Deep Temporal Deaggregation: Large-Scale Spatio-Temporal Generative Models

Authors: David Bergström, Mattias Tiger, Fredrik Heintz
Primary area: generative models
Keywords: generative learning, mobility data, denoising diffusion probablistic models

Abstract:
Access to spatio-temporal trajectory data is essential for improving infrastructure, preventing the spread of disease and for building autonomous vehicles. However, it remains underutilized due to limited availability, as it cannot be shared publicly due privacy concerns or other sensitive attributes. Generative time-series models have shown promise in generating non-sensitive data, but show poor performance for large-scale and complex environments. In this paper we propose a spatio-temporal generative model for trajectories, TDDPM, which outperforms and scales substantially better than state-of-the-art. The focus is primarily on trajectories of peoples' movement in cities. We propose a conditional distribution approach which unlock out-of-distribution generalization, such as to city-areas not trained on, from a spatial aggregate prior. We also show that data can be generated in a privacy-preserving manner using $k$-anonymity. Further, we propose a new comprehensive benchmark across several standard datasets, and evaluation measures, considering key distribution properties.

Decision: reject
Reviewer scores: 6, 6, 3

Reviews and discussion:
<thread>
creation date:
1730521167883

thread_type:
official_review

<thread_object>
creator: reviewer_3lhk

<content>
summary:
This paper propose TDDPM, a spatiotemporal generative model for trajectories. It aims to address two issues, (1) shortage of publicly available trajectories data; (2) model should conduct predictions about unobserved parts in space. To address these issues, TDDPM applies occupancy frequency marginal distribution as local information and hierarchical occupancy frequency mixture. The authors demonstrate the effectiveness of TDDPM on real-world datasets, showing improvements in forecasting accuracy compared to baseline models.

strengths:
1. The paper focuses on the privacy issues of spatio-temporal trajectory data, as well as enhancing the prediction about unobserved parts in space. This is a very valuable and meaningful research topic.
2. The paper provides detailed visualizations and What-if analysis.

weaknesses:
1. This paper proposes a privacy-preserving for generating synthetic trajectory samples, however, its contribution to privacy protection is limited. The paper uses only k-anonymity to protect local information in Section 5.3, which is a straightforward design. 
2. There are no baselines in Section 5.2 and Section 5.3. The author should demonstrate how other methods perform in the Generalization experiment and with k-anonymity.

questions:
Could you provide brief descriptions of your baselines?
</content>
</thread_object>

</thread>
<thread>
creation date:
1730564718906

thread_type:
official_review

<thread_object>
creator: reviewer_jubv

<content>
summary:
This paper presents TDDPM, a model for generating high-fidelity, privacy-preserving trajectory data in complex environments. TDDPM leverages a denoising diffusion approach to deaggregate spatial data into individual trajectories, allowing realistic time-series generation that generalizes to unseen areas. By conditioning on spatial aggregates, it achieves strong out-of-distribution performance. The model also uses k-anonymity for privacy and introduces a new benchmark for evaluating synthetic trajectory data, making it a valuable tool for urban planning and autonomous driving applications.

strengths:
(1) The demonstration of proposed method TDDPM is detailed. 
(2) The authors describe the motivation and background of generating out-of-distribution trajectories in detail.

weaknesses:
1. The contributions of TDDPM are unclear. The authors should clearly claim the contributions in the end of introduction.
2. In Table 1 and Table 2, standard deviation of KL and JS divergence are not reported.
3. This paper lacks an ablation study part. Experiments should be added to verify the effectiveness of proposed two steps mentioned in Section 4.
4. The experiment setting is confusing. The authors claim that TDDPM could achieve out-of-distribution generalization in Abstract. More analysis should be added to demonstrate the diffirence between the synthetic dataset and Geolife/Porto.
5. Line 308 to line 325 seems making no sense.

questions:
See weaknesses.
</content>
</thread_object>

</thread>
<thread>
creation date:
1730644711747

thread_type:
official_review

<thread_object>
creator: reviewer_urhw

<content>
summary:
This paper introduces a new task—out-of-distribution generalization—for synthetic trajectory generation, which current models in the literature cannot address. To enable this generalization capability, the paper proposes using a heatmap as a conditioning constraint in a generative denoising diffusion model. Experiments on both unconditional trajectory generation and generation for new environments and hypothetical scenarios are conducted to evaluate its effectiveness. Additionally, the generation performance is analyzed under private heatmap scenarios.

strengths:
1. This paper addresses a novel task, enabling the synthetic trajectory generation model to generalize to new areas.
2. A heatmap is used as a novel conditioning mechanism to achieve this generalization capability in the trajectory generation model.
3. Extensive experiments are conducted to evaluate the proposal.

weaknesses:
1. Additional metrics, such as Density Error, Trip Error, Length Error, and Pattern Score (as used in the TrajDiff paper), should be considered for evaluation.
2. The proposed method splits regions for training, but it is unclear whether the model can correctly generate cross-region trajectories under these conditions.
3. In Table 2, the reasons why the model trained on 25% outperforms the model trained on 100% in most cases should be explained more clearly.
4. As an ablation study, it would be helpful to include results when the model does not use the conditioning component for the unconditional trajectory generation task.
5. Providing information on hyperparameters and including a complexity analysis would improve the paper.
6. Typo:  "ell(..." should be "l(..." on Page 5.

questions:
Same as the weaknesses
</content>
</thread_object>

</thread>
<thread>
creation date:
1732533747612

thread_type:
official_comment

<thread_object>
creator: area_chair_mk6m

<content>
title:
Acknowledge the author responses

comment:
Dear Reviewers,

Thank you very much for your effort. As the discussion period is coming to an end, please acknowledge the author responses and adjust the rating if necessary.

Sincerely,
AC
</content>
</thread_object>

</thread>
<thread>
creation date:
1732895944957

thread_type:
official_comment

<thread_object>
creator: authors

<content>
comment:
We would like to thank all reviewers for their constructive feedback. Thanks to their input we have made the following improvements to the paper during the rebuttal period:

- Corrected the title in the pdf so that it now corresponds to the submission title (Page 1\)   
- Clarified the contributions of the paper by adding a list at end of introduction (end of page 2\)  
- Clarified the motivation leading up to the list of harmonized synthetic data qualities that we use for our study (previously line 308 to line 325, now on lines 325 to 329 on page 7\)  
- Extended analysis of the generalization result, comparing the generalization result to result in Table 1 where models have access to full data (page 8, under section 5.2), i.e. the baseline comparisons for Generalization experiment (5.2) and K-Anonymity experiment (5.3)  
- Extended the evaluation by also using DiffTraj’s evaluation measures to Table 1 (Page 9), briefly mentioning them in the Evaluation section and given a more detailed description in A.3.3 (Appendix)  
- Added an ablation study that isolates the improvement of using local information (Text on page 8-9 and data in Table 4 on page 10\)

Errata: Section 5.3.1 should be 5.4
</content>
</thread_object>

</thread>


Metareview:
metareview:
This paper proposes a spatio-temporal generative model for trajectories, TDDPM, which focuses primarily on trajectories of peoples' movement in cities.  Although there are several interesting ideas, the reviewers also raised several concerns on evaluation, contribution, and privacy-preserving properties.  In particular, the paper is not thoroughly written because it is not aligned with the title about privacy.  Thus, I recommend a reject.