AutoPRL: Automated Path Representation Learning via a Learnable View Generator
Authors: Sean Bin Yang, Ying Sun, Yunyao CHeng, Hao Miao, Jilin Hu, Bin Yang, Torben Bach Pedersen, Christian S. Jensen
Organizations: Aalborg University, Denmark. · Chongqing University of Posts and Telecommunications, China. · East China Normal University, Shanghai, China.
Self-supervised learning has emerged as a powerful paradigm for Path Representation Learning (PRL) in road networks. PRL aims to learn informative representations of paths traversed by moving objects, such as vehicles, where each path consists of a sequence of connected road segments. These representations are essential for various downstream tasks, including travel-time estimation and path ranking. However, existing methods often suffer from ineffective view generation and inefficient training procedures due to their reliance on heuristic augmentations and computationally intensive contrastive learning. To address these issues, we propose AutoPRL, an effective and efficient self-supervised PRL framework that integrates three key components. The Automatic Data Augmentation (AutoDA) module employs variational autoencoders to generate semantically meaningful and diverse feature-level path views by modeling each topology-aware node embedding with a Gaussian latent distribution, thereby mitigating information loss and enhancing representation quality. Second, the Cross-View Decoupled Contrastive (CVDC) learning mechanism improves training efficiency by disentangling the optimization of positive and negative pairs across node- and path-level views, enabling more stable contrastive learning. Third, an Adaptive Co-Training (ACT) module dynamically adjusts uncertainty-based loss weights to balance multiple objectives, ensuring efficient and scalable training. Extensive experiments on three real-world datasets demonstrate that AutoPRL achieves strong and consistent performance in both effectiveness and efficiency across multiple downstream tasks.
Figures & tables
Fig. 1: Architectures of the conventional contrastive paradigm and our AutoPRL for path representation learning. In (a), heuristic augmentation directly perturbs the discrete path sequence. In (b), AutoDA generates feature-level views as continuous embedding sequences from topology-aware node embeddings, rather than discrete node sequences on the road-network graph. Contrast denotes the contrastive loss.
Fig. 2: An example of heuristic path view generation.
Fig. 3: Solution Overview.
Fig. 4: Overall architecture of AutoPRL . The framework consists of three modules: AutoDA for automatic path view generation, CVDC for contrastive representation learning with cross-path and cross-node objectives, and ACT for adaptive multi-objective optimization.
Fig. 5: Automatic path view generation in AutoDA . The input path is first mapped into topology-aware spatial embeddings. Two VNAEs then encode the path into hidden representations, from which MLPs parameterize Gaussian latent distributions. By sampling from these distributions, AutoDA generates two semantically consistent feature-level views, pi(1) and pi(2) .
Fig. 6: Illustration of the progress-based prior. The approximate posterior is aligned with a dynamic Gaussian prior through KL regularization. During training, the mean of the prior gradually shifts from μs1 to μsT , which improves the alignment between the prior and the evolving latent space.
Method
Aalborg
Chengdu
Harbin
MAE ↓
MAPE (%) ↓
RMSE ↓
MAE ↓
MAPE (%) ↓
RMSE ↓
MAE ↓
MAPE (%) ↓
RMSE ↓
Supervised
PathRank
2.913
18.713
4.832
5.802
34.014
8.894
4.127
14.339
5.301
HierETA
3.097
18.620
5.058
5.060
28.820
7.921
4.897
16.506
5.961
CompactETA
3.030
19.726
4.791
4.832
28.678
7.447
4.372
14.974
5.362
HMTRL
3.515
19.955
5.792
4.861
28.529
7.473
4.704
15.543
5.803
Unsupervised
Node2vec
5.701
35.485
8.165
5.997
35.919
8.653
5.399
18.709
6.773
TABLE I: Overall performance on travel time estimation. The best results are highlighted in bold, and the second-best results are underlined.
Method
Aalborg
Chengdu
Harbin
MAE ↓
TAU ( τ ) ↑
RHO ( ρ ) ↑
MAE ↓
TAU ( τ ) ↑
RHO ( ρ ) ↑
MAE ↓
TAU ( τ ) ↑
RHO ( ρ ) ↑
Supervised
PathRank
0.159
0.727
0.751
0.123
0.703
0.742
0.145
0.821
0.832
HierETA
0.152
0.757
0.767
0.178
0.735
0.774
0.133
0.846
0.879
CompactETA
0.169
0.698
0.709
0.195
0.661
0.705
0.160
0.796
0.833
HMTRL
0.163
0.729
0.741
0.149
0.779
0.789
0.133
0.838
0.874
Unsupervised
Node2vec
0.253
0.632
0.637
0.246
0.464
0.505
0.211
0.468
0.512
TABLE II: Overall performance on path ranking. The best results are highlighted in bold, and the second-best results are underlined.
Fig. 7: Effect of Pre-training.
Method
Chengdu
Harbin
10K
30K
50K
60K
80K
100K
Toast
5.233
7.254
9.283
7.674
9.231
11.643
PIM
4.107
6.788
8.328
5.962
7.479
9.896
START
2.765
4.371
5.287
4.102
5.997
8.122
LightPath
2.431
3.289
5.364
3.562
5.296
7.432
RED
1.453
1.578
1.690
2.590
3.102
3.457
TABLE III: Evaluation on Most Similar Path Search Task.
Method
Aalborg
Chengdu
Acc.
Pre.
F1
Acc.
Pre.
F1
Toast
0.692
0.711
0.738
0.774
0.785
0.806
PIM
0.722
0.739
0.762
0.785
0.799
0.813
START
0.751
0.770
0.787
0.804
0.826
0.853
LightPath
0.752
0.768
0.790
0.817
0.832
0.849
RED
0.785
0.811
0.830
0.844
0.860
0.879
TABLE IV: Evaluation on Path Recommendation Task.
Method
Aalborg
Chengdu
R@3
R@5
MAP
R@3
R@5
MAP
Toast
0.656
0.705
0.697
0.770
0.797
0.800
PIM
0.679
0.716
0.663
0.792
0.837
0.845
START
0.708
0.725
0.692
0.834
0.860
0.863
LightPath
0.714
0.756
0.736
0.830
0.855
0.823
RED
0.733
0.778
0.746
0.862
0.881
0.863
TABLE V: Evaluation on Path Imputation Task.
Fig. 8: Ablation Study.
Fig. 9: Effect of Temperature t
Fig. 10: Scalability Performance vs. Cardinality
Fig. 11: (a) Model training time (in minutes) per epoch; (b) model inference time (in ms) per path; and (c) – (e) training convergence of AutoPRL on three datasets.
Method
Travel Time Estimation
Path Ranking
Aalborg
Chengdu
Aalborg
Chengdu
MAE
MAPE
RMSE
MAE
MAPE
RMSE
MAE
TAU ( τ )
RHO ( ρ )
MAE
TAU ( τ )
RHO ( ρ )
AutoPRL-DGM
1.976
12.901
3.396
4.683
27.447
7.091
0.079
0.870
0.878
0.112
0.821
0.830
AutoPRL-GMGP
1.971
12.867
3.382
4.672
27.318
7.074
0.073
0.890
0.893
0.103
0.836
0.845
TABLE VI: Ablation study on travel-time estimation and path ranking under different generative modeling paradigms.
Fig. 12: Effect of Embedding Dimension.
Training Epochs
Aalborg
Harbin
AutoPRL-SimCLR
AutoPRL-DCLW
AutoPRL-SimCLR
AutoPRL-DCLW
16
32
64
128
16
32
64
128
16
32
64
128
16
32
64
128
400
2.844
2.657
2.539
2.340
2.112
2.044
1.971
1.954
–
–
–
–
–
–
–
–
500
2.435
2.216
2.161
2.064
–
–
–
–
6.158
5.725
5.384
5.008
4.001
3.964
3.883
3.775
600
–
–
–
–
–
–
–
–
5.574
4.977
4.472
4.258
–
–
–
–
700
–
–
–
–
–
–
–
–
4.476
4.433
4.212
3.979
–
–
–
–
TABLE VII: Batch-size Sensitivity Analysis on Travel-Time Estimation Comparing AutoPRL-SimCLR and AutoPRL-DCLW .
Method
Travel Time Estimation
Path Ranking
Aalborg
Chengdu
Aalborg
Chengdu
MAE
MAPE
RMSE
MAE
MAPE
RMSE
MAE
TAU ( τ )
RHO ( ρ )
MAE
TAU ( τ )
RHO ( ρ )
Toast
3.439
21.311
5.555
4.918
28.567
7.513
0.138
0.768
0.779
0.131
0.734
0.752
Toast w/ noise
4.426
21.906
6.066
5.622
29.134
8.233
0.207
0.672
0.683
0.206
0.692
0.703
PIM
3.471
21.314
5.640
4.924
28.553
7.493
0.141
0.764
0.777
0.125
0.725
0.745
PIM w/ noise
4.003
21.904
5.982
5.427
28.994
7.89
0.192
0.727
0.738
0.169
0.674
0.685
TABLE VIII: Performance on Travel-Time Estimation and Path Ranking under Noisy Representations.
Fig. 13: Case study of path view generation. We compare the original path, a random dropping view, and an AutoDA -generated view that is projected onto the road network only for visualization. Random dropping may remove important intermediate nodes and break local path continuity, while AutoDA better preserves the overall route semantics.
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.
Sean Bin Yang, Hao Miao, Zongyi Xu +5
Aalborg University, Denmark · The Hong Kong Polytechnic University, Hong Kong · Chongqing Univeristy of Posts and Telecommunications, China +1
Path representation learning (PRL) for road networks has received increasing research attention, due to various path-related applications. Existing works on PRL typically exploit the co-occurrence relationship among road segments and paths to learn a vector as the path representation, without exploring the varied traveler behaviors and the regional correlation on the path. In this work, we propose to learn distributional representations, which provide valuable information for use in path-related applications, by capturing the varied traveler behaviors as well as the various dependencies within regions of road segments. We propose a novel Transformer-based Multi-view Distributional Representation Learning (TraveL) framework to encode a path along with a travel starting time to a distributional representation, which can be used to decode possible samples of on-path traveler behavior. Moreover, by analyzing the regional correlation which reveals various road segment relationships, we propose a regional attention to encode these correlations in a path. Also, we explore the idea of Kolmogorov-Smirnov (K-S) test to compare the sampled traveler behavior against the collected ground truth to facilitate training. Experimental results show that the proposed TraveL model outperforms the state-of-the-art methods on both synthetic and real-world datasets, by 14.7% in Mean K-S distance for travel time distribution estimation, 16.7% in Mean Absolute Error (MAE) for path similarity prediction, and 3.97% in MAE for destination prediction.
Trajectory representation learning underpins a wide range of trajectory analytics tasks; however, most existing self-supervised approaches, whether discriminative or generative, adopt an open-loop paradigm, relying on fixed data augmentations or random masking without feedback, which limits their ability to generalize and scale. We propose REFINE, a simple yet effective Representation lEarning Framework vIa closed-loop traNscription rEfinement for trajectory data. Drawing upon feedback control theory, REFINE tightly couples road-network-aware generative reconstruction with feedback-driven contrastive learning, enabling the model to capture fine-grained local movement semantics and global spatio-temporal dependencies without manually designed augmentation views. We further provide a control-theoretic analysis that establishes convergence guarantees for the proposed closed-loop optimization. Extensive experiments on four real-world datasets demonstrate that REFINE consistently outperforms state-of-the-art methods across multiple downstream tasks while remaining computationally efficient and scalable. This paper is an extended version of REFINE: Trajectory Representation Learning via Closed-Loop Transcription, to appear in KDD 2026.
Sean Bin Yang, Ying Sun, Jilin Hu +5
Aalborg University Aalborg, Denmark · Chongqing Universtiy of Posts and Telecommunications Chongqing, China · East China Normal University Shanghai, China +1