Title: PLMTrajRec: A Scalable and Generalizable Trajectory Recovery Method with Pre-trained Language Models
Venue: NeurIPS 2025
Accepted: yes
Keywords: Spatio-temporal data mining

trajectory data mining

vehicle trajectory recovery
Abstract: Spatiotemporal trajectory data is crucial for various traffic-related applications. However, issues such as device malfunctions and network instability often result in sparse trajectories that lose detailed movement information compared to their dense counterparts. Recovering missing points in sparse trajectories is thus essential. Despite recent progress, three challenges remain. First, the lack of large-scale dense trajectory datasets hinders the training of a trajectory recovery model. Second, the varying spatiotemporal correlations in sparse trajectories make it hard to generalize across different sampling intervals. Third, extracting road conditions for missing points is non-trivial.

To address these challenges, we propose $\textit{PLMTrajRec}$, a novel trajectory recovery model. It leverages the scalability of a pre-trained language model (PLM) and can effectively recover trajectories by fine-tuning with small-scale dense trajectory datasets. To handle different sampling intervals in sparse trajectories, we first convert sampling intervals and movement features into prompts for the PLM to understand. We then introduce a trajectory encoder to unify trajectories of varying intervals into a single interval. To extract road conditions for missing points, we propose an area flow-guided implicit trajectory prompt that represents traffic conditions in each region, and a road condition passing mechanism that infers the road conditions of missing points using the observed ones. Experiments on four public trajectory datasets with three sampling intervals demonstrate the effectiveness, scalability, and generalization ability of PLMTrajRec. Code is available at https://github.com/wtl52656/PLMTrajRec.


Peer reviews:
comments: SUMMARY:
The paper introduces a novel trajectory recovery model PLMTrajRec to recover map-matched trajectory by fine-tuning a  pre-trained Language Model. By converting sampling intervals, movement features, and traffic flow into token sequences, the model unifies sparse trajectories of heterogeneous sampling rates and predicts both road-segment IDs and within-segment moving ratios. Experiments on four public datasets show outstanding gains of accuracy and RMSE compared to SOTA methods.

STRENGTHS AND WEAKNESSES:
Strengths

(1) The work focuses on an interesting problem, extending pretrained language models to trajectory recovery and showing that generic PLMs's ability in spatio-temporal tasks.

(2) This paper delivers a comprehensive evaluation on four real-world datasets and shows promising performance.

(3) The architecture including the interval-aware embedder, dual prompts, and LoRA fine-tuning is clearly illustrated, and every module is justified through ablation studies.



Weaknesses

(1) The demonstration of third challenge (Figure 1) is confusing.

(2) Experimentation on more cities could be considered.

(3) Apart from the temporal granularity, the generalization ability of the method can be considered for zero-shot experiments between different cities.

QUESTIONS:
See weakness.

LIMITATIONS:
Yes, the authors discuss the limitations in Appendix A.
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 2
DATE: 2025-06-25

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces a method entitled PLMTrajRec which tries to recover missing points in sparse trajectories. \n\nThere are three challenges in this task: 1) lack of datasets; 2) different sampling intervals of input trajectory data are hard to recover due to complex and dynamic spatiotemporal traffic conditions; 3) extracting road information for missing points is challenging.\n\nTo tackle the three problems, the authors 1) use pretrained language models (PLM) as backbone which equipped with human common sense and relieve the lack of data; 2) introduce an interval and feature-guided (IF-guided) explicit traejctory prompt to provide the sampling interval information to PLM, and also use a \"Trajectory Interval Unification\" module to gaurantee that each two points in the input trajectory has a same sampling interval; 3) design an area flow-guided (AF-guided) implicit trajectory prompt to provide dynamic traffic flow information for PLM, and also a road condition passing mechanism to retrieve relevant road condition for missing points to be recovered.\n\nThe authors test their model on four datasets, and the results show PLMTrajRec and its variant gets the best score on the metrics of Accuracy, Recall, Precision, Mean Absolute Error (MAE), and Root Mean Square Error (RMSE).\n\nThe authors conducted a solid ablation study to verify the effectiveness of each module. Besides, the authors also provide experiments to contrast the zero-shot ability between PLMTrajRec and other methods. Finally, the authors also provide efficient analysis to show computation  costs of the model.\n\nSTRENGTHS AND WEAKNESSES:\n1.Quality: This paper proposed a new method, provided detailed information and background of method. The author also provided code for reproducing. Also, the author provides rich and solid experiments to verify the rationality and effectiveness of the proposed method.\n2.Clarity: The paper is well organized, and provide link to appendix in each place that need extra information. However, in some place, the logic maybe not clarified.\n3.Significance: This paper tries to propose a method to solve the problem of recovering missing points in trajectories. The model can be applied in ride-hailing applications, provide more accuracy information for consumers and administrator. The results on the test set show the potential to utilize this method.\n4.Originality: Using PLM for Spatiotemporal data-related downstrained tasks is not a new thing. The authors should further elaborate on the common methods in related fields and clarify the innovations of this study.\n\nQUESTIONS:\n1.Questions\na.Line 197, you mentioned a function $f(d_{s, l})$ which seems to map the distance between a point and a road segment to a number (larger distance, smaller value of the number). So, does the function actually function as the weight to obtain $h_s^{road}$? And why use a \\textbf{ransomly initialized vector} (line 200)? Is the vector fixed or leaned?\nb.Line 223-226. You mentioned you use \"self-attention\", but $H'$ acts as the query, and $E_w$ serves as both key and vlue. In my understanding of attention, you may use cross attention instead of self-attention.\nc.Line 228 and Figure 2. You said you incorporated the Transformer position embedding PE into \\textbf{each} element of $Z$. 1) I hope you can provide more information about the position encoding you used. Is PE something about \"Learnable Fourier Features\" you mentioned in Appendix F? 2) In Figure 2, it seems that PE is a token instead of auxiliary embeddings for each token.\nd. Line 208-211. You only collect information for two points (the nearest observed forward and backward trajectory points), what do you think of extracting the information of the road between the two points? Will that be helpful?\n2.Writing Suggestions\na.The format should be consistent in section Preliminaries. The first words of Line 99, 103, 107, 113.\nb.More visualization and information about your method is helpful for understanding. Although the authors provide visualization in appendix, I do not see any link to Appendix K. I hope the author can add some links to the paper so that readers can see the visualization of the model result. I also suggest the author can provide an example of raw prompt and LLM output in appendix, that helps readers understand your tasks.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper addresses the problem of recovering missing points in sparse spatiotemporal trajectories, which is crucial for traffic-related applications but challenged by sparse data, varying sampling intervals, and difficulty in modeling dynamic road conditions. The authors propose PLMTrajRec, a trajectory recovery method that leverages a pre-trained language model (PLM) to overcome these challenges. Their contributions include: (1) Designing dual trajectory prompts (explicit and implicit) that encode sampling intervals, movement features, and road conditions into formats understandable by a PLM. (2) Introducing an interval-aware trajectory embedder that standardizes trajectories with diverse sampling intervals. (3) Proposing a road condition passing mechanism to estimate road conditions at missing points. Extensive experiments on four real-world datasets demonstrate the method’s effectiveness, scalability, and generalization, outperforming existing baselines, especially in low-data and zero-shot settings.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n1. This work tackles an important and practical problem with real-world implications in transportation systems, urban planning, and mobility analytics.\n\n2. The paper is technically solid, proposing several thoughtful innovations (e.g., dual prompts, interval-aware embedding, and road condition passing) that are well-motivated and clearly explained. \n\n3. The paper is clearly written and well-organized, making the methodology and experiments easy to follow. \n\nWeaknesses\n1. The method introduces additional computational and memory overhead compared to some baselines, which is mitigated but not fully eliminated by LoRA fine-tuning. \n\n2. Some details, such as the choice of PLM (why BERT over GPT or Llama) and sensitivity to the choice of PLM, are discussed but could benefit from deeper empirical analysis.\n\n3. While the paper discusses broader impacts in the appendix, the main text could highlight more explicitly potential societal risks (e.g., privacy, misuse of mobility data).\n\nQUESTIONS:\n1. The ablation compares different PLMs but does not deeply explore why encoder-only PLMs outperform others for this task. Could the authors elaborate on how task-specific needs (e.g., bidirectionality, context length) drive this choice, and how sensitive the performance is to PLM size and architecture?\n\n2. The paper demonstrates good scalability to low-data settings, but at the cost of increased inference time and memory. Are there scenarios (e.g., edge devices, real-time applications) where this trade-off becomes prohibitive? Could the authors discuss potential ways to further reduce the footprint?\n\n3. The design of the dual prompts is clever, but it’s not clear how sensitive the method is to the exact prompt format and content. Have the authors explored alternative prompt formulations, or can they recommend guidelines for constructing effective prompts in similar tasks?\n\nLIMITATIONS:\nSee in weakness", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes PLMTrajRec, a novel method for recovering missing points in sparse trajectory data using Pre-trained Language Models (PLMs). It leverages the scalability of PLMs and structured prompts to generalize across various sampling intervals and limited dense data. The model incorporates Dual Trajectory Prompts and an Interval-aware Trajectory Embedder, which unifies trajectories to a fixed interval and extracts features using learnable spatial encodings and road condition estimation. A message-passing-inspired mechanism is used to infer road conditions at missing points using nearby observed points. Experiments on four public datasets demonstrate superior performance, scalability, and generalization compared to baselines.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n\n- **Novel Integration of PLM for Map-Matched Trajectory Recovery**: The attempt to apply Pre-trained Language Models (PLMs) to the map-matched trajectory recovery task is highly novel. Unlike prior works that mainly rely on sequence models or graph-based approaches, this paper leverages the powerful sequence modeling capabilities and pretrained knowledge of language models, which is an original idea.\n- **Thoughtful Design for Key Challenges**: The methodology is well-designed to address three core challenges: data scarcity, varying sampling intervals, and road condition inference.\n    - PLM scalability mitigates the limited data problem.\n    - An interval-aware embedder and explicit prompts effectively handle various sampling intervals (generalizability).\n    - Implicit prompts and the road condition passing mechanism enable inference of road conditions at missing points.\n    \n    These components are logically integrated and well-motivated.\n    \n- **Reproducibility**: The authors provide code and dataset access, enhancing the reproducibility of the results.\n- **Comprehensive Experimental Evaluation**:\n    - **Multiple Datasets & Scales**: The model is evaluated on four public datasets with different city types and scales, showing its general performance.\n    - **Various Sampling Intervals**: Experiments cover three different sampling intervals (4min, 2min, 1min), systematically validating the model’s ability to handle varying temporal sparsity.\n    - **Targeted Studies**: The paper includes well-designed experiments to highlight core strengths, such as scalability studies (training with limited data) and zero-shot studies (testing on unseen intervals).\n\nWeaknesses\n\n- **Lack of Deep Justification for PLM’s “Language” Capability in Trajectory Tasks**: The paper may lack a deeper explanation of *why* PLMs are effective for trajectory recovery beyond their strength as general sequence encoders. It would be helpful to analyze what aspects of trajectory data align with PLMs’ language modeling abilities—do the prompts facilitate any kind of linguistic reasoning, or are they merely embedding manipulators? Currently, the model may be perceived as simply utilizing PLM as a strong sequence encoder without fully exploiting its “language understanding” capabilities.\n- **Dependence on Road Network Structure**: Since the model’s output is directly tied to road segment IDs and segment-level positioning, it raises concerns about the model’s dependence on a specific road network structure. For example:\n    - How would the model perform on regions with entirely different road network structures (as hinted at in Appendix A.1)?\n    - What happens if the road network evolves over time?\n    \n    There is potential for significant performance degradation in such cases.\n    \n    Additionally, it’s worth questioning whether the core ideas of PLMTrajRec—such as PLM usage, trajectory prompts, and interval-aware embedding—can be transferred to **free-space trajectory recovery** settings, where road network information is not available. Some parts of the model (e.g., output layer design and certain feature extraction steps) seem tightly coupled to road network structure. It would be valuable to hear the authors’ perspective on the adaptability of PLMTrajRec to free-space scenarios.\n\nQUESTIONS:\nPlease refer to the weakness section.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 2, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-03"}