Title: TransferTraj: A Vehicle Trajectory Learning Model for Region and Task Transferability
Venue: NeurIPS 2025
Accepted: yes
Keywords: Spatio-temporal data mining

trajectory data mining

vehicle trajectory learning
Abstract: Vehicle GPS trajectories provide valuable movement information that supports various downstream tasks and applications. A desirable trajectory learning model should be able to transfer across regions and tasks without retraining, avoiding the need to maintain multiple specialized models and subpar performance with limited training data. However, each region has its unique spatial features and contexts, which are reflected in vehicle movement patterns and are difficult to generalize. Additionally, transferring across different tasks faces technical challenges due to the varying input-output structures required for each task. Existing efforts towards transferability primarily involve learning embedding vectors for trajectories, which perform poorly in region transfer and require retraining of prediction modules for task transfer.

To address these challenges, we propose $\textit{TransferTraj}$, a vehicle GPS trajectory learning model that excels in both region and task transferability. For region transferability, we introduce RTTE as the main learnable module within TransferTraj. It integrates spatial, temporal, POI, and road network modalities of trajectories to effectively manage variations in spatial context distribution across regions. It also introduces a TRIE module for incorporating relative information of spatial features and a spatial context MoE module for handling movement patterns in diverse contexts. For task transferability, we propose a task-transferable input-output scheme that unifies the input-output structure of different tasks into the masking and recovery of modalities and trajectory points. This approach allows TransferTraj to be pre-trained once and transferred to different tasks without retraining. We conduct extensive experiments on three real-world vehicle trajectory datasets under various transfer settings, including task transfer, zero-shot region transfer, and few-shot region transfer. Experimental results demonstrate that TransferTraj significantly outperforms state-of-the-art baselines in different scenarios, validating its effectiveness in region and task transfer. Code is available at https://github.com/wtl52656/TransferTraj.


Peer reviews:
comments: SUMMARY:
TransferTraj proposes a unified trajectory-learning framework that simultaneously enables region-level and task-level transfer. Without retraining, the mask-and-recover pre-training objective can be applied to three generative downstream tasks—trajectory prediction, trajectory recovery, and OD travel-time estimation. Experiments on Chengdu, Xi’an, and Porto taxi datasets show that TransferTraj outperforms strong baselines, confirming both its effectiveness and scalability.

STRENGTHS AND WEAKNESSES:
Strengths

(1) The authors tackle both region and task transferability in a single framework, reducing the need to train and maintain multiple region- or task-specific models.

(2) Extensive experiments provide strong empirical evidence for the ability of TransferTraj.

(3) A unified masking-and-recovery pre-training scheme enables zero-shot usage on three heterogeneous generative tasks without re-training prediction heads.



Weaknesses

(1) The evaluation of TP task only focuses on the precision of the trajectories’ destinations and ignores the intermediate trajectory, which weakens the claim of accurate trajectory prediction.

(2) The unified input and output scheme of three tasks is pivotal to the task transferability, which is just stated in Appendix. Without an explicit, worked example in the main body, it will hurt clarity and reproducibility.

QUESTIONS:
See weakness.

LIMITATIONS:
Yes
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-06-26

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis article proposes TransferTraj, a deep learning-based architecture designed to facilitate transfer across regions and tasks in the context of trajectory prediction. The model addresses domain discrepancies that arise between different geographic regions and related tasks, such as prediction, recovery, and travel time estimation. To achieve this, it introduces the Region-Transferable Trajectory Encoder (RTTE), which encodes spatial and temporal information from trajectories. RTTE includes two key components: TRIE, which captures relative spatial relationships between trajectory points, and SC-MoE, a mechanism that detects movement patterns based on local context by leveraging similarities in urban or road environments. Additionally, the article proposes a task-transfer scheme based on masking and recovering trajectory modalities or points. The proposed approach is evaluated on three real-world datasets and compared against multiple baseline methods, showing improvements in accuracy and generalization capability.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n- The article addresses a real and relevant problem: the generalization of trajectory models to new regions and tasks.\n- It proposes an architecture that integrates key components to improve transferability across regions and tasks, combining several established deep learning techniques such as mixture-of-experts, attention mechanisms, and masking + recovery strategies. Additionally, the use of a rotation matrix to capture spatiotemporal information is a novel and valuable idea.\n- Multiple baseline models are considered for comparison, which supports the empirical evaluation of the proposed method.\n\nWeaknesses:\n- Although several baselines are used for comparison, the paper does not specify how they were trained or tuned. It is not clear whether pre-trained weights were reused or whether hyperparameters were fairly adjusted, which raises concerns about the fairness of the comparisons.\n- The paper lacks implementation details such as the number of layers, embedding size, batch size, and task-specific learning rates. These aspects are only superficially mentioned in the appendices and not explained in the main text.\n\nQUESTIONS:\nIn the proposed method, there are some important implementation details that remain unclear. For instance, in the definition of the POI modality: ” The POI modality $Pi = {l | dis(l, pi) ≤ \\underset{\\text{dist}}{\\varphi^{\\text{poi}}}}$ is defined as the set of all POIs lying within a distance $\\underset{\\text{dist}}{\\varphi ^{\\text{poi}}}$ of the trajectory point $pi$.”\nWhat specific distance metric is used for dis(l, pi)? Is it the Haversine distance, Euclidean, or another geodesic measure appropriate for geographic coordinates?\n\nIn the same context, $\\underset{\\text{dist}}{\\varphi ^{\\text{poi}}}$ and $\\underset{\\text{dist}}{\\varphi ^{\\text{road}}}$ are introduced as distance thresholds for selecting POIs and road segments around each trajectory point. However, it is not explained how these hyperparameters were selected. Were these values fixed across all datasets, or tuned for each region individually? What was the rationale or methodology behind the selection of these thresholds?\n\nRegarding the Transformer-based multimodal architecture, it is unclear whether the model was trained entirely from scratch or built upon a pre-existing encoder. Could the authors clarify whether a standard Transformer (e.g., RoFormer, ViT, etc.) was adapted, or whether the encoder was implemented from the ground up? Additionally, how many layers and attention heads are used in the RTTE module?\n\nAlthough Figure 1 provides a schematic overview of the proposed architecture, its connection to the textual description is limited. I suggest aligning the narrative more explicitly with the labeled components of the figure as the model is described. In particular, the integration of the four modalities (spatial, temporal, POI, road network) is conceptually important but currently somewhat unclear. A more detailed mapping between the components in the figure and their function in the pipeline would greatly improve clarity.\n\nLIMITATIONS:\nThe paper presents certain technical limitations, particularly regarding the lack of detailed experimentation and insufficient information on the training and comparison of baseline models. For further discussion, please refer to the “Weaknesses” and “Questions” sections.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-26"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents an innovative vehicle trajectory learning framework that supports both region and task transfer. It addresses the limitations of existing embedding-based methods, which usually require retraining when applied to new regions or tasks. The model uses four modalities and two key modules TRIE and SC-MoE to capture relative spatial relationships and adapt to different movement patterns across regions. The authors also propose a unified input-output scheme that reformulates various tasks as a masking and recovery problem, making it possible to pretrain the model in a task-agnostic way. Experiments show that TransferTraj performs exceptionally well on three real-world datasets.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1.The paper presents an interesting topic and introduces a novel unified framework that supports both region and task transfer within a single model, without requiring retraining. The proposed RTTE module and the task-transferable input-output scheme are both innovative and effective. Together, they enable the model to generalize well to other regions, different spatial contexts, and various trajectory-related tasks.\n\n2.The authors provide a theoretical derivation to show that the proposed module is effective in capturing the relative spatial relationships between trajectory points.\n\n3.The paper is well-structured and logically organized. It clearly explains the motivation and challenges of learning relative spatial information, capturing dynamic movement patterns, and designing a unified input-output format.\n\n4.The author conducts comprehensive experiments on zero-shot, few-shot, and task transfer scenarios across multiple tasks and datasets, proving that the proposed models can outperform the SOTA. The code and data are publicly available, which ensures reproducibility.\n\nOverall, I think this paper offers an interesting and novel solution for transferable vehicle trajectory learning, but a few weaknesses can be highlighted:\n\nWeakness:\n\n1.Theoretically, trajectory representation learning models can also achieve task transferability by attaching task-specific heads. What are the key advantages of TransferTraj compared to these methods?\n\nSince POIs and road network structures vary significantly across regions, the authors should further clarify how these two modalities are encoded.\n\nQUESTIONS:\n1.Compared to traditional trajectory representation learning models, what are the core advantages of TransferTraj in terms of task transferability?\n\n2.Why can the POI and road network modalities generalize across different regions, given that their spatial distributions and semantics may differ?\n\n3. In the task-transferable input-output scheme, how are the masking rates for modalities and trajectory points determined?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 4, "DATE": "2025-06-29"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes TransferTraj, a novel vehicle trajectory learning model designed for both transferability across regions and downstream tasks. The author introduces RTTE and spatial, temporal, POI, and road network modalities to address region transfer by modeling relative spatial relationships using a rotary position matrix and dynamically routing trajectories to experts based on local spatial context. Additionally, the paper introduces the unified input and output structures for various tasks via modality and points masking and recovery, enabling task transfer after pre-training. Extensive experiments on three real-world datasets show SOTA performance in task transfer and zero-shot region transfer.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1.The paper introduces a novel problem formulation and is the first to incorporate both regional and task transferability into a single model, laying the foundation for the development of a trajectory base model.\n\n2.This paper presents a well-designed and technically sound architecture. The TRIE and SC-MoE modules effectively handle spatial feature differences and regional context distributions. A unified pre-training strategy enables multi-task learning without introducing additional parameters.\n\n3.Experiments are comprehensive and rigorous, covering three tasks, three datasets, and diverse transfer scenarios. Ablation and efficiency studies further validate the model’s effectiveness and lightweight design.\n\n4.The paper is well-structured and clearly articulates its research objectives, methodology, and conclusions.\n\nWeakness:\n1.Although the authors claim that the TRIE module addresses the challenge of varying trajectory lengths across regions, they do not provide a clear explanation of how this is achieved. Additionally, the paper lacks an analysis of trajectory length distribution differences across datasets to support this claim.\n\n2.The author terms the top-K gating mechanism \"prevent the movement patterns from being represented by the same set of experts\", however, it does not explain why or how this mechanism successfully enforces expert diversity.\n\n3.There are some inconsistencies in the writing. For example, the term \"trajectory modality\" is used interchangeably as \"modality\" and \"feature\" throughout the paper. The masked spatial and temporal modalities ( p^{ms} and p^{mt} in Section 4.2) are not clearly illustrated in the figures.\n\nQUESTIONS:\n1.The experiments are conducted with k=4 and C=8, but Figure 2 shows that the number of activated experts in different-density regions mostly concentrates around 2, 3, and 4. Does this suggest that some experts may be redundant?\n\n2.When the sampling rate is set to $\\mu=16\\epsilon$, both the proposed model and the baseline experience a significant increase in error. What might be the underlying cause of this phenomenon?\n\n3.In the pre-training section, how are the lengths of the masked start and end segments defined? how is the modality mask of trajectory points determined?\n\nThe TRIE module is designed to address generalization with varying trajectory lengths. What is the distribution of trajectory lengths across different regions? Is the model sensitive to trajectory length or sampling frequency?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-01"}