Title: Cross City Traffic Flow Generation via Retrieval Augmented Diffusion Model
Venue: NeurIPS 2025
Accepted: yes
Keywords: Traffic Flow

Diffusion

Retrieval-Augmented Generation
Abstract: Traffic flow data are of great value in smart city applications. However, limited by data collection costs and privacy sensitivity, it is rather difficult to obtain large-scale traffic flow data. Therefore, various data generation methods have been proposed in the literature. Nevertheless, these methods often require data from a specific city for training and are difficult to directly apply to new cities lacking data.
To address this problem, this paper proposes a retrieval-augmented diffusion generation model with representation alignment. We use data from multiple source cities for training, extract consistent representations across multiple cities, and leverage retrieval-augmented generation (RAG) technology to incorporate historical data from source cities under similar conditions into the condition, aiming to improve the accuracy of data generation in the target city.
Experiments on four real-world datasets demonstrate that, compared with existing deep learning methods, our method achieves better cross-city transfer performance.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis work proposes CRAFT, a retrieval-augmented diffusion model designed for cross-city traffic flow generation. CRAFT addresses the domain shift problem across cities through Geographic Feature Alignmentand supplements missing dynamic traffic patterns in target cities using Retrieval-augmented Conditional generation. This enables the model to generate high-quality traffic flow data under zero-shot settings. Experimental results demonstrate that CRAFT significantly outperforms existing methods on four real-world city datasets, and the generated data achieves performance close to real data in downstream tasks, offering a practical solution for cities lacking historical traffic data.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths: \n1. The contrastive learning approach effectively distinguishes different traffic patterns across regions, facilitating the learning of a unified and transferable region-traffic pattern representation, which benefits downstream traffic flow generation tasks.\n2. The experimental results demonstrate strong performance in cross-city traffic flow generation and subsequent prediction tasks, validating the effectiveness of the proposed traffic pattern alignment and cross-city alignment strategies.\n\nWeaknesses:\n1. Using a diffusion model as the backbone of the framework may impact efficiency, yet there is no evaluation or comparison of training/inference time or computational costs.\n2. The experiments are limited to bike-sharing datasets, lacking robustness validation across broader transportation domains (e.g., vehicular traffic, pedestrian flow).\n\nQUESTIONS:\n1. Can population, road network, and POIs fully capture a region’s traffic flow patterns? In real-world road networks, traffic patterns are also dynamically influenced by neighboring regions. Does the proposed model account for such inter-regional dependencies?\n2. In the temporal length experiments, why does the baseline DDPM method exhibit improved performance as T increases? Can this scaling behavior be explained? Since the proposed method also adopts DDPM as its backbone, why does it not demonstrate similar scaling capabilities?\n3. Why is the evaluation restricted to bike-sharing grid data? Were other grid-based datasets considered to ensure broader applicability?\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-23"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes CRAFT, a retrieval-augmented diffusion framework for zero-shot cross-city traffic flow generation. It aligns geographic features across source and target cities using both flow-based and optimal transport-based losses, augments conditions by retrieving similar temporal patterns from source cities, and generates region-level inflow and outflow sequences from noise. Experiments on four North American cities with a leave-one-city-out protocol show that CRAFT outperforms multiple generative baselines in generation quality and downstream forecasting utility.\n\nSTRENGTHS AND WEAKNESSES:\n### Strengths\n- Introduces a novel zero-shot cross-city generation task with clear practical applications for data-scarce cities  \n- Combines geographic alignment and retrieval modules within a conditional diffusion model in an end-to-end trainable framework  \n- Provides comprehensive evaluation across multiple cities and assesses both generation metrics and downstream prediction performance  \n\n### Weaknesses\n- The method mainly integrates existing techniques such as graph-based geographic encoding, retrieval-augmented conditioning, and conditional diffusion models, without introducing novel algorithmic components or architecture-level innovations\n- Lacks detailed analysis or visualization of the retrieved temporal patterns and their contribution  \n- Evaluation is limited to four similar grid-based North American cities and does not cover diverse urban layouts or special event scenarios\n\nQUESTIONS:\n- It seems that the current implementation only considers feature-based alignments. Would incorporating explicit road network topology or intersection connectivity further improve alignment?  \n- Can the model capture extreme events such as holiday surges or incident-driven congestion?  \n- What are the computational and memory costs when scaling to larger source datasets?\n\nLIMITATIONS:\nWhile CRAFT establishes a promising framework for zero-shot cross-city flow generation, it could benefit from integrating richer road network topology and dynamic event indicators to better capture heterogeneous congestion patterns and unusual traffic spikes. Expanding the evaluation to include cities with non-grid layouts and providing visualizations of retrieved temporal snippets would clarify the retrieval module’s impact and support more informed tuning of retrieval strategies.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-23"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes CRAFT (Cross-city Retrieval-Augmented traffic Flow generaTion), a retrieval-augmented diffusion model with geographic representation alignment, to address the challenge of generating traffic flow data for new, data-lacking cities. Unlike existing methods that rely on city-specific data and struggle with cross-city generalization, CRAFT leverages data from multiple source cities and incorporates two key modules: Geographic Feature Alignment (GFA) to tackle domain shift by aligning geographic representations across cities through traffic flow alignment (TFA) and cross-city alignment (CCA), and Retrieval-based Condition Augmentation (RCA) to mitigate insufficient conditions by retrieving relevant historical traffic patterns from source cities. Built on the DDPM backbone, the model is trained on source cities and deployed zero-shot on target cities. Experiments on four real-world bicycle traffic datasets (Chicago, Washington D.C., Toronto, New York City) demonstrate that CRAFT outperforms existing baselines in cross-city zero-shot generation, achieving superior performance on metrics like CPC, NMAE, and NRMSE, with generated data showing high utility in downstream traffic prediction tasks, closely approaching the performance of real data.\n\nSTRENGTHS AND WEAKNESSES:\nThe strengths of this paper lie in several key aspects. Firstly, it addresses the critical challenge of cross-city traffic flow generation, a relatively underexplored area, by proposing a novel transfer learning framework that enables zero-shot generation in unseen target cities without relying on their historical data . Secondly, the introduced Geographic Feature Alignment (GFA) and Retrieval-based Condition Augmentation (RCA) modules effectively tackle domain shift and insufficient condition issues, respectively, and these lightweight, plug-in components do not require altering the backbone architecture, showcasing the method's simplicity and practicality . Additionally, extensive experiments on four real-world urban datasets demonstrate the model's state-of-the-art zero-shot generation performance, with significant improvements over existing baselines, and its generated data exhibits high utility in downstream traffic prediction tasks, closely approaching the performance of real data . Moreover, the paper provides sufficient details on the methodology, experimental settings, and open access to code and datasets, ensuring reproducibility .\n\nA notable limitation of the paper's methodology lies in its insufficient literature review regarding cross-city traffic flow generation and transfer learning. In the \"Related Work\" section, while it briefly categorizes traffic flow generation models into physics-based, static, and dynamic ones, and mentions retrieval-augmented generation (RAG) applications in time series, it lacks a comprehensive survey of existing studies specifically focusing on cross-city scenarios. Additionally, the discussion on transfer learning, which is central to enabling cross-city generalization, is underdeveloped—there is little engagement with relevant transfer learning frameworks or prior attempts to address domain shift in urban traffic flow generation. This inadequacy results in a failure to fully contextualize the proposed method within the broader landscape of cross-city transfer learning research, weakening the connection between the methodology and the core cross-city challenge it aims to solve.\n\nAnother major issue with the method is that it fails to compare with existing cross-city-specific methods. Instead of engaging with approaches specifically designed for cross-city scenarios, the paper merely lists a set of general generative models (such as GMEL, DFG, KSTDiff, etc.) as baselines without distinguishing those tailored for cross-city transfer. This lack of comparison with dedicated cross-city methods makes it difficult to fully assess the novelty and advancement of the proposed CRAFT in addressing the unique challenges of cross-city traffic flow generation.\n\nQUESTIONS:\n1. Does the model in the paper compare with existing cross-city-specific methods? And has the paper conducted sufficient literature research on cross-city traffic flow generation?\n\n2. What is the time complexity of the CRAFT method proposed in the paper? And is the training and inference process of this method efficient?\n\n3. The paper doesn't explicitly state if CRAFT can be applied to the traffic4cast dataset. But considering CRAFT is designed for cross - city traffic flow generation using geographic features and retrieval - augmented diffusion to handle domain shift and insufficient conditions, it might be adaptable to challenging datasets like traffic4cast. Its generalizable and flexible key components (Geographic Feature Alignment and Retrieval - based Condition Augmentation) imply that with suitable adjustments for the specific characteristics and complexity of traffic4cast, application could be feasible, though further experiments are needed to verify this.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a novel conditional-augmented diffusion model framework for addressing cross-city transfer issues in traffic flow data generation. Specifically: Aiming at the domain shift problem caused by the geographical feature domain shift of different cities, the authors propose a geographic representation alignment training method to extract high-quality representations and establish cross-city correspondences between regions. To tackle the insufficiency of dynamic generation conditions, the authors design a retrieval-augmented method based on data-rich cities. They make full use of the abundant data from source cities to enhance the model's performance in target city. From the reader's perspective, the most appealing aspect of this paper is the model's cross-city zero-shot generation capability, which is a highly demanded ability in spatio-temporal data analysis.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\nS1. The authors propose a novel framework of cross-city RAG and optimal transport-based representation alignment. For the problem of cross-city zero-shot generation, they effectively capture the similarity factors between cities, making full use of abundant data from source cities to enhance the model's generalization and scalability.\n\nS2. The research on cross-city traffic flow data generation is critical for the spatio-temporal data field. Most existing studies focus on traffic state prediction, but deep learning-based paradigms rely heavily on massive historical data for training, failing to predict traffic states in cities or regions with insufficient data. How to transfer deep learning models to data-scarce cities remains a challenging problem, and this paper provides valuable insights.\n\nS3. The writing is concise and clear, with a lucid elaboration on the cross-city migration problem and corresponding challenges, enabling readers to grasp key points easily. The experimental analysis is also comprehensive.\n\nWeaknesses:\n\nW1. According to the authors' description, the traffic flow generation problem does not require historical traffic flow data as input but relies on basic geographical information. It is recommended that the authors elaborate on the data dependencies of this method in the experiments or appendix, i.e., what minimum inputs researchers need to prepare to synthesize data using this method.\n\nW2. Some writing details in the paper need improvement. The Preliminaries section defines a graph composed of regions, but the subsequent text does not explicitly explain how the model models the adjacency relationships of urban spaces. The basic features of urban regions used later (POI, population, and road networks) should also be explicitly defined in the Preliminaries section.\n\nW3. The abbreviations of different city datasets in the experimental section should be unified (e.g., Table 1 and Table 2 use full dataset names, while Figure 7 uses abbreviations) and explained to ensure the paper's rigor and readability.\n\nQUESTIONS:\nSee Weaknesses.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 6, "REVIEWER_CONFIDENCE": 5, "CLARITY": 4, "IMPACT": 4, "ORIGINALITY": 3, "DATE": "2025-07-02"}