can be effectively adapted to target cities. In addition, a pattern-based spatial-temporal memory is designed to distill long-term temporal information (i.e., periodicity). We conduct extensive experiments on two tasks: traffic (taxi and bike) prediction and water quality prediction. The experiments demonstrate the effectiveness of our proposed model over several competitive baseline models.