Cross-city Few-Shot Traffic Forecasting via Traffic Pattern Bank

TL;DR

This paper introduces a novel cross-city few-shot traffic forecasting framework using a Traffic Pattern Bank, enabling knowledge transfer from data-rich to data-scarce cities to improve forecasting accuracy.

Contribution

It proposes a Traffic Pattern Bank with a pre-trained encoder and clustering, facilitating effective knowledge transfer for traffic forecasting across cities.

Findings

TPB outperforms existing methods on real-world datasets.

The approach effectively transfers knowledge from data-rich to data-scarce cities.

Meta-training with Reptile improves model initialization.

Abstract

Traffic forecasting is a critical service in Intelligent Transportation Systems (ITS). Utilizing deep models to tackle this task relies heavily on data from traffic sensors or vehicle devices, while some cities might lack device support and thus have few available data. So, it is necessary to learn from data-rich cities and transfer the knowledge to data-scarce cities in order to improve the performance of traffic forecasting. To address this problem, we propose a cross-city few-shot traffic forecasting framework via Traffic Pattern Bank (TPB) due to that the traffic patterns are similar across cities. TPB utilizes a pre-trained traffic patch encoder to project raw traffic data from data-rich cities into high-dimensional space, from which a traffic pattern bank is generated through clustering. Then, the traffic data of the data-scarce city could query the traffic pattern bank and explicit relations between them are constructed. The metaknowledge is aggregated based on these relations and an adjacency matrix is constructed to guide a downstream spatial-temporal model in forecasting future traffic. The frequently used meta-training framework Reptile is adapted to find a better initial parameter for the learnable modules. Experiments on real-world traffic datasets show that TPB outperforms existing methods and demonstrates the effectiveness of our approach in cross-city few-shot traffic forecasting.