Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts

TL;DR

This paper introduces MIP, a novel framework that enhances urban flow prediction by using a memory bank to learn invariant features, improving robustness against distribution shifts in spatial-temporal data.

Contribution

The paper proposes Memory-enhanced Invariant Prompt learning (MIP), a new method that adaptively extracts invariant features for urban flow prediction under distribution shifts, with a learnable memory bank and invariant learning.

Findings

MIP outperforms baseline models on two public datasets.

MIP demonstrates robustness against out-of-distribution data.

Memory bank effectively captures causal features for prediction.

Abstract

Urban flow prediction is a classic spatial-temporal forecasting task that estimates the amount of future traffic flow for a given location. Though models represented by Spatial-Temporal Graph Neural Networks (STGNNs) have established themselves as capable predictors, they tend to suffer from distribution shifts that are common with the urban flow data due to the dynamics and unpredictability of spatial-temporal events. Unfortunately, in spatial-temporal applications, the dynamic environments can hardly be quantified via a fixed number of parameters, whereas learning time- and location-specific environments can quickly become computationally prohibitive. In this paper, we propose a novel framework named Memory-enhanced Invariant Prompt learning (MIP) for urban flow prediction under constant distribution shifts. Specifically, MIP is equipped with a learnable memory bank that is trained to memorize the causal features within the spatial-temporal graph. By querying a trainable memory bank that stores the causal features, we adaptively extract invariant and variant prompts (i.e., patterns) for a given location at every time step. Then, instead of intervening the raw data based on simulated environments, we directly perform intervention on variant prompts across space and time. With the intervened variant prompts in place, we use invariant learning to minimize the variance of predictions, so as to ensure that the predictions are only made with invariant features. With extensive comparative experiments on two public urban flow datasets, we thoroughly demonstrate the robustness of MIP against OOD data.