Multi-scale Traffic Pattern Bank for Cross-city Few-shot Traffic Forecasting

TL;DR

This paper introduces MTPB, a novel framework that leverages multi-scale traffic pattern banks and cross-city knowledge transfer to improve traffic forecasting in cities with limited data, demonstrating superior performance on real datasets.

Contribution

The paper proposes a multi-scale traffic pattern bank and a cross-city transfer learning approach for few-shot traffic forecasting, addressing data scarcity in target cities.

Findings

MTPB outperforms existing methods on real-world datasets.

The approach effectively transfers knowledge across cities.

The framework enhances forecasting accuracy in data-scarce scenarios.

Abstract

Traffic forecasting is crucial for intelligent transportation systems (ITS), aiding in efficient resource allocation and effective traffic control. However, its effectiveness often relies heavily on abundant traffic data, while many cities lack sufficient data due to limited device support, posing a significant challenge for traffic forecasting. Recognizing this challenge, we have made a noteworthy observation: traffic patterns exhibit similarities across diverse cities. Building on this key insight, we propose a solution for the cross-city few-shot traffic forecasting problem called Multi-scale Traffic Pattern Bank (MTPB). Primarily, MTPB initiates its learning process by leveraging data-rich source cities, effectively acquiring comprehensive traffic knowledge through a spatial-temporal-aware pre-training process. Subsequently, the framework employs advanced clustering techniques to systematically generate a multi-scale traffic pattern bank derived from the learned knowledge. Next, the traffic data of the data-scarce target city could query the traffic pattern bank, facilitating the aggregation of meta-knowledge. This meta-knowledge, in turn, assumes a pivotal role as a robust guide in subsequent processes involving graph reconstruction and forecasting. Empirical assessments conducted on real-world traffic datasets affirm the superior performance of MTPB, surpassing existing methods across various categories and exhibiting numerous attributes conducive to the advancement of cross-city few-shot forecasting methodologies. The code is available in https://github.com/zhyliu00/MTPB.