Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts (2412.05534v1)

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.

• The paper presents a memory-enhanced framework that extracts invariant features via a learnable memory bank to improve urban flow predictions under distribution shifts.
• It employs semantic graph construction and an intervention mechanism to effectively separate invariant and variant prompts for enhanced model robustness.
• Experimental results on public urban datasets demonstrate superior accuracy and resilience compared to current state-of-the-art prediction methods.

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

The paper "Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts" presents an innovative approach to address the challenges associated with urban flow prediction tasks, particularly when dealing with Out-of-Distribution (OOD) data. The authors propose a framework known as Memory-enhanced Invariant Prompt learning (MIP), designed to enhance the robustness of Spatial-Temporal Graph Neural Networks (STGNNs) against distribution shifts that typically occur in spatial-temporal urban flow data.

Urban flow prediction, a critical task for smart city applications, involves forecasting various traffic-related metrics such as vehicle and pedestrian flow. Despite the effectiveness of STGNNs, they often falter under the conditions of distributional shifts due to dynamic environmental factors. This paper introduces a novel framework that effectively utilizes invariant patterns from data to improve OOD generalization.

Methodology

At the core of the MIP is a learnable memory bank trained to store and retrieve causal (invariant) features from spatial-temporal graphs. This memory bank allows the model to query and extract both invariant and variant prompts—essentially patterns—specific to a location and a time step. Unlike traditional approaches that may require environments to be artificially simulated to induce data shifts, MIP circumvents this by intervening directly on the variant prompts at various spatial and temporal junctures.

The MIP model comprises several components:

• Memory Bank and Semantic Graph Construction: The memory bank encapsulates the causal features, which are then used to construct a semantic adjacency matrix that aids in feature propagation.
• Invariant and Variant Prompt Extraction: Using the memory bank, the framework extracts invariant and variant prompts which are processed through a specially designed querying process, enhancing the ability to distinguish between truly causal features and spurious patterns.
• Invariant Learning and Intervention Mechanisms: The model includes an intervention mechanism that swaps features between nodes to simulate diverse data distributions, thereby enabling an effective separation of invariant and variant patterns.

Results

The authors validate the MIP framework through experiments on public urban flow datasets. These experiments reveal that MIP demonstrates superior robustness and prediction accuracy in comparison to existing state-of-the-art methods. Numerical benchmarks show that MIP consistently outperforms traditional methods, especially in scenarios characterized by significant distribution shifts.

Implications and Future Directions

The approach outlined in the paper offers several implications for both theoretical exploration and practical applications. Theoretically, MIP's design could be extended to develop more generalized models capable of handling various types of OOD challenges across different spatial-temporal domains. Practically, it provides city planners with improved tools for effective traffic and urban management under uncertain and dynamic conditions.

Future research could explore extending memory-enhanced learning techniques to other domains where distribution shifts pose substantial challenges. Another potential area for exploration is optimizing the computational efficiency of such models to ensure real-time application feasibility.

In conclusion, this paper contributes a robust methodology to urban flow prediction tasks. The proposed MIP framework addresses the critical issue of distribution shifts, offering a methodology that significantly enhances the practicality and resilience of urban predictive models in real-world settings.