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DGCPath: Distribution-Aware Generative Contrastive Framework for Self-supervised Path Representation Learning -- Extended Version
Sean Bin Yang, Hao Miao, Zongyi Xu, Jilin Hu, Xiangmeng Wang, Hua Lu, Bin Yang, Christian S. Jensen
TL;DR
Path representation learning is constrained by deterministic contrastive objectives and handcrafted view augmentations, motivating a more transferable approach. DGCPath combines diffusion-generated views, distribution-level contrastive alignment, and generative cross-supervision, outperforming state-of-the-art methods across three datasets and two downstream tasks.
Problem
Existing self-supervised path representation methods rely on deterministic contrastive learning and handcrafted view augmentations, limiting cross-scenario generalization.
Method
DGCPath combines a diffusion-based view generator, distribution-aware variational contrastive learning, and generative cross-supervision for path representation learning.
Results
DGCPath consistently outperforms state-of-the-art methods across three real-world datasets and two downstream tasks.
Takeaways & Limitations
The framework yields more generalizable path representations and improved downstream-task performance.
Takeaways & Limitations
Self-reconstruction may allow the encoder to preserve view-specific or coordinate-dependent information.
Abstract
from arXiv · showhide
Due to the proliferation of vehicle trajectory data enabled by advanced sensing technologies, path representation learning has become a pivotal task in intelligent transportation systems. Although existing self-supervised approaches have achieved promising performance, their dependence on deterministic contrastive learning paradigms and handcrafted view augmentation strategies inherently restricts their cross-scenario generalization capabilities. To address these limitations, we present DGCPath, an innovative Distribution-aware Generative Contrastive learning framework for Path representation. This framework establishes a synergistic connection between generative modeling and distributional contrastive learning, enabling the acquisition of robust and transferable feature embeddings. Specifically, our framework incorporates: (1) a diffusion-based view generator that autonomously produces semantically coherent yet diverse trajectory views from Gaussian noise; (2) a variational contrastive mechanism that enforces latent feature alignment at the distribution level, transcending conventional instance-wise consistency; and (3) a novel generative cross-supervision module that reinforces view-level consistency through cross-view reconstruction learning. Comprehensive evaluations on three real-world trajectory datasets demonstrate that DGCPath outperforms state-of-the-art baselines on two distinct downstream tasks, validating its enhanced generalization capability and representation effectiveness.
1 Introduction
Path representation learning is important for intelligent transportation analysis, but deterministic contrastive objectives and handcrafted augmentations limit representation flexibility and transferability. DGCPATH addresses these constraints by combining diffusion-based view generation with distributional and generative contrastive learning, and achieves consistent gains across datasets and tasks.
- Vehicle path representations encode raw trajectories as compact vectors that support traffic analysis, path recommendation, and traffic prediction.
- Existing methods rely on deterministic contrastive samples and pre-defined augmentations, which restrict representation expressiveness, adaptability, and cross-task transfer.
- DGCPATH models path representations as latent probability distributions and aligns distributions rather than fixed instances to learn smoother, uncertainty-aware embeddings.
- Its diffusion-based view generator produces diverse, semantically consistent path views from noise without manually designed augmentation heuristics.
- The framework combines distributional contrastive learning with generative cross-supervision to align views of the same path and separate different paths.
- DGCPATH consistently outperforms state-of-the-art methods across three real-world datasets and two downstream tasks in representation quality and downstream performance.
2 Related Work
Prior path representation learning work primarily studies single-modality path sequences and develops embeddings for downstream intelligent transportation applications.
- Existing path representation methods mostly focus on single-modality path-sequence data.
3 Preliminaries
The preliminaries define road networks as directed graphs and paths as topologically continuous ordered node sequences, then formulate representation learning as mapping paths to latent vectors.
- A road network is modeled as a directed graph G = (V, E), with nodes representing intersections and directed edges representing road segments.
- A path is an ordered sequence of connected nodes whose consecutive nodes are linked by directed edges.
- The objective is to learn a function fθ that maps each path to a d-dimensional representation vector in latent space.
4 Methodology
DGCPath combines distributional contrastive learning with generative cross-supervision to learn path representations that are view-invariant, path-discriminative, and semantically informative. Its theoretical analysis explains how the objectives suppress view-specific information, separate different paths, and preserve shared semantics.
- Distributional Contrastive Learning: DGCPath aligns latent distributions of positive path views while separating distributions from different paths.The objective uses small JS divergence for positive pairs and large JS divergence for negative pairs.
- Invariant Feature Learning via Generative Cross-Supervision: Generative cross-supervision encourages reconstructions from one view to preserve semantic information shared with another view.Its trainable reconstruction component can be reduced by improving representation of the shared semantic component S.
- Invariant Feature Learning via Generative Cross-Supervision: Unlike self-reconstruction, cross-supervision removes the shortcut of exploiting view-specific nuisance factors.The nuisance factor in one view is not predictive of the nuisance factor in the other view, discouraging augmentation-specific or coordinate-dependent information.
- Overall Theoretical Interpretation: Jointly optimizing the proposed objectives yields representations that are simultaneously view-invariant, path-discriminative, and semantically informative.The theoretical interpretation assigns complementary roles to distributional alignment, distributional contrast, and generative cross-supervision.
- View Invariance via Distributional Alignment: Minimizing positive-pair JS divergence suppresses augmentation-specific information and reduces prediction discrepancy across views.The analysis connects distributional alignment to conditional view invariance and an upper bound on view-dependent prediction differences.
- Path Discriminability via Distributional Contrast: The negative contrastive term enlarges the margin between positive and negative path pairs, improving latent-distribution discriminability.As the margin increases, the corresponding prediction-error upper bound decreases exponentially.
5 Experiments
Experiments evaluate path representations on three real-world datasets across travel time estimation and path ranking, using unsupervised pre-training followed by supervised regression.
- Datasets: Three datasets—Aalborg, Chengdu, and Harbin—provide road networks and GPS trajectories for evaluation.The road networks contain 10,017, 6,632, and 8,497 nodes, respectively.
- Downstream Tasks: The study evaluates travel time estimation and path ranking as downstream tasks.Travel time estimation uses MAE, MAPE, and RMSE; path ranking assigns preference scores to candidate paths.
- Baselines: Ten baselines span six unsupervised and four supervised path-representation methods.Several unsupervised baselines rely on predefined augmentations such as random masking or node dropping.
- Implementation: All baseline representations are set to 128 dimensions, with optimization conducted using AdamW and a cosine-decay schedule over 600 epochs.The schedule includes a 50-epoch warm-up period.
5.1 Performance Findings
DGCPath achieves the strongest reported downstream performance across all three datasets and both tasks, with diffusion-based generation and distribution-aware contrastive learning identified as contributing factors.
- Overall Results: DGCPath achieves the best performance across all baselines on both downstream tasks and all three datasets.The evaluation covers travel time estimation and path ranking.
- Travel Time Estimation: 13.27% and 2.21% are the reported maximum reductions in Aalborg travel-time MAE and RMSE, respectively.These figures concern DGCPath’s comparison with the evaluated baselines.
- Path Ranking: 4.66% and 6.94% are the reported maximum increases in Harbin Kendall’s τ and Spearman’s ρ, respectively.These figures concern the path-ranking task.
- Interpretation: DGCPath’s performance is attributed to integrating a diffusion-based view generator with distribution-aware contrastive learning.The combination is described as capturing spatial structure and representation diversity.
5.2 Ablation Study
Ablation results indicate that all three major components contribute to DGCPath, with the variational contrastive module having the largest reported impact on generalizable representations.
- Component Contributions: Removing any of DVG, VCM, or GCSM causes performance to drop on both downstream tasks.The ablations evaluate the diffusion-based view generator, variational contrastive module, and generative cross-supervision module.
- Component Contributions: The VCM has a more critical role than DVG and GCSM in learning generalizable path representations.The reported impact varies by downstream task.
- Component Contributions: DVG and GCSM provide complementary contributions to the overall model.Removing either component also degrades performance.
- Pre-training Effects: With DGCPath pre-training, PathRank matches the 12K-label from-scratch performance using 8K, 9K, and 9K labels on Aalborg, Chengdu, and Harbin.These figures are reported for travel time estimation.
- Pre-training Effects: PathRank with pre-training outperforms its non-pretrained counterpart when both use the full 12K labeled samples.The comparison is reported after transferring DGCPath’s learned encoder parameters.
5.4 Path Recommendation
The path recommendation evaluation extends DGCPATH testing to recommend user-chosen paths among alternatives, while parameter studies examine temperature, embedding dimensionality, and diffusion depth.
- Path Recommendation: Path recommendation labels the user-taken trajectory as 1 and alternative paths with the same origin and destination as 0.Performance is evaluated using Accuracy, Precision, and F1-score, with higher values indicating better recommendation quality.
- Temperature: The parameter study finds the best performance at temperature t = 0.5.A moderately warm temperature balances contrastive-objective sharpness, mitigating overly confident similarity assignments and improving optimization stability and representation quality.
- Embedding Dimensionality: Embedding dimensionality performs best at d = 128, providing a favorable balance between representation capacity and overfitting.
- Diffusion Step: DGCPATH achieves the best performance when the diffusion step is T = 1000.Smaller diffusion depths may provide insufficient perturbation, whereas excessively large depths can introduce unnecessary noise that degrades representation quality.
5.6 Effect of Dynamic Traffic State
The dynamic traffic state experiment adds temporal traffic information to DGCPATH and reports further performance improvement.
- Dynamic Traffic State: Incorporating dynamic traffic states further improves DGCPATH performance.The variant DGCPATH w/ DTS explicitly considers temporal traffic information constructed from time-varying traffic-flow sequences.
5.7 Model Scalability
The scalability analysis evaluates DGCPATH with different training-data proportions and finds consistently better performance as more training data is used.
- Training Data Size: DGCPATH shows consistent performance improvement as the training-data proportion increases from 20% to 100%.The analysis uses the Aalborg dataset and compares training proportions of 20%, 40%, 60%, 80%, and 100%.
5.8 Robustness Analysis
The robustness analysis evaluates noisy path representations and reports that DGCPATH remains robust while outperforming comparison methods.
- Noisy Representations: DGCPATH consistently outperforms comparison methods under noisy representations through a smaller performance drop percentage.Gaussian perturbations, including σ = 0.03, are injected into path representations before training a regressor on the Aalborg and Chengdu datasets.
6 Conclusion
DGCPATH combines learnable view generation, distribution-aware contrastive learning, and generative cross-supervision to improve the generalizability of path representations across downstream tasks.
- DGCPATH generates two path views with a learnable diffusion-based generator, aligns positive-pair distributions while separating positive and negative pairs, and applies generative cross-supervision.