Title: TrajMamba: An Efficient and Semantic-rich Vehicle Trajectory Pre-training Model
Venue: NeurIPS 2025
Accepted: yes
Keywords: Trajectory Representation Learning

Spatio-Temporal Data Mining

Self-supervised
Abstract: Vehicle GPS trajectories record how vehicles move over time, storing valuable travel semantics, including movement patterns and travel purposes. Learning travel semantics effectively and efficiently is crucial for real-world applications of trajectory data, which is hindered by two major challenges. First, travel purposes are tied to the functions of the roads and points-of-interest (POIs) involved in a trip. Such information is encoded in textual addresses and descriptions and introduces heavy computational burden to modeling. Second, real-world trajectories often contain redundant points, which harm both computational efficiency and trajectory embedding quality. To address these challenges, we propose TrajMamba, a novel approach for efficient and semantically rich vehicle trajectory learning. TrajMamba introduces a Traj-Mamba Encoder that captures movement patterns by jointly modeling both GPS and road perspectives of trajectories, enabling robust representations of continuous travel behaviors. It also incorporates a Travel Purpose-aware Pre-training procedure to integrate travel purposes into the learned embeddings without introducing extra overhead to embedding calculation. To reduce redundancy in trajectories, TrajMamba features a Knowledge Distillation Pre-training scheme to identify key trajectory points through a learnable mask generator and obtain effective compressed trajectory embeddings. Extensive experiments on two real-world datasets and three downstream tasks show that TrajMamba outperforms state-of-the-art baselines in both efficiency and accuracy.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes an efficient and semantically rich pre-trained model for vehicle trajectory design, TrajMamba. The model primarily addresses two core challenges: First, efficiently extracting travel purposes related to road and POI features; Second, how to handle redundant data points in real trajectories to improve computational efficiency and embedding quality. Specifically, TrajMamba comprises three key components:  Traj-Mamba Encoder jointly models GPS and road perspective to capture complex vehicle movement patterns.  Travel Purpose-Aware Pre-training aligns the trajectory embeddings learned by the model with travel purposes extracted from road and POI text information, enabling the model to understand travel intent without incurring additional computational overhead. Knowledge Distillation Pre-training uses a learnable masking generator to identify and compress key points in trajectories, yielding efficient and information-rich compressed trajectory embeddings.   Extensive experiments on two real-world datasets and three downstream tasks demonstrate that TrajMamba outperforms current state-of-the-art baseline models in both accuracy and efficiency.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. TrajMamba can effectively extract two key types of semantic information from vehicle trajectories. The first is to capture continuous “movement patterns” by jointly modeling GPS and road perspectives. The second is to efficiently learn “travel purposes” from road and POI information through pre-training.\n2. The proposed model uses knowledge distillation pre-training and a learnable mask generator to effectively identify and compress redundant points in trajectories, thereby improving computational efficiency and embedding quality.\n3. The experiments conducted on two real-world datasets and three downstream tasks demonstrated that TrajMamba outperforms existing state-of-the-art baseline models and exhibits efficiency in terms of model size and computation time.\n4. The proposed model offers scalability. Compared with other advanced models, TrajMamba can achieve better performance with less training data and shorter fine-tuning time.\n\n\nWeaknesses：\n1. The performance of the model depends heavily on high-quality external data, including accurate road network maps, POI information, and the accuracy of the map-matching algorithm. If these external data contain noise or are missing (which is common for trajectory data), the performance of the model may decline.\n2. This paper proposes a two-stage pre-training process that includes “purpose perception” and “knowledge distillation.” However, the article does not fully argue why these two independent stages are necessary, and whether training directly using compressed trajectories can achieve the same effect.\n3. This paper claims that the model is efficient, mainly in terms of the final embedding computation (inference) time and model size. However, this paper uses a two-stage training framework and combines many modules and additional information collection operations, the efficiency of which may not be satisfactory and may mislead readers' judgment.\n4. The experiment was conducted only on trajectories in two Chinese cities (Chengdu and Xi'an) and provided by the same organization. The homogeneity of this dataset raises questions about the model's generalization ability.\n\nQUESTIONS:\n1. Regarding the trajectory compressor or mask generator, does it mainly remove redundant trajectory points? What is the effect of directly downsampling the trajectory by a certain ratio?\n2. Regarding the task of arrival time estimation, the setting seems somewhat unreasonable. Typically, trajectory points have similar sampling frequencies, and predictions based on start and end points or road segment sequences should be more realistic.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 5, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-06-30"}

comments: SUMMARY:
This paper proposes an efficient and semantics-rich vehicle-trajectory learning called TrajMamba which consists of three coordinated components. On two DiDi taxi datasets (Chengdu & Xi’an), TrajMamba improves over nine SOTA baselines on three downstream tasks—destination prediction, arrival-time estimation, and similar-trajectory search.

STRENGTHS AND WEAKNESSES:
Strengths

(1) The work tackles the real-world bottleneck of scaling semantic trajectory analytics, efficiently modelling GPS traces while preserving travel-purpose information.

(2) By replacing quadratic self-attention with linear SSM blocks and adding a learnable compression module, the whole pipeline keeps time- and memory-costs low.

(3) Three downstream tasks (destination prediction, arrival-time estimation, similar-trajectory search) are evaluated on two large-scale city datasets, with comprehensive ablations, sensitivity studies and efficiency baselines.

Weaknesses

(1) DP task only computes the MAE/RMSE for the last coordinate and path-aware metrics are absent, which may not reflect the exact learning ability of the trajectory representation.

(2) In Table 7, the “w/o FT” variant underperforms fine-tuned models by a large margin. Without stronger evidence, it is difficult to assess the intrinsic value of the pre-training stage.

QUESTIONS:
(1) Do the other methods in Table 7 also have pre-training stage? If they do not have a separate pre-training stage, what is the setting and is this fair?

(2) See weakness.

LIMITATIONS:
Yes.
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-06-30

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper studies the problem of vehicle trajectory learning. To address it, this paper proposes TrajMamba, a semantic-rich trajectory learning framework that integrates GPS and road features via a dual-branch encoder, leverages travel purpose and applies knowledge distillation for trajectory compression. Experiments on two datasets demonstrate the effectiveness of proposed model.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\nS1: This paper investigates the fundamental vehicle trajectory learning problem.\nS2: This paper develops an efficient and semantic-rich vehicle trajectory pretraining model.\nS3: Experiments are conducted to demonstrate the effectiveness of the proposed method.\n\nWeaknesses:\n\nW1: The motivation of this paper is weak. \n(1) The claimed burden of textual modeling for extracting travel purposes seems overstated. Lightweight or offline text encoders (e.g., ALBERT-12M, MiniLM-22M, DistilBERT-66M) are widely available and effective, yet the paper neither adopts nor compares against these alternatives. Besides, the process for obtaining the initial textual embeddings in the Travel Purpose-aware Pre-training module is not clear. If these embeddings are derived using the above lightweight or offline encoders, the motivation that \"extracting travel purposes can be computationally heavy\" is questionable. Moreover, although the authors claim no additional overhead, the proposed dual-text encoder setup inherently introduces latent computational costs, making the efficiency claims difficult to verify.\n(2) The authors argue that redundant GPS points harm both efficiency and effectiveness, yet the claim regarding effectiveness is not well substantiated.\n\nW2: Although the paper presents some technical contributions, the proposed Traj-Mamba framework exhibits limited novelty, as it primarily adopts existing Mamba/SSM blocks—which have already been explored in trajectory modeling [1]—to model GPS and road views separately, followed by a simple linear fusion. Besides, this dual-view design for GPS and road views  is also a common paradigm in trajectory representation learning.\n\n[1] \"Trajectory mamba: Efficient attention-mamba forecasting model based on selective ssm.\" Proceedings of the Computer Vision and Pattern Recognition Conference. 2025.\n\nW3: The claimed effectiveness of the compression module is weakened by the use of existing rule-based preprocessing to remove explicit redundancy. This setup makes it difficult to disentangle the contribution of the proposed soft masking mechanism from that of the preprocessing itself. I recommend providing an case study to better validate the effectiveness of the learnable trajectory compression.\n\nW4. To justify the paper’s claim of achieving state-of-the-art performance, it is recommended to include recent work such as [2, 3,4] as baselines.\n\n[2] \"Mrgrp: Empowering courier route prediction in food delivery service with multi-relational graph.\" Companion Proceedings of the ACM on Web Conference. 2025.\n[3] \"UniTR: A Unified Framework for Joint Representation Learning of Trajectories and Road Networks.\" Proceedings of the AAAI Conference on Artificial Intelligence. 2025.\n[4] \"Path-LLM: A Multi-Modal Path Representation Learning by Aligning and Fusing with Large Language Models.\" Proceedings of the ACM on Web Conference. 2025.\n\nW5. The paper emphasizes efficiency, but the observed improvements are modest. Besides, a detailed complexity analysis is recommended to support the efficiency claims.\n\nQUESTIONS:\nQ1: In the Hyperparameter Analysis section, shouldn’t the optimal value of E be 512 instead of 256 based on the results?\n\nQ2: For the DP task on road segments, why is Recall used as the evaluation metric instead of F1-score, which is more commonly adopted in the literature [4]?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-01"}

comments: SUMMARY:
The paper introduces TrajMamba, a Mamba-based framework for trajectory compression and embedding. The authors propose a Travel Purpose-aware Pre-training method to incorporate travel purpose information, thereby alleviating the burden of encoding textual descriptions through contrastive learning. Additionally, a Knowledge Distillation Pre-training scheme is introduced to explicitly eliminate redundant trajectory points using a selfsupervised paradigm. Experiments conducted on two real-world datasets demonstrate the effectiveness of the proposed model.

STRENGTHS AND WEAKNESSES:
Strengths:
[S1] Using sentence embedding models and contrastive learning to capture trajectory travel intentions is both novel and promising. By shifting the computational burden to the offline phase, the training efficiency is greatly improved.
[S2] The approach of using a mask and the Gaussian error function for self-supervised trajectory compression training is ingenious.
[S3] The experimental setup is reasonable, and both the effectiveness and efficiency of the model are validated.

Weaknesses:
[W1] The discussion on trajectory compression is insufficient. Is there any analysis of the compressed trajectories, such as the average compression ratio? How were the weights (1/2) for the loss functions L^MEC and L^mask determined, and how do different weights affect downstream tasks and compression performance?
[W2] There is a lack of discussion regarding the sentence embedding. The paper uses text-embedding-3-large; would using other models affect the performance of the proposed approach？

QUESTIONS:
See weaknesses.

LIMITATIONS:
Yes.
TITLE: Official Review
CLARITY: 3
IMPACT: 4
ORIGINALITY: 4
DATE: 2025-07-02