Efflex: Efficient and Flexible Pipeline for Spatio-Temporal Trajectory Graph Modeling and Representation Learning

TL;DR

Efflex addresses the challenge of learning dense representations from massive spatio-temporal trajectories by constructing graphs directly from trajectories using a multi-scale KNN scheme with feature fusion to form a weighted adjacency $S$, where $s_{ij} = \frac{e^{dist(T_i, T_j)}}{\sum_{T_k \in \mathcal{K}} e^{dist(T_i, T_k)}}$ with $dist$ drawn from Fréchet, Hausdorff, or DTW, and then learning embeddings via a lightweight Graph Convolutional Network ($Efflex-B$) or a parameter-rich node2vec ($Efflex-L$). On Porto and Geolife, Efflex achieves state-of-the-art representation learning across multiple distance measures, while Efflex-B provides up to 36x faster embedding extraction with competitive accuracy. The framework is designed for both real-time, edge deployments and large-scale, compute-heavy settings, and points to future extensions such as incorporating Large Language Models as encoders for richer graph features.

Abstract

In the landscape of spatio-temporal data analytics, effective trajectory representation learning is paramount. To bridge the gap of learning accurate representations with efficient and flexible mechanisms, we introduce Efflex, a comprehensive pipeline for transformative graph modeling and representation learning of the large-volume spatio-temporal trajectories. Efflex pioneers the incorporation of a multi-scale k-nearest neighbors (KNN) algorithm with feature fusion for graph construction, marking a leap in dimensionality reduction techniques by preserving essential data features. Moreover, the groundbreaking graph construction mechanism and the high-performance lightweight GCN increase embedding extraction speed by up to 36 times faster. We further offer Efflex in two versions, Efflex-L for scenarios demanding high accuracy, and Efflex-B for environments requiring swift data processing. Comprehensive experimentation with the Porto and Geolife datasets validates our approach, positioning Efflex as the state-of-the-art in the domain. Such enhancements in speed and accuracy highlight the versatility of Efflex, underscoring its wide-ranging potential for deployment in time-sensitive and computationally constrained applications.

Figures (5)

• Figure 1: The proposed Efflex pipeline. We offer two models, Efflex-B and Efflex-L, to learn accurate embeddings from the original high-volume trajectory data. Efflex-B specializes in improving the speed while Efflex-L focuses on obtaining state-of-the-art performance, indicating different applications for each model.
• Figure 2: Overview of the Efflex pipeline.Pipeline Train: We build the graph from original trajectory data using multi-scale KNN algorithms with feature fusion by an attention module. The adjacent matrix and node features are then input into a lightweight GCN (Efflex-B) / node2vec grover2016node2vec (Efflex-L) for accurate embedding learning. Efflex-B specializes in improving the speed significantly while Efflex-L embraces state-of-the-art performance. Pipeline Test: We conduct the top-k trajectory search experiment where given a query trajectory, the model outputs the top-k similar ones. The precise search results indicate Efflex's ability to learn high-quality representations of the original data.
• Figure 3: Visualization of edge connection weights.Figure (a) - (c): Edge connection weights obtained by single-scale KNN ($k$ = 10, 20, 50). Figure (d): Connection weights fused by multi-scale KNN with the attention mechanism.
• Figure 4: Qualitative visualization of the trajectory similarity search task.Left: Query trajectory. Right (a):Top-3 similar ground truth trajectories. Right (b):Top-3 similar retrieval results of our model (Efflex-B). Our retrieval results are consistent with ground truth.
• Figure 5: Ablation study on different structures and embedding dimensions under three distances on Porto. We compare the results of single-scale KNN ($k$=10,20,50), multi-scale KNN (with different fusion strategies), and different output embedding dimensions.