Cross City Traffic Flow Generation via Retrieval Augmented Diffusion Model
Authors: Yudong Li
Jingyuan Wang
Xie Yu
Peiyu Wang
Qian Huang
Venue: NeurIPS 2025 poster ()
Keywords: Traffic Flow
Diffusion
Retrieval-Augmented Generation
Primary area: applications
TL;DR: We proposed a cross-city traffic flow generation method.

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.

Cross City Traffic Flow Generation via Retrieval Augmented Diffusion Model
Yudong Li Jingyuan Wang"1:2 1,2.3 Xie Yu!, Peiyu Wang', Qian Huang
School of Computer Science and Engineering, Beihang University, Beijing, China School of Economics and Management, Beihang University, Beijing, China
MIIT Key Laboratory of Data Intelligence and Management, Beihang University, Beijing, China Global Technical Service Dept, Huawei Technologies Co., Ltd Beijing, China {yudongli, yjwang, yuxie_scse, scae, peiyuwang) -bbuaa, edu. cn huangqian wwwwawwwi......... com
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-augmeened diffusiin generation model with geographic representation alignment. We use data from multiple source cities for training, extract consistent representations across generation (RAG) technology
multiple cities, and leverage setreval-auggmeter incorporate dynamic traffic flow patterns into the condition, aiming to improve the accuracy of data generation in the target city. Experiments on four real-world datasets demonstrate that, compared existing generation methods, our method achieves best cross-city zhoo performance. Our code and datasets can be found in https::/gi thtp::///tttubbcoo/oo.o hotp.///aa//yy181111///....
Introduction
Background. Traffic flow data is crucial in intelligent transportation systems 226 17 urban manage- ment [16] 49) and smart cities applications [1]. The success of traffic flow analysis [48] (55) is coupled with data-hungry paradigm, where superior performance and remarkable generalization ability rely on large-scale and high-quality data. However, unlike computer vision (CV) and natural language processing (NLP), where large-scale datasets are readily available from public sources, collecting traffic flow data faces strict privacy constraints and much higher costs. As result, existing public traffic flow datasets are usually limited in both scale and quality. Overcoming this challenge to acquire larger-scale, higher-quality traffic flow data has emerged
critical bottleneck.
Motivation of This Work. In response, traffic flow generation has become an increasingly urgent research direction (58). It aims to synthesize realistic traffic flow by learning the conditional mapping from static urban geographic context dynamic traffic flow distributions. Early work predominantly employed physics-driven models [60] 522 42 288, which only estimate static average flow volumes traffic. More recently, deep generative approaches have been dominant, leveraging richer geographic context as input to model flow distributions with greater fidelity. Their modeling paradigm has shifted from variational
and struggled capture the complex temporal dynamics observed
"oorresponding author: Jingyuan Wang
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

---

Chicago
Trafflc F Flow Pattern
Terunto
r3
Seationical Valurs
Statistical Vahuna
Taeb
PO
SOODOOO (TF-DD)
POl
Boads
Population
Populition
There still correspondencee between geographic contexts and patterss even in cross-city scenarios
(b) shift disrupts original correspion
dences.
Figure 1: In la For each region, we aggregate the statistical values of its geographic contexts to form the raw geographic features, finding that regions with similar geographic features may share flow patterns even across different cities. From Tb, further observe that, overall, this correspondence becomes blurred in cross-city scenarios. Although geographic features and traffic flow are still roughly correlated, the presence of cross-city domain shift prevents it from being applied directly.
autoencoders (VAEs) 22 and generative adversarial networks (GANs) g to difffusion models 12] which now lead the field due to their strong conditional generation capabilities. Consequently, recent studies emphasize incorporating stronger urban priors into conditions (58), thereby supporting more fine-grained and accurate traffic flow generation.
While incorporating stronger geographic priors into model inputs boost performance, these priors are often city-specific, which limits the model's ability to generalize. Such priors are essentially the dependencies between local geographic structures and their corresponding flow patterns, which typically require training on city-specific historical flow data. This reliance hinders model deployment in cities with insufficient traffic flow records. In this paper, attempt to address this issue, and explore directly generating dynamic traffic flow in entirely unseen cities via a cross-city transfer learning framework.
Problem Analysis. To achieve our motivation, we must restrict model learning from common geographic contexts shared across cities, thus avoiding the aforementioned city-specific priors.
In specific, they denote points of interest (POIs), roads, and population. However, utilizing these contexts for cross-city transfer faces two difficulties:
Domain Shift: Within single city, regions with similar geographic features typically exhibit analogous traffic-flow patterns. However, in cross-city settings, domain shifts in geographic representations disrupt this correspondence (as illustrated in Fig. [ Consequently, regions from different cities-even if their geographic features are close in representation space-may exhibit markedly different traffic-flow behaviors (see in Fig. lbp.
Insufficient Condition: static geographic contexts alone cannot support accurate cross-city flow generation. Regions with similar spatial characteristics may share similar periodicity and trends but their stochastic properties can vary significantly (e.g., absolute values of mean, peak and variance). Such dynamics provided by historical flow records but absent in cross-city scenarios.
1 46), which forecasts future traffic based on historical flow data, traffic flow generation synthesizes realistic traffic flow mainly static geo- graphic features. Departing from previous methods, our model is explicitly designed for cross-city generalization: is trained source cities and directly deployed unseen target cities.
Remark: Unlike traffic flow prediction 18 50
Design Insights and Contributions. Accordingly, we propose Cross-city Gearieval-Augmenne traffic Flow generaTion model (CRAFT), which is simple yet effective DDPM-based [12] model. Fundamentally, we propose the Geographic Feature Alignment (GFA) to address domain shift; For Insufficient Condition:, we propose the Retrieval-basse Condition Augmentation (RCA), which integrates traffic flow from source cities to supplement temporal dynamics. Our contributions can be summarized as:
Our work is an initial probe to tackle cross-city traffic flow generation, proposing a transfer learning framework that enables traffic flow generation without historical data in target cities.

---

Specifically, we proposed GFA and RCA to tackle two fundamental challenges in cross-city flow generation. Both are lightweight. plugin components without altering the backbone architecture. revealing the simplicity of our approach.
Extensive experiments four real-world urban datasets demonstrate the state-of-the-art (SOTA) zero-shot generation performance of our model and further validate its strong generalization ability,
2 Related Work
Traffic Flow Generation
Models has evolved along two key trajectories: leveraging more compre- hensive geographic features as inputs, and evolving from static to dynamic flow generation. This development can be grouped into three stages: physics-based, static, and dynamic flow generation models. Physics-bused Models rely empirical rules. The Gravity Models 1 52 60) and Radiation generate arigin-destinan
(OD) flows based on gravitational laws and radiative diffusion, respectively. These methods use simple geographic features as inputs and are limited to bearse-grained OD flow prediction; Subsequently, neural networks became prevalent due to their ability to incorporate more comprehensive geographic information. Initially, Static Flow Generation Models learn the dependencies between static traffic flows and the geographic characteristics of urban regions (27 40
Models 228 42
43] For instance, DeepGravity 43 extracts features from Open (OSM) to estimate OD flows, while DeepFlowGen [40] incorporates abundant POI data. Although these methods capture richer geo-features than physics-based models, they still fall short in modeling temporal dynamics; Further, Dynamic Flow Generation Models address this limitation by modeling temporal variations through generative frameworks [58 14
R4
37
56
36
33
35
The field has advanced
from generative adversarial networks (GANs) 14 to diffusion models 3 58 which now lead the
field due to their strong conditional generation capabilities.
Comparison: Current models perform well within individual cities as they tend to involve city-specific priors, but this reliance hinders their cross-city transfer abilities. In contrast, our model is designed to enable cross-city generalization by leveraging the common features shared across cities.
Detrieval-Augmentee Generation (RAG) is widely used in large language models (LLMs) 24 7..
as it can dynamically integrate knowledge from external databases and enhance generation accuracy. Recently, time series analysis involves RAG to provide meaningful guidance [25]. For example,
20 applied similarity retrieval based on time series embeddings to improve the prediction accuracy and enhance the zero-shot capabilities of time series foundation models (TSFM) [5] (54]. RAG is helpful for enhancing the model's 's capabilities in unseen scenarios. However, in the field of
25
spatio-temporal data generation [2 59 51, attempts using RAG still remain limited.
3 Preliminaries
Definition 1: (Region) We divided each city into N non-overlapping basic rectangular grids or polygons, denoted as R frali 1,2, ..., where N is the total number of regions. Each r; is characterized by various geographical features including population, road network and points of interest (POIs). These features serve the basic static condition for traffic flow generation.
Definition 2: (Region Graph) A region graph is denoted as g (R,E), where & E {0, (0,1)|M0x0) is the binary adjacency matrix. Specifically, Ei, = if r; and ry are adjacent, and & = otherwise.
Definition 3: (Traflic Flow) Given region ri. assuming it has C' traffic flow features, such as traffic inflow and outflow. We denote the traffic flow of r; at t-th time slice with time length as X E CCT, The traffic flow dataset of all regions staring at all time slice is denoted as X.
(Traffic flow generation). The objective of traffic flow generation is train a model F to generate dynamic traffic flow of regions based on their static geographic features, including population, road network and POIs. Specifically. F takes region graph as input and outputs X'
Problem Statement:
==
(1)
where are trainable parameters of F, and X evaluated by measuring the similarity to the worldworld traffic flow data X.
3

---

Geographical Feature Alignment
Retriovl-bbssd Condition Angmentation
(C) Conditional Diftusion Backbone
Source cilies
Source ges
Source BoM
Smiiingg
Random Nove
Papaldina
X
Spatial Encoder
Retrieve
Dawn Sample
Dawn Sample
Cond
Up Sample
Distance
by A
Matris
Ugtinal Thanggrt
Date Time
X(n-1)
Fouaatyy Koup Ceou
Target goo
Denalse
Figure 2: The framework CRAFT
Problem Statement: (Cross-city traffic flow generation). Models are trained on data from multiple source cities and make zero-shot generation on unseen target cities. Formally, let g denote the region graph of target city and g(*) denote parameters trained from source cities and the process is
p(0) F (g (()))
4 Methodology
4.1
Framework
The framework of CRAFT is in Figure. which applies the DDPM as the backbone. Specifically, CRAFT contains three modules: (1) Geographical Feature Alignment (GFA); (2) Retrieval- bbssed Condition Augmentation (RCA); (3) Conditional Diffusion Backbone.
Initially, the datasets are divided into source and target cities: source cities include both traffic flow data and regional geographic features, while the target city only has regional geographic features. GFA is first pre-trained on source and target cities to provide cross-city geographic feature alignment. Subsequently, RCA is designed to retrieve relevant historical data from source cities via the aligned regional representations. supplying an augmented condition for the diffusion backbone. Finally, Conditional Diffusion Backbone takes augmented condition make zero-shot traffic flow generation in the target city.
4.2 Geographic Feature Alignment
To address the challenge of domain shift (in Section D.. we propose the geographic feature alignment module. Unlike existing geographic representation learning methods (19
57 53 which learn representations based on entity embeddings, we begin by selecting common geographic features shared across cities and use them to construct basic representations for each region. Subsequently, we udopt the Graph Transformer [4] as the spatial encoder, which takes each city 's region graph G as input to generate higher-level representations. Finally, the encoder is trained through traffic flow alignment (TFA) and cross-city alignment (CCA) to enable transferable geographic representations.
Basic Geographic Representation. For most cities, both static maps (OpenStrrettMa) and popula- tion data (WorldPop) are publicly available and highly correlated with traffic flow patterns. Therefore, the basic geographic representations are derived from the following three aspects:
POls: POIs are highly correlated with the region functions. For each region, we use the TF-IDF construct POI representations f (poi) based on POIs' categories and numbers.
algorithm 39
Roads: For each region, we compute the total length of road segments across all road categories, as this captures the region's 's transportation capacity. Regions with dense networks and a high proportion of trunk roads typically offer better accessibility and attract heavier volume of traffic flow. The road feature is denoted (roadd)

---

Population: For each region, we collect statistics on its population size and density, as population implicitly reflect the scale of its traffic flow. Regions with higher population density tend to generate larger volumes of traffic flow. The population feature is denoted as f(pop),
For each region rú. its f(zos), f(roud), and f(pop) are concatenated and projected through a multi-layer perceptron (MLP) to generate the basic geographic representation
2i = MLP ((PoP) (pot)) (1
(3)
Further, the spatial encoder takes 2; as input and models the correlations among regions. For a specific city with its G. Region representations are further embedded as
{he, h22, .... hN) Graph Transformer ((=1, 22. 22...., 2.......)..... ] ,),,
(4)
where N is the region numbers of this city and G is the region graph. To obtain cross-city transferable region representations, we pre-train the spatial encoder through the proposed traffic flow alignment and cross-city alignment methods. During the pre-training stage, geographic representations of both source and target cities are jointly involved to facilitate cross-city alignment.
Traffic Flow Alignment (TFA). Only regions from source cities are involved in the TFA. We aim to ensure that the differences in region representations reflect the differences in their corresponding traffic flows. Specifically, we first compute the average traffic flow for each region over the time period. Then, all regions from the source cities collected the set R2 (2, ,Y,, J. with
(a) their corresponding representations denoted as H. { (11 h12 ,N,. where N, is the total
number of regions from all source cities. The traffic flow alignment loss is defined as
1 Eis(( -dy)d
LFA
(5)
N
where du MinMaxNorma.j (11/0) ng () dij = MinMaxNorm, (I) - -x,,xx,,,2) and X;
the average traffic flow data of region r;-
Cross-City Alignment (CCA). We seek to project semantically similar regions from both source and target cities into proximity within the representation space. Since explicit regional correspondence labels are unavailable, our model needs to learn these correspondences adaptively. By leveraging flow patterns, TFA alone draws regions with similar flows together. Although it does not fully resolve domain shift, it groups regions with both similar flow dynamics and geographic context. Conse- quently, in the representation space each region's 's counterparts naturally reside in its neighborhood. Therefore, we formulate the cross-city alignment as an optimal transport (OT) problem [32), seeking the minimum-coss distribution mapping between regions the source and target cities under the Wasserstein distance.
Specifically, the representations of all regions in source cities and target city collected into the set H. and H4 |1 ( ,.. NN respectively. N₂ is the number of regions in the target city. As both H. and H,, are enumerable, we can directly calculate the Wasserstein distance and tackle this problem through the optimal transport solver (2) 6). First, Euclidean distance is used to get the initial representation distance matrix D between regions in source cities and the target city, and Dij is
Di = 1
(5) - n/ - where (*)) E Hey h1) E H4-
Then, the transport matrix T is calculated by the OT solver, where T OTSolver (D). The cross- city alignment loss LCA is defined as Wasserstein distance, which is equal to the dot product of the transport matrix T and the distance matrix D, as follows
Lcs E.jTTy" Dij.
For more details on the formulation of optimal transport problem and the structure of the OT solver, please refer to Appendix A. The total alignment loss is the weighted sum LF and LCa. where λ and X2 are the balance weights, as follows
LA XLLFA + X2LCA-
(8)
5

---

Remark: As shown in Fig. | basic geographic representations are too coarse to guide cross-city flow generation. To refine this issue, we propose GFA with two complementary losses: (1) Traffic-flow alignment: refine regional representations in the correct direction according to regional flow patterns from source cities; (2) Tackle cross-city alignment without explicit regional correspondence labels, enables our model to adaptively determine the most accurate counterpart by leveraging an optimal transport loss.
4.3 Retrieval-based Condition Augmentation
To address the challenge of insufficient condition (Section D. we proposed the retrieval- aarrentl-augmenneed condition augmentation strategy. For each target region, our method retrieves relevant historical data from source cities according to its geographic representation, supplementing the input conditions for the diffusion backbone. For each target region ry, the generated input condition contains three parts: geographic features h4, time embedding temb, and historical data from source cities Li,t.
temb contains three periodic temporal components: the month in the year montth E [1]. the day in the week Eday E [1, 7] and the hour in the day thour E [1, 24). These three components are encoded into vectors through the embedding layers and concatenated to obtain the time embedding (tmontth || (tay|(hhour), which indicates the start time of the generated flow.
temb= =
Ci denotes the retrieved results for according to montth Idays Hhour and h;. Traffic flows from source cities are first segmented into sequences of length T. We then filter these segments using two criteria: (1) Starting time information montth ddag and howr; (2) Region representation similarity
to ha. The matched top K flow sequences are selected as Xt, {X 1,2 X2... Xx, }. where Xx. E RCXT, where X., is the average flow of region Ta staring time slice t. We employ self-attention [45] block Attn(-) to extract flow patterns from Xt,, which takes the averaged &,, as input to mitigate the impact of noise. The extracted retrieval result is
K ΣK
Ct = Attn
Xk.
Finally, above features are concatenated and projected by Multi-Layer Perceptron (MLP) block to generate the input condition Ci.e for the target region as follows
Ci = MLP (h;) (es,e(ttmm).
(10)
4.4 Conditional Diffuusion Backbone
As shown in Fig. 2 we adopt the Denoising Diffusion Probabilistic Model (DDPM) as the backbone.
Training. In the training phase, for the traffic flow sample X in source city, we add random noise through the forward diffusion process and use a ID-U-Net (38] as a noise estimator to predict the added noise. Following most of the literature about diffusion model, use X to represent Xt,, in the rest of this paper. Following [12), the moise-adding process of i: steps can be simplified as:
X V + V1 ābC,
(11)
where e ~ N I) and 0g is a hyper parameter. The noise estimator Eσ takes the noised sample x(k), the step and the condition vector C,2, as inputs to predict the noise. The Mean Squared Error (MSH) loss function is used to train the noise estimator, as follows
LNe = Ex,x,,.c
(x((), ki c.t) -
(12)
Inference. In the inference phase, for each r; in the target city, the model takes random Gaussian noise E ~ (O, I) as input and generates its corresponding traffic flow X conditioned on the c, through an n-step denoising process:
x
X {()) == -N (Ho
-N(HO (x((), k. c..) o² (x(0) ks
(13)
In the equation
13 ss denotes the mean variable and 0² is the variance:
14 x (k) k, c(,e)
x (A)
Bx Eo (x()), ki c..) o² (x(1),))
1 āk-1 8k-
Vax
ax
1 O&
(14)

---

Table 1: Cross-city traffic flow generation results
Method
(IK((1)
Inflow NMAR[]
NRMSb())
(PCC')
Outflow NMARS()
NRMSSBLJ
(ICC2))
Inflow NMAR[]
NRMSFX.)
CROXT)
Outfiow NMARL.
NRMSSR()
0:730
0.227
0.741
0.319
0.716
0.362
0.48
0.345
0.600
0.239
0.342
0.005
0.67
0.613
0.619
0.330
0.11
York CINNYYC Walhiggae
0.51
0.533
0:332
0.000
0.509
0451
0.528
0.466
0.634
0.420
B422
0411
0.393
0.41
0.275
0.396
0.345
0.514
0.512
0490
0.385
0.238
0.385
0.468
0.785
0.316
0,815
0.16
GMEL
0.735
0.236
0:383
0.746
112112
0.276
0.585
0.310
0.579
0.672
0.183
0.242
DRG
0.375
0.395
0.514
0.278
113994
0.512
0.581
0.182
0.278
0.383
0.183
0.279
KST
0/06
(1469
0.597
0.248
11:13
(.540)
0.043
0.277
0.366
00700
01722
0.364
IGAN
0.561
0.358
0.463
0.572
10:354
(1461
0.368
0.330
0.476
0:400
0.388
0.522
Diflwaee
0.521
0.421
0.531
0.546
10.56
0.574
0.363
0:475
0.608
0-434
0.440
0.575
DIT
0.552
0.504
0.489
0.901
11.381
0.471
0.419
0.416
0.508
0.449
0.363
0.478
DEPM
0.392
0.358
0.474
0.995
11.359
0.474
0,535
0.316
0.431
0.525
0.324
0.438
CVAR
0.099
(1289
0.194
0.995
11.299
199
0.568
0.244
0.403
0.571
072000
0:401
CRAFT
0,804
0.178
0.367
0.884
I1.19
0L248
6,782
0.181
0,170
0.786
0.102
IL168
where Bg is hyper-parameter and og II Bx)) Through n-step denoising process, the traffic flow data X (0) is generated.
5 Experiment
Dataset: We conducted experiments on four real-world bicycle trip datasets, namely Chicago (CHI)) Washington, D.C. (DC Toronto (TRT) and New York City (NYC) We manually partition each city into grid-based regions and count the number of bicycles entering and exiting each region within each hour to obtain the traffic flow. In our experiments, we use normalized traffic flow values; Baseline: We employ GMEL
(27), DFG 40), KSTDiff (58), CGAN (29), Diffwave (3], DiT [3], DDPM 12] and CVAE [10 as our baselines; Evaluation Metric: We evaluate our model on the metrics of Common Part of Commuters (CPC), Normalized Mean Absolute Error (NMAF) and
Normalized Root Mean Square Error (NRMSE). Details about dataset pre-procession, baselines, and
evaluation metrics are in Appendix
5.1 Overall Generation Performance
Generation Performance: Table. reports the zero-shot cross-city performance comparison between
our method and selected baselines. The experiments are organized into four groups. Each city is sequentially assigned as the target city while the remaining three cities serve as source cities. Models are trained exclusively on the source cities and evaluated in a zero-shot manner on the unseen target city. For each target city, both inflow and outflow data are generated. From the results, our method consistently outperforms existing approaches and achieves state-of-the-art (SOTA) performance across various settings. It yields improvemmen improvement of 59.7% compared with the average level of all baselines and an improvement of 22.5% compared with the second-bes baseline (GMEL). When compared with ordinary DDPM, approach yields an average improvement of 61.5%.
Utility of the Generated Data: To further assess the quality of the generated flow data, we evaluate them on downstream traffic flow task across multiple target cities. Specifically, we use the synthetic data produced by CRAFT and other baselines to train two representative models for flow prediction: vanilla LSTM 13 and a Transformer
(45 As reference, we also train the same models on real flow data from the corresponding target cities. As shown in Table. 2 our model consistently outperforms all baseline methods across various evaluation metrics. It achieves an improvement of 55.9% over the average level of all basclines and an improvement of 14.9% over the second-best baseline (DDPM). Furthermore, model achieves performance that is closest to the results of direct training on real traffic flow data. Specifically, it demonstrates an average performance degradation of only 10.4%, with minimum drop of 3.8% and a maximum drop of 22.2%. These results underscore the model's
aww.:ttaas.....mm.a.a.am.. com
https://s3. aww.:/aass.ammmzmus..ss http://eno..o..epeeaaaaaaaeerrre wtw.dddaaaaintttrppppt
httpa://ckano.. https://s3, htt.://as..ammzaaasss.... com/ http://wwwaaaaaaoooaaaaaa//////0

---

Table 2: Data utility comparison traffic flow prediction
CHicago(CHU
Washington, D.C.DD))
Yoronto
New York CHYINYC)
Gen
Outfow
Inflow
Outloww
Inflow
OutBoww
MAE
RMSE
MAE
RMSE
MAE
RMSE
MAE
RMSE
MAB
RMSE
RMSE
RMSE
Real
0,100
0.152
0.105
0.157
0.105
0.156
0.109
0.163
0.132
0.191
0.132
0.191
0.063
0.107
0.063
0.106
GMEL
0,198
0.264
0.202
0.242
0.325
0.201
DEG
0.900
0.443
0.52
0.222
0.153
KSTDDR
0.339
0.422
0.306
0.43
0.514
0.504
0.560
0.672
0.78
0.365
CGAN
LSTN
0.435
0.302
0.414
0,540
0.250
0.571
0.447
0.506
0.380
0,445
0.347
0.451
0.291
0.432
1.334
0.475
Diffawe
0.275
0.583
0.277
0.376
0.321
0.418
0.347
0.442
0.373
0.445
0.563
0.437
0.211
0.306
0.177
0.278
DIT
0.266
0.315
0.251
0.307
0.307
0.361
0.381
0.347
0.334
0.583
0.333
0.363
0.346
0.250
0.250
0.262
DIPM
0.376
0.119
CVAE
0.252
0.344
0.356
0.363
0.789
0.231
CRAFT
0.109
0.164
0.111
0.166
0.124
0.150
0.330
0.189
0.141
0.383
0.112
0.869
0,110
Real
0,098
0.151
0.100
0.154
0.095
0.149
0.100
0.157
0.131
0.196
0.132
0.197
0.056
0.100
0.056
0.105
GMEL
0.268
0.189
0.256
DEG
0.337
0.484
0.311
0.453
0.51
0.338
0.314
0,089
0.148
KSTDDF
TrunSomeE
0.336
0.469
0.338
0.477
0.514
0.505
0.560
0,672
0.479
0.507
0.488
0,500
0.208
0.338
0.218
0.357
CGAN
0.373
0.487
0.380
0.296
0.395
0.503
0.463
0.508
0.286
0.582
0.297
0.38%
0.264
0.391
1..776
01406
DiEware
0.282
0.384
0.261
0
0.315
0.396
0.303
0.382
0.362
0.432
0.366
0.436
0.178
0.271
0.175
0.258
DIT
0.275
0.317
0.255
0.304
0.297
0.352
0.365
0.347
0.335
0.381
0.333
0.363
0.229
0.367
0.23
0.270
DDPM
0.1%
0.247
CVAE
0.308
0
CRAFT
0.105
0.161
0.108
0.165
0.116
0.171
0.122
0.182
0.141
0.306
0.144
0.111
0.067
0,111
Agrmen
w/o Tame Exb
NDo Aogeaet
Aighine
Eri
RDA
0.3
Astalioo
0.3
0.3
9.
0.4
Kee
DC
Teerto
New York
0.1
Claage
Tom
Fala York
(a) Inflow CPC
(b) Inflow
NMAE
(c) Inflow
NRMSE
45
455 Algement
weo RCA
who Timc Enb
wnss ROA
A/o Tene Ere
No.Abnalon
Adartr
0.7
0.3
DC
Tom
New York
0:1
Creage
100
Now York
( Outflow NRMSE Figure 3: The overall ablation results on metrics of CPC (1), NMAE (4), NRMSE (L)
(d) Outflow- CPC
(e) Outflow NMAE
3.5
Sturee Citor
Rermal uouuttiii noit
Otty
6.1
6.7
2
6.7
(a) TFA CCA
(b) only CCA
(c) TFA + CCA
(d) only TFA
Figure 4: Alignment Analysis on TFA and CCA (New York City is the Target City). All scatter points in this figures represent the regions' geographic features. In (a) and (b), the color intensity indicates the value of traffic flow. After TFA, regions with similar flow patterns exhibit similar geographic representations. By comparing (c) and (d), we observe that CCA alleviates the distribution shift between the target city and source cities.
superior capability in traffic flow generation, outperforming existing baselines and highlighting its strong potential for practical deployment.
5.2 Model Analysis
Ablation Study: Recall that our CRAFT relies on two main components: GFA and RCA. In Fig. 3
we first conducted overall ablation studies to testify their effectiveness based on following settings. (T)
w/o Alignment: remove GFA by directly using target region's 's original geographic features for cross-
8

---

(a) Real
(b) Ours
(d) (e) DDPM
Figure 5: Heatmap of the generated traffic inflow in Chicago
(6) enc. layers
(b) temp. dims
(c) geo. dims
(d) enc. layers
(c) temp dims
(1 geo. dims
Figure 6: Sensitivity analysis of inflow generation NRMSE (fig. a, b, c), and CPC (fig. d, e.
CHI-CPC
DC-CPC
(g) CPC
(h) TRENMAE
() TRT NRMSE
CPC
NYC
NYC NRMSE
Figure 7: Temporal length extension
cities on metrics CPC (t). NMAE (L), NRMSE (J),
metrics are evaluated on inflow generation.
city retrieval and generation of conditions. (2) w/o RCA: remove the retrreeal-augmmented features hi,t from Ci (3) w/o Temporal embedding: remove the temporal embeddings temb from C₁ which supplements the periodic patterns of the traffic flow. Both w/o RCA and w/o Temporal embedding are related RCA. From the experimental results in Fig. 3 we can notice: (1) GFA provides the most improvement, highlighting that distribution shift is indeed the major problem in the cross-city generation. (2) RCA, femb contributes the most to the performance gain, highlighting the value of periodic temporal patterns. Additionally, a consistently improves performance, highlighting the value supplementing dynamic patterns from source cities.
Further, we explore the specific role of the traffic flow alignment (TFA) and the cross-city align- (CCA) in GFA. Fig. 4 apply t-SNE to all regions' geographic features in New York. We observe that TFA indeed aligns geographic features with traffic flow patterns. CCA mitigates the distribution shift in geographic features between source cities and the target city, thereby enhancing cross-city generalization. Experiments on all four city are provided in Appendix C.
Sensitivity Analysis: We made various sensitivity analyses on four important hyperparameters,
including (1) the layer number of the sequence encoder; (2) the dimension of temporal embedding: (3) the dimension of the geographic representation. As shown in Fig. 6 our model demonstrates strong robustness, with maximum performance fluctuation of 8.8%. Detailed results for all evaluation metrics are provided in Appendix C.
5.3 Additional Experiments and Discussion
Geographic Visualization: Fig. compares generated flow distributions of CRAFT and baselines Chicago (please refer Appendix C for all cities). CRAFT and GMEL are most identical to the real distributions. Notably, CRAFT outperforms other baselines with more realistic generated flow. This highlights of CRAFT's strong zero-shot generation ability and better alignment with geographic and flow patterns.
Temporal Length Extension: In general, most models default a horizon of T 24, where most baselines remain stable. Increasing T both raises task difficulty and enriches sample information, so an ideal model should exploit the enriched information from bigger while remaining stable in performance loss. To further explore the potential of each model's l long horizon generation, we trained

---

several baselines and CRAFT on {2T, .T. } as (48, 72,... 168). As Fig. shows: (1) All models' performance drops as T grows, revealing a common long-horizon bottleneck. (2) Diffwave exhibits high variance and poor accuracy, while diffusion-based DiT and DDPM remain steadier and more accurate. (3) CRAFT consistently achieves SOTA and outperforms all these baselines, demonstrating its superior potential in long-horizon generation. More results are in Appendix C.
Conclusion
In this paper, we propose CRAFT, model capable of zero-shot traffic flow generation. Unlike existing approaches, CRAFT is specifically designed for cross-city scenarios and advantage in generating high-quality traffic flow data in cities with limited or historical flow records. Specifically, we identify two key challenges: domain shift and insufficient condition. To address these, we introduce Geographic Feature Alignment (GFA) domain shift and Retrieval- Augmentation (RCA) for insufficient condition. As both GFA and RCA serve as concise plug-and-lay modules, our method requires no additional modifications to the diffusion backbone. Extensive experiments on four real-world datasets demonstrate the broad effectiveness of CRAFT, and further illustrates the great potential of CRAFT the fields urban planning and traffic management.
Condition
Restrict by available datasets, CRAFT is currently validated on in-out flow generation, and we have not yet explored its ability on other data types like origin-destingee destination (OD) flows. Furthermore, restricted by computational resources, we do not conduct experiments on temporal horizon more than 168. Actually. from Fig. 7 CRAFT remains the most stable across varying temporal horizons, demonstrating its great potential for extension to longer temporal horizons.
Limitations:
Acknowledgements
Jingyuan Wang's work was partially supported by the National Natural Science Foundation of China (No. 72222022, 72171013, 72242101) and the Fundamental Research Funds for the Central Universities (JKF-202501722618)),
References
[1] Hugo Barbosa, Mare Barthelemy, Gourab Ghoshal, Charlotte R James, Maxime Lenormand, Thomas Louail, Ronaldo Menezes, José Ramasco, Filippo Simini, and Marcello Tomasini. Human mobility: Models and applications. Physics Reports, 734:1-74, 2018
[2] Nicolas Bonneel, Michiel Van De Panne, Sylvain Paris, and Wolfgang Heidrich. Displacement interpolation using lagrangian mass transport. Proceedings of the 2011 SIGGRAPH Asia conference, pages 1-12, 2011.
[3] Haoye Chai, Tong Li, Fenyu Jiang, Shiyuan Zhang, and Yong Li. Knowledge guided conditional diffusion model for controllable mobile traffic generation. In Companion Proceedings of the ACM Web Conference 2024, pages 851-854, 2024.
[4] Jiawei Cheng. Jingyuan Wang, Yichuan Zhang, Jiahao Ji, Yuanshao Zhu, Zhibo Zhang, and Xiangyu Zhao. Poi-enhancer: An 11m basddaee semantic enhancement framework for poi represen- tation Icarning. In Proceedings of the AAAI conference on artificial intelligence, volume 39, pages 11509-11517. 2025.
[5] Abhimanyu Das, Weihao Kong, Rajat Sen, and Yichen Zhou A decoder-only foundation model for time-series forecasting. In Forty-first International Conference on Machine Learning, 2024.
[6] Rémi Flamary, Nicolas Courty, Alexandre Gramfort, Mokhtar Z. Alaya, Aurélie Boisbunon, Stanislas Chambon, Laetitia Chapel, Adrien Corenflos, Kilian Fatras, Nemo Fournier, Léo Gautheron, Nathalie T.H. Gayraud, Hicham Janati, Alain Rakotomamonjy, levgen Redko, Antoine Rolet, Antony Schutz, Vivien Seguy, Danica J. Sutherland, Romain Tavenard, Alexander Tong, and Titouan Vayer. Pot: Python optimal transport. Journal of Machine Learning Research, 22(78): 1-8, 2021. URL http: /mwrr../rr/////// org/ wttggppprr////eeeee/ee/eee ppapers/v22/2222 451. html
10

---

[7] Yunfan Gao, Yun Xiong, Xinyu Gao, Kangxiang Jia, Jinliu Pan, Yuxi Bi, Yixin Dai, Jiawei Sun, Haofen Wang, and Haofen Wang. Retrieval-auge -uggeeeted generation for large language models: A survey, arXiv preprint 2rXiv:23/2.
2023.
[8] Ian J Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial nets, Advances in Neural Information Processing Systems, 27, 2014.
[9] Chengkai Han, Jingyuan Wang. Yongyao Wang. Xie Yu, Hao Lin, Chao Li, and Junjie Wu. Bridging traffic state and trajectory for dynamic road network and trajectory representation learning. In Proceedings of the AAAI Conference Artificial Intelligence, volume 39, pages 11771, 2025.
[10] William Harvey, Saeid Naderiparizi, and Frank Wood. Conditional image generation by conditioning variational auto-encoders. arXiv preprint arXiv:2102.12033 2021.
[11] Kethmi Hirushini Hettige, Jiahao Ji, Shili Xiang, Cheng Long, Gao Cong, and Jingyuan Wang. Airphynet: Harnessing physics-guided neural networks for air quality prediction. arXiv preprint arXiv:2402.03784, 2024.
[12] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in Neural Information Processing Systems, 33:6840-683 6851, 2020.
[13] Sepp Hochreiter and Jürgen Schmidhuber. Long short-term memory. Neural Compulation, 9 (8):1735-1780, 1997.
[14] Shuodi Hui, Huandong Wang, Tong Li, Xinghao Yang. Xing Wang. Junlan Feng, Lin Zhu, Chao Deng, Pan Hui, Depeng Jin, al. Large-scale urban cellular traffic generation via knowledge-enhancee gans with multi multi-periodic patterns. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 4206-4206, 2023.
[15] Jiahao Ji, Jingyuan Wang, Zhe Jiang, Jiawei Jiang, and Hu Zhang. Stden: Towards physics- guided neural networks for traffic flow prediction. In Proceedings of the AAAI conference on artificial intelligence, volume 36, pages 4048-4056, 2022.
[16] Jiahao Ji, Jingyuan Wang, Junjie Wu, Boyang Han, Junbo Zhang, and Yu Zheng. Precision cityshield against hazardous chemicals threats via location mining and self-supervised learning, In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 3072-3080, 2022.
[17] Jiahao Ji, Jingyuan Wang, Chao Huang, Junjie Wu, Boren Xu, Zhenhe Wu, Junbo Zhang, and Yu Zheng. Spatio-temporal self supervised learning for traffic flow prediction. Proceedings of the AAAI conference artificial intelligence, volume 37, pages 4356-4364, 2023.
[18) Jiawei Jiang, Chengkai Han, Wayne Xin Zhao, and Jingyuan Wang. Paformer: Propagation delay-aware dynamic long-range transformer for traffic flow prediction. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 37, pages
2023.
[19] Jiawei Jiang, Dayan Pan, Houxing Ren, Xiaohan Jiang, Chao Li. and Jingyuan Wang. Self- supervised trajectory representation learning with temporal regularities and travel semantics. In 2023 IEFE 39th international conference data engineering (ICDE), pages 843-855. IEEE, 2023.
[20] Baoyu Jing, Si Zhang, Yada Zhu, Bin Peng, Kaiyu Guan, Andrew Margenot, and Hanghang Tong. Retrieval based time series forecasting. arXiv preprint arXiv:2209. /3525, 2022.
[21] Alexandra Kapp. Julia Hansmeyer, and Helena Mihaljević. Generative models for synthetic urban mobility data: A systematic literature review. ACM Computing Surveys, 56(4):1-37, 2023.
[22] Diederik Kingma, Max Welling, et al. Auto-encoding variational bayes, 2013.
[23] Zhifeng Kong, Wei Ping, Jiaji Huang, Kexin Zhao, and Bryan Catanzaro. Diffwave: versatile diffusion model for audio synthesis. arXiv preprint arXiv:2009.0976) 2020.
11

---

[24] Patrick Lewis, Ethan Perez, Aleksandra Piktus. Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih. Tim Rocktäschel, et al. Retrieval-augmentt generation for knowledge-intensive nlp tasks. Advances in Neural Information Processing Systems, 33:9459 -944,, 2020.
[25] Jingwei Liu, Ling Yang. Hongyan Li, and Shenda Hong. Retrieval-augmented diffusion models for time series forecasting. Advances in Neural Information Processing Systems, 37:2766-2786. 2024.
[26] Zehua Liu, Jingyuan Wang. Zimeng Li, and Yue He. Full bayesian significance testing for neural networks traffic forecasting. In Proceedings of the Thirty-Thine International Joint Conference Artificial Intelligence (IICAI), 2024.
[27] Zhicheng Liu, Fabio Miranda, Weiting Xiong, Junyan Yang, Qiao Wang, and Claudio Silva. Learning geo-contextual embeddings for commuting flow prediction. In Proceedings of the AAAI conference on artificial intelligence, volume 34, pages 808-816, 2020.
[28] A Paolo Masucci, Joan Serras, Anders Johansson, and Michael Batty. Gravity versus radiation models: On the importance of scale and heterogeneity in commuting flows. Physical Review E-Statistical, Nonlinear, and Soft Matter Physics, 88(2):022812, 2013.
[29] Mehdi Mirza and Simon Osindero. Conditional generative adversarial nets. arXiv preprint arXiv:1411.122 1784, 2014.
[30] Kanghui Ning, Zijie Pan, Yu Liu, Yushan Jiang, James Y Zhang, Kashif Rasul, Anderson Schneider, Lintao Ma, Yuriy Nevmyvaka, and Dongjin Song. Ts-rag: Retrieval-augment generation based time series foundation models are stronger zero-shot forecaster. arXiv preprint arXiv:2503.07649, 2025.
[31] William Peebles and Saining Xie. Scalable diffusion models with transformers. In Proceedings of the IEEE/CVF international conference on computer vision, pages 4195-4205, 2023.
[32] Gabriel Peyré, Marco Cuturi, et al. Computational optimal transport: with applications to data science. Foundations and Trends@ in Machine Learning, 11(5-6):355-677, 2019.
[33] Can Rong, Jingtao Ding, Zhicheng Liu, and Yong Li. Complexity-aware large scale origin- destination network generation via diffusion model. arXiv preprint arXiv:2306.0488 048733 2023.
[34] Can Rong, Jie Feng. and Jingtao Ding. Goddag: Generating origin-destination flow for new cities via domain adversarial training. IEEE Transactions on Knowledge and Data Engineering, (011)))0048810055 2023.
[35] Can Rong, Huandong Wang, and Yong Li. Origin-destination network generation via gravity- guided gan. arXiv preprint 2:Xi::2300603399 2023.
[36] Can Rong, Jingtao Ding, Yan Liu, and Yong Li. A large-scale benchmark dataset for commuting origin-destination matrix generation. arXiv preprint arXiv:2407. 15823, 2024.
[37] Can Rong, Zhicheng Liu, Jingtao Ding, and Yong Li. Learning to generate temporal origin- destination flow based-on urban regional features and traffic information. ACM Transactions on Knowledge Discovery from Data, 18(6): 1-1,, 2024.
[38] Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-net: Convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-assisted Intervention-MICCA 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18, pages 231-241. Springer, 2015.
[39] Gerard Salton and Christopher Buckley. Term- weinhtiigt approaches in automatic text retrieval. Information Processing & Management, 24(5):513-523, 1988.
[40] Erzhuo Shao, Huandong Wang. Jie Feng, Tong Xia, Hedong Yang, Lu Geng, Depeng Jin, and Yong Li. Deepflowgen: Intention-aware fine grained crowd flow generation via deep neural networks. IEEE Transactions on Knowledge and Data Engineering, 34(12):5693-577, 2021.
12

---

[41] Yunsheng Shi, Zhengjie Huang. Shikun Feng, Hui Zhong, Wenjin Wang, and Yu Sun. Masked label prediction: Unified message passing model for semi-supervised classification, arXiv preprint arXiv:2009.03:3 03500, 2020.
[42] Filippo Simini, Marta C González, Amos Maritan, and Alszót-Lááz7óó Barabási. universal model for mobility and migration patterns. Nature, 484(7392):96-100) 2012.
[43] Filippo Simini, Gianni Barlacchi, Massimilano Luca, and Luca Pappalardo. A deep gravity model for mobility flows generation. Nature communications, 12(1):6576, 2021.
[44] Kutay Tire, Ege Onur Taga, Muhammed Emrullah Iidiz, and Samet Oymak. Retrieval augmented time series forecasting. arXiv preprint arXiv:241 1.08249, 2024.
[45] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan Gomez, Fukasz. Kaiser, and Illia Polosukhin. Attention is all you need. Advances in Neural Information Processing Systems, 30, 2017.
[46] Jingyuan Wang, Yating Lin, Junjie Wu, Zhong Wang, and Zhang Xiong. Coupling implicit and explicit knowledge for customer volume prediction. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 31, 2017.
[47] Jingyuan Wang. Xiaojian Wang, and Junjie Wu. Inferring metapopulation propagation network for intra-city epidemic control and prevention. In Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery and data mining, 2018.
[48] Jingyuan Wang, Jiawei Jiang. Wenjun Jiang. Chao Li, and Wayne Xin Zhao. Libeity: An open library for traffic prediction. In Proceedings of the 29th International Conference on Advances in Geographic Information Systems, pages 145-148, 2021.
[49] Jingyuan Wang, Xin Lin, Yuan Zuo, and Junjie Wu. Dgeye: Probabilistic risk perception and prediction for urban dangerous goods management. ACM Transactions on Information Systems (TOIS), 3--3),1-30, 2021.
[50] Jingyuan Wang, Jiahao Ji, Zhe Jiang, and Leilei Sun. Traffic flow prediction based spatiotem- poral potential energy fields. IEEE Transactions on Knowledge and Data Engineering, 35(9): 9073-9087, 2022.
[51] Jingyuan Wang, Yujing Lin, and Yudong Li. Gtg: Generalizable trajectory generation model for urban mobility. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 39, pages 834-842, 2025.
[52] Alan Geoffrey Wilson. A family of spatial interaction models, and associated developments. Environment and Planning A, 1(1):1-3,, 1971.
[53] Yifan Yang, Jingyuan Wang, Xie Yu, and Yibang Tang. Hygmap: Representing all types of map entities via heterogeneous hypergraph.
[54] Jiexia Ye, Weiqi Zhang, Ke Yi, Yongzi Yu, Ziyue Li, Jia Li, and Fugee Tsung. A survey of time series foundation models: Generalizing time series representation with large language model. arXiv preprint wxXiv:2405.0.355 2024.
[55] Xie Yu, Jingyuan Wang, Yifan Yang, Qian Huang, and Ke Qu Bigcity: A universal spatiotem- poral model for unified trajectory and traffic state data analysis. In 2025 IEEE 41 International Conference Data Engineering (ICDE), pages 4455-4469. IEEE, 2025.
[56] Shiyuan Zhang, Tong Li. Shuodi Hui, Guangyu Li. Yanping Liang, Li Yu. Depeng Jin, and Yong Li. Deep transfer learning for city-scale cellular traffic generation through urban knowledge graph. Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 4851, 2023.
[57] Wentao Zhang, Jingyuan Wang, Yifan Yang, et al. Veccity: A taxonomy-guided library for map entity representation learning. arXiv preprint arXiv:2411.00874, 2024.
13

---

[58] Zhilun Zhou, Jingtao Ding, Yu Liu, Depeng Jin, and Yong Li. Towards generative modeling of urban flow through knowledge knowledge-enhaneee entannced denoising diffusion. In Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems, pages 1-12, 2023.
[59] Yuanshao Zhu, Yongchao Ye, Shiyao Zhang, Xiangyu Zhao, and James Yu. Difftraj: Generating gps trajectory with diffusion probabilistic model. Advances in Neural Information Processing Systems, 36:65168-65 65188 2023.
[60] George Kingsley Zipf. The p1 p2 / d hypothesis: on the intercity movement of persons. American Sociological Review, 11(6)6677-666 1946.
14

---

NeurIPS Paper Checklist
1. Claims
Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Answer: [Yes]
Justification:
The main claims made in the abstract and introduction accurately summarize the key contributions of our work.
Guidelines:
The answer NA means that the abstract and introduction do not include the claims made the paper.
The abstract and/or introduction should clearly state the claims made, including the contributions made in the paper and important assumptions and limitations. A No or
NA answer to this question will not be perceived well by the reviewers.
The claims made should match theoretical and experimental results, and reflect how much the results can be expected to generalize to other settings.
is fine to include aspirational goals as motivation as long as is clear that these goals are not attained by the paper.
2. Limitations
Question: Does the paper discuss the limitations the work performed by the authors?
Answer: [Yes]
Justification:
We include the limitations of our work in Section
Guidelines:
The answer NA means that the paper has no limitation while the answer No means that the paper has limitations, but those are not discussed in the paper.
The authors are encouraged to create a separate "Limitations" section their paper.
The paper should point out any strong assumptions and how robust the results are to violations of these assumptions (e.g., independence assumptions, noiseless settings, model well-specification, asymptotic approximations only holding locally). The authors should reflect how these assumptions might be violated in practice and what the implications would be.
The authors should reflect on the scope of the claims made, e.g., if the approach was only tested on few datasets or with few runs. In general, empirical results often depend on implicit assumptions, which should be articulated.
The authors should reflect on the factors that influence the performance of the approach. For example, facial recognition algorithm may perform poorly when image resolution is low images are taken in low lighting. Or speech-to-text system might not be used reliably to provide closed captions for online lectures because it fails to handle technical jargon.
The authors should discuss the computational efficiency of the proposed algorithms and how they scale with dataset size.
If applicable, the authors should discuss possible limitations of their approach to address problems of privacy and fairness.
While the authors might fear that complete honesty about limitations might used by reviewers as grounds for rejection, worse outcome might be that reviewers discover limitations that aren't 't acknowledged judgment and recognize that individual actions in favor of transparency play impor- tant role in developing norms that preserve the integrity of the community. Reviewers will be specifically instructed to not penalize honesty concerning limitations.
the paper. The authors should use their best
3. Theory assumptions and proofs
Question: For each theoretical result, does the paper provide the full set of assumptions and complete (and correct) proof?
Answer: [NA]
15

---

Justification:
This is not theoretical paper.
Guidelines:
The answer NA means that the paper does not include theoretical results.
All the theorems, formulas, and proofs in the paper should be numbered and cross- referenced.
All assumptions should be clearly stated or referenced the statement of any theorems.
The proofs can either appear the main paper or the supplemental material, but they appear in the supplemental material, the authors are encouraged to provide short proof sketch to provide intuition.
Inversely, any informal proof provided in the core of the paper should be complemented by formal proofs provided in appendix supplemental material.
Theorems and Lemmas that the proof relies upon should be properly referenced.
4. Experimental result reproducibility
Does the paper fully disclose all the information needed to reproduce the main ex- perimental results of the paper to the extent that it affects the main claims and/or conclusions of the paper (regardless of whether the code and data are provided or not)?
Question:
Answer: [Yes]
Justification: Our method is well described in Section f and detailed experiment settings, code and data provided by the URLs in Abstract.
Guidelines:
The answer NA means that the paper does not include experiments. If the paper includes experiments, No answer this question will not be perceived well by the reviewers: Making the paper reproducible is important, regardless of whether the code and data are provided or not.
If the contribution is dataset and/or model, the authors should describe the steps taken to make their results reproducible or verifiable.
Depending on the contribution, reproducibility can be accomplished in various ways. For example, if the contribution is novel architecture, describing the architecture fully might suffice, or if the contribution is a specific model and empirical evaluation, it may be necessary to either make possible for others to replicate the model with the same dataset, or provide access to the model. In general. releasing code and data is often one good way accomplish this, but reproducibility can also be provided via detailed instructions for how to replicate the results, access to a hosted model (e.g.. in the case of large language model), releasing of a model checkpoint, or other means that are appropriate to the research performed.
While NeurIPS does not require releasing code, the conference does require all submis- sions to provide some reasonable avenue for reproducibility, which may depend on the nature of the contribution. For example
(a) If the contribution is primarily a new algorithm, the paper should make it clear how reproduce that algorithm. (b) If the contribution is primarily
model architecture, the paper should describe the architecture clearly and fully.
(c) If the contribution is a new model (e.g., a large language model), then there should either be way to access this model for reproducing the results or way to reproduce the model (e.g., with an open-source dataset instructions for how to construct the dataset).
(d) We recognize that reproducibility may be tricky in some cases, in which case authors are welcome deseribe the particular way they provide for reproducibility, In the case of closed-source models, it may be that access to the model is limited in some way (e.g., registered users), but it should be possible for other researchers to have some path to reproducing or verifying the results.
5. Open access to data and code
Question: Does the paper provide open access to the data and code, with sufficient instruc- tions to faithfully reproduce the main experimental results, as described in supplemental material?
16

---

Answer:
[Yes]
Justification:
The URLs of the anonymized code and data have been provided in the Abstract.
Guidelines:
The answer NA means that paper does not include experiments requiring code.
Please see the NeurIPS code and data submission guidelines (https://nips.. cc/
for more details.
While we encourage the release of code and data, we understand that this might not be possible, so "No" an acceptable answer. Papers cannot be rejected simply for not including code, unless this is central to the contribution (e.g., for new open-source benchmark).
The instructions should contain the exact command and environment needed to run to reproduce the results. See the NeurlPS code and data submission guidelines (https
/
wwtsSubbmiiiioooiiioooooooio for more details.
The authors should provide instructions on data access and preparation, including how access the raw data, preprocessed data, intermediate data, and generated data,
The authors should provide scripts to reproduce all experimental results for the new proposed method and baselines. If only subset of experiments are reproducible, they should state which ones are omitted from the script and why.
At submission time, to preserve anonymity, the authors should release anonymized versions (if applicable).
Providing as much information as possible in supplemental material (appended the paper) is recommended, but including URLs to data and code is permitted.
6. Experimental setting/details
Question: Does the paper specify all the training and test details (e.g., data splits, hyper- parameters, how they were chosen, type of optimizer, etc.) necessary to understand the results?
Answer: [Yes]
Justification: Our method is well described in Section and detailed experiment setting is provided Appendix
Guidelines:
The answer NA means that the paper does not include experiments.
The experimental setting should be presented in the core of the paper to level of detail that is necessary to appreciate the results and make sense of them.
The full details can be provided either with the code, in appendix, or as supplemental material.
7. Experiment statistical significance
Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?
Question:
Answer: [No]
Justification:
We reported the best results within the range of adjustable parameters for each
method.
Guidelines:
The answer NA means that the paper does not include experiments.
The authors should answer "Yes" if the results are accompanied by error bars, confi- dence intervals, or statistical significance tests, at least for the experiments that support the main claims of the paper.
The factors of variability that the error bars capturing should be clearly stated (for example, train/test split, initialization, random drawing of some parameter, or overall with given experimental conditions).
The method for calculating the error bars should be explained (closed form formula, call to a library function, bootstrap, etc.)
The assumptions made should be given (e.g., Normally distributed errors).
17

---

It should be clear whether the error bar is the standard deviation or the standard error of the mean.
is OK to report 1-sigma error bars, but one should state it. The authors should preferably report a 2-sigma error bar than state that they have 96% CI, if the hypothesis of Normality of errors is not verified.
For asymmetric distributions, the authors should be careful not to show in tables or figures symmetric error bars that would yield results that are out range (e.g. negative enror rates).
If error bars reported in tables plots, The authors should explain in the text how they were calculated and reference the corresponding figures or tables in the text.
8. Experiments compute resources
For each experiment, does the paper provide sufficient information on the com- puter resources (type of compute workers, memory, time of execution) needed reproduce the experiments?
Question:
Answer:
[Yes]
Justification: The computer resources needed to reproduce the experiments are presented in Appendix B.
Guidelines:
The answer NA means that the paper does not include experiments.
The paper should indicate the type of compute workers CPU or GPU, internal cluster, or cloud provider, including relevant memory and storage.
The paper should provide the amount of compute required for each of the individual experimental runs as well as estimate the total compute.
The paper should disclose whether the full research project required more compute than the experiments reported in the paper (c.g., preliminary or failed experiments that didn't 't make into the paper).
9. Code of ethics
Question: Does the research conducted in the paper conform, in every respect, with the NeurlPS Code of Ethics ttttps://nerrip..ooom http://wwrrp..cccoooo..//cctccaa
Answer: [Yes]
Justification:
We read the NeurlPS Code of Ethics and strictly adhere to
Guidelines:
The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics.
If the authors answer No, they should explain the special circumstances that require deviation from the Code of Ethics.
The authors should make sure to preserve anonymity (e.g., there a special consid- eration due to laws or regulations in their jurisdiction).
10. Broader impacts
Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?
Answer:
[Yes]
Justification: discuss impacts of our method Section
Guidelines:
The answer NA means that there no societal impact of the work performed.
If the authors answer NA or No, they should explain why their work has no societal impact why the paper does not address societal impact.
Examples of negative societal impacts include potential malicious or unintended uses (e.g., disinformation, generating fake profiles, surveillance), fairness considerations (e.g., deployment of technologies that could make decisions that unfairly impact specific groups), privacy considerations, and security considerations.
18

---

The conference expects that many papers will be foundational research and not tied particular applications, let alone deployments. However, if there is direct path to any negative applications, the authors should point it out. For example, it is legitimate to point out that an improvement in the quality of generative models could be used to generate deepfakes for disinformation. On the other hand, it is not needed to point out that a generic algorithm for optimizing neural networks could enable people to train models that generate Deepfakes faster.
The authors should consider possible harms that could arise when the technology is being used as intended and functioning correctly, harms that could arise when the technology being used intended but gives incorrect results, and harms following from (intentional or unintentional) misuse of the technology.
If there negative societal impacts, the authors could also discuss possible mitigation strategies (e.g., gated release of models, providing defenses in addition to attacks, mechanisms for monitoring misuse, mechanisms monitor how system learns from feedback over time, improving the efficiency and accessibility of MI..
11. Safeguards
Does the paper describe safeguards that have been put in place for responsible release of data models that have high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)?
Question:
Answer: [NA]
Justification: The paper poses no such risks.
Guidelines:
The answer NA means that the paper poses such risks.
Released models that have high risk for misuse or dual-use should be released with necessary safeguards to allow for controlled use of the model, for example by requiring that users adhere usage guidelines or restrictions to access the model or implementing safety filters.
Datasets that have been scraped from the Internet could pose safety risks. The authors should describe how they avoided releasing unsafe images.
We recognize that providing effective safeguards is challenging, and many papers do not require this, but we encourage authors to take this into account and make best faith effort.
12. Lieenses for existing assets
Question: Are the creators or original owners of assets (e.g., code, data, models), used in the paper. properly credited and are the license and terms of use explicitly mentioned and properly respected?
Answer: [Yes]
Justification: We have cited the relevant papers and provided links to download the data.
Guidelines:
The answer NA means that the paper does
existing assets.
The authors should cite the original paper that produced the code package or dataset.
The authors should state which version of the asset is used and, if possible, include URL.
The name of the license (c.g., CC-BY 4.0) should be included for each asset.
For scraped data from a particular source (c.g., website), the copyright and terms of service of that source should be provided.
If assets are released, the license, copyright information, and terms of use in the package should be provided. For popular datasets, paperswi thcode. com com/datasets has curated licenses for some datasets. Their licensing guide can help determine the license of dataset.
For existing datasets that are re-packaged, both the original license and the license of the derived asset (if it has changed) should be provided.
19

---

If this information is not available online, the authors are encouraged to reach out to the asset's 's creators.
13. New assets
Question: Are new assets introduced in the paper well documented and is the documentation provided alongside the assets?
Answer: [Yes]
We release the code URLs in Abstract and provide documentation the form of README.
Justification:
Guidelines:
The answer NA means that the paper does not release new assets.
Researchers should communicate the details of the dataset/code/moodl as part of their submissions via structured templates. This includes details about training, license, limitations, etc.
The paper should discuss whether and how consent obtained from people whose asset is used.
At submission time, remember to anonymize your assets (if applicable). You can either create an anonymized URL or include an anonymized zip file.
14. Crowdsourcing and research with human subjects
For crowdsourcing experiments and research with human subjects, does the paper include the full text of instructions given to participants and screenshots, if applicable, as well details about compensation (if any)?
Question:
Answer: [NA]
Justification:
The paper does not involve crowdsourcing nor research with human subjects.
Guidelines:
The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.
Including this information in the supplemental material is fine, but if the main contribu- tion of the paper involves human subjects, then much detail as possible should be included the main paper.
According to the NeurlPS Code of Ethics, workers involved in data collection, curation, or other labor should be paid at least the minimum wage in the country of the data collector.
15. Institutional review board (IRB) approvals or equivalent for research with human subjects
Question: Does the paper describe potential risks incurred by study participants, whether such risks were disclosed to the subjects, and whether Institutional Review Board (IRB) approvals (or equivalent approval/review based the requirements of your country institution) were obtained?
Answer: [NA]
Justification: The paper does not involve crowdsourcing nor research with human subjects.
Guidelines:
The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.
Depending the country in which research is conducted, IRB approval (or equivalent) may be required for any human subjects research. If you obtained IRB approval, you should clearly state this in the paper.
We recognize that the procedures for this may vary significantly between institutions and locations, and we expect authors to adhere to the NeurlPS Code of Ethics and the guidelines for their institution.
For initial submissions, not include any information that would break anonymity (if applicable), such the institution conducting the review.
20

---

16. Declaration of LLM usage
Question: Does the paper describe the usage of LLMs if it is an important, original, or non-standard component of the core methods in this research? Note that if the LLM is used only for writing, editing, or formatting purposes and does not impact the core methodology. scientific rigorousness, originality of the research, declaration is not required.
Answer: [NA]
Justification: LLM is used only for polishing writing.
Guidelines:
The answer NA means that the core method development in this research does not involve LLMs as any important, original, non-standard components.
Please refer to L.LM policy (https:/ for what should or should not be described.
21

---

A Model Details & Hyperparameters
A.1 Basic Geographic Features
We extract urban geographic features from OpenStreetMap (OSM) and WorldPop 6 data. The original geographic features include the following three aspects.
POI features (denoted as f(pat)) reflect the function of a region. We attempt to construct the POI semantic features of a region by counting the quantities of various POI categories. However, the quantities of different POI categories exhibit significant imbalance, for instance, the total number of commercial POIs far larger than that of residential POIs. This imbalance can easily cause the model to overlook the influence of POI categories with smaller quantities. Therefore, we employ the TF-IDF algorithm [39] to extract POI features. Specifically, we treat each POI category as "word", consider all POIs within a region as "document", and define the entire city as the word for a document, the algorithm automatically incorporates the word's frequency in the corpus for weighted analysis.
"corpus". When calculating the importance
Road features (denoted (road)) reflect the transportation attribute of a region. We calculate the total length of all categories of road segments within region. Regions with dense road networks and large number of trunk roads usually have convenient transportation and tend to generate higher volume of traffic flow.
Population features (denoted as f(Po)) reflect the traffic potential of region. Regions with higher population density are more likely to generate higher volume of traffic flow. We obtained the United Nations (UN)-adjusted 100m resolution national population data from WorldPop and counted the population number in each manually partitioned rectangular regions.
A.2 Optimal Transport Problem
Optimal Transport (OT) is mathematical problem aiming to find the most efficient way to move mass from source distribution to target distribution. It was introduced by Gaspard Monge in 1781. When both the source and target distributions are represented by enumerable samples (N, samples for source and N₂ samples for target), the OT problem can be formally defined as
T = arg min PER X.N ΣT. Diss
i
(15)
s.t. T1 w. and 1 = Wi,
where E NR.N.NN is the cost matrix (distance matrix) defining the cost to move mass from source distribution to target distribution, w. E R N. and w. E R. are the weights of each samples in the source and target distribution. The total weights of both w, and w; equal to 1. The objective of the OT problem is to find a transportation plan T that minimizes the total transportation cost under the weights-equal constraint.
OT problem has two main functions: (1) Measuring the distance between two distributions; (2) Finding the correspondences between two distributions. We employed both. Specifically, we treated the geographic representations of regions in the source and target cities as two mass distributions, with each region assigned the same weight. we used the solution of the OT problem (also known as the Wasserstein distance) to measure the geographic representation distance of the source and target cities, and treated it loss to optimize the spatial encoder, thereby pulling the correspondent regions in representation space.
As illustrated in Equation [5] the OT problem is a linear programming problem, and we use an OT solver based on the network simplex algorithm 2 to address it. Thanks to the Python Optimal Transport (POT) tool, we can conveniently calculate the solution of OT problem.
org
https:://iituu..com//ee
22

---

Table 3: Hyperparameters setting for CRAFT
Hyperparameter
Setting value
Diffusion steps (n)
500
31 ~ fn
0.0002 ~ 0.04 (linear)
GraphTransformer (GT) layers
Temporal encoder layers
GT attention heads
Retrieval top-K value
Batch size
256
Learning rate
5 x 10-6
Training epochs
300
Regional geographic representation (h,) dimensions
128
Temporal encoder hidden dimensions (dimensions of wi,1)
256
Hour embedding (thour) dimensions
64
Weekday embedding (tweek) dimensions
Month embedding (tmonth) dimensions Condition (c,,t) dimensions
64
256
A.3 Implementation Details
For the proposed CRAFT method, provide the hyperparameter settings in Table t facilitate the reproducibility by researchers. All these parameters are recommended values, not fixed, and can be adjusted according the dataset and experimental environment. During training, the AdamW optimizer was used. To enhance stability, the EMA (Exponential Moving Average) mechanism was adopted to train the diffusion model.
B
Details of Experimental Settings
B.1 Experimental Environment
All neural network models (including CRAFT and other baselines) are implemented in Py Torch and trained on a single NVIDIA RTX 3090 GPU. The experimental machine ran on Ubuntu 20.04.6 LTS, was equipped 24-core Intel(R) Xeon(R) Silver CPU, and had 503 GB of RAM. The training time for all models on a single dataset did not exceed 16 hours.
B.2 Datasets and Pre-processing
Table 4: Data description
Datasets
Chicago
Washington D.C.
Toronto
New York City
Trips (x (x1)))
5136
4011
2395
35080 2023.01-2023.1
Time range
2023.01-2023.1
0023.01-2023.12
2020.01-2020.12
Regions
73
82
61
96
POIs
17205
14070
20621
50776
We conducted experiments using the traffic flow datasets of four cities, namely Chicago, Washington D.C., Toronto, and New York City. The original data are all trip records shared bicycles, which include the latitude and longitude of the starting and ending points of users' trips well as timestamps. We associated the trips with the manually partitioned urban regions, and counted the number of bicycles entering and leaving each region within each hour, which served as the traffic flow values. Details of the datasets are presented Table 4
In fact, the user trip data is sparse, which leads to the instability of the traffic flow trend in original data. This also common problem researches about traffic flow data. In response to this, we have adopted two processing methods: (1) We have filled in the missing values in the traffic flow sequence through linear interpolation. For the situation where there are values at the previous and subsequent time steps but missing values in the middle, have filled them with the average of the
23

---

previous and subsequent values. (2) We have used the moving average method to smooth the values within a window with time length of For the processed data, we have set sliding window with length of T to extract the training and test samples. If there are still missing values exceeding 5% in certain sample, we will discard that sample.
B.3 Baseline Methods
The baseline models used in the experiment include two types of deep learning traffic flow generation models that have emerged in the literature in recent years: Static Flow Generation Models and Dynamic Flow Generation Models. Static Flow Generation Models directly estimate traffic flow using the geographic features and temporal information of urban regions, and they include the following two models.
It uses two graph neural networks to extract features and can simultaneously predict the inflow and outflow as well as the OD (origin-destination) flow between regions.
GMEL :
DFG [40]: It uses deep cross network to extract the POI (point of interest) features inside and outside the region, conducts supervised training using the intention-aware pedestrian flow, and predicts the inflow and outflow of the region.
Dynamic Flow Generation Models use deep generative models to learn the complex distribution of data and map random noise into data samples. The selected baseline models are as follows.
KSTDiff 58
It uses the urban knowledge graph to extract the representations urban geographic entities, and then constructs a knowledge-enhanced spatiotemporal diffusion model to generate the inflow and outflow of regions.
CGAN [29]: This is conditional generative adversarial network. In the experiment, we use the original static geographic features and time embeddings as conditions to guide the generation of the GAN.
Diffwave [33 This is a diffusion model suitable for generating time series data and is often used in speech synthesis. In this experiment, use it to learn the distribution of traffic flow data. DiT :T: This a diffusion probabilistic model with a Transformer as the noise estimator.
DDPM [12 This is a diffusion probabilistic model U-Net as noise estimator for image synthesizing.
To be applied in traffic flow data generation, we replaced 2D convolutions with ID convolutions.
CVAE [00] This conditional variational autoencoder. Condition information is added both the Encoder and the Decoder to guide it to learn the conditional probability distribution of the data. We use the original static geographic features and time embeddings as conditions.
B.4 Evaluation metrics
We used three commonly used metries in traffic flow generation research to evaluate the quality of generated data, including Common Part of Commuters (CPC). Normalized Mean Absolute Error (NMAE), Normalized Root Mean Square Error (NRMSE). Since the the actual traffic flow values in different cities vary greatly, the traffic flow data generated by model are all normalized, which can reflect the relative magnitudes of traffic flows in different regions and at different time in the same city, All the metrics are calculated based on the normalized data, and the calculation formulas are as follows
2 DM min (0t. MA) M
CPC
Σn 0s + - Vi
M
NMAE = M ΣM 10: yal
1
(16)
max y/) mn:)
NRMSE V M M (0x y:)²
max (1)) min(ys)
where ÿ; the generated value, y/ is the real value, and M is the number of values of all samples in the test dataset. To enhance the stability of evaluation results, we grouped the data by the Region ID, month, weekday and hour of the target city's test samples and calculated the differences between the aroup-averaged values.
24

---

Additional Experiments
C.1 Sensitivity Analysis Results
(a) Indow-CPC
(b) IaDlo--MMAE
(c) Inflow-NRMSE
(d) OutloowCCCC
(e) Outflow-NMAE
(1) Outhow-NRMSE
Figure 8: Sensitivity analysis for the layer number sequence encoder
(a) Inflow-CPC
(b) Intiow-NMAE
(c) IRMS
(d) Outlow-CPC
(0) Outflow-NMAE (f) OutIow-NRMSE
Figure 9: Sensitivity analysis for the dimension of temporal embedding
(a) Inflow CPC
(h) Inflow NMAE (c) Mallow-NRMS
(d) Outflow CPC
Outfoow-NMMA -MMA (i) NRMSE
Figure 10: Sensitivity analysis for the dimension of geographic representation
To validate how different hyperparameter configurations affect model performance, we plotted the variation curves of evaluation metrics against three key hyperparameters: (1) the layer number of the sequence encoder; (2) the dimension of temporal embedding: (3) the dimension of the geographic representation. The results of sensitivity analyses across all datasets and evaluation metrics shown in the Fig. 8 Fig. and Fig. 10 We can observe that CRAFT demonstrates excellent robustness across all datasets and evaluation metries, eliminating the need for meticulous hyperparameter tuning to ensure superior model performance.
C.2 Temporal Length Extension Results
Overall Zero-shot Generation Performance in Target City. In the main experiments of the paper,
we set the generation time window length to T 24. We then progressively extended the time length T to the following values: (48, 72, 96, 120, 144, 168). The experimental setup remained consistent: training the model using data from three cities and performing zero-shot generation on fourth city. conclude that: (1) CRAFT achieved best performance in over 98.6% cases across all datasets and evaluation metrics, demonstrating that CRAFT exhibits state-of-the-art (SOTA) zero-shot generalization capabilities for sequence data generation varying temporal lengths. (2) CRAFT demonstrates stable performance, while methods such Diffwave and DDPM exhibit significant performance fluctuations under different temporal length settings.
Detailed evaluation results presented in Table 6 From these tables,
Data Utility Comparison. We compared the utility of generated data across different temporal lengths for traffic flow prediction tasks. Regardless of the value of T, we used the first 7/2 historical sequence input to predict the 7/2 future flow sequence. The downstream models are trained on generated data from various methods and tested on real data. Detailed results are presented in Table a 8 We found that in 86.5% of cases, the downstream model trained with CRAFT's generated data achieved best performance, indicating that CRAFT's generated data has the better utility than other basclines.
Computational Cost. As the length of temporal length increases, the model size and training time will also grow correspondingly. To observe this phenomenon in detail, we conducted experiments on a single NVIDIA RTX 3090 GPU, collected relevant statistical data during the training phase, and the results are shown in Table 5) We observe that our model's computational cost grows linearly with temporal length increases, but this effect weak. Extending the time length is acceptable in terms of computational cost.
25

---

Table 5: The relationship between computational cost and temporal length
T
Model size (Byte)
Train time (s/epoch)
Valid time (s/epcoh)
Avg memory (GB)
24
51781096
40,999
5.685
0.343
48
51830248
41.156
5.531
0.344
72
51879400
43.729
6.053
0.344
96
51928552
44.555
5.631
0.345
120
51977704
46.712
6,429
0.345
144
52026856
48.883
6.666
0.345
168
52076008
51.896
6.351
0.346
C.3
Visualization of the Traffic Flow Spatial Heatmap
We display the average traffic flow of real data and generated data on maps. The visualizations for all datasets and baselines are shown in Fig. [I This comparison intuitively demonstrates that the traffic data generated by CRAFT exhibits the highest similarity to real data in terms of spatial distribution, indicating that CRAFT can effectively capture the universal mapping relationship between geographic representations and traffic flow across different cities.
C.4 Visualization of Geographic Feature Alignment (GFA)
We employed t-SNE analysis to visualize the impact of the Traffic Flow Alignment (TFA) and Cross City Alignment (CCA) modules Geographic Feature Alignment. The results of the four experiments are presented in Fig. [2] Regardless of which city was chosen as the target, the alignment results exhibited similar conclusions: (1) Under the combined action of TFA and CCA, regions with high and low traffic volumes were well-separated in the representation space. (2) Without TFA, representations from different cities tended to cluster into multiple groups, with high- and low-traffic regions intermingled within the same cluster, making it difficult to distinguish and reducing the quality of conditions. (3) Without CCA, significant domain shift occurred between the source and target cities. Specifically, portion of the target city's region representations deviated from the concentrated representation area of the source city, potentially leading to poorer transferability.
26

---

Table 6: Cross-city traffic flow generation results with extended temporal length
Methel
City
CIC
XMAA
NE31SE
CC
NMAZ
NIDIE
CDC
NOUAD
30913
EPC
X01AZ
CPC
SMAZ
N0002
CTC
NMAA
NESSS
01133
0330
01153
0.354
0,422
01152
0336
0.497
0.130
03526
0.30
1.223
0.45
0.155
0.330
KSTDEE
3.35
0.88
0,860
0.900
0.30
0.99
0.341
0.444
0.53
0.399
1003
0.300
0485
DiEvars
0.60
2.56
0303
0.88
0455
0.50
0.66
0430
0.55
0.822
0.50
3.599
0360
0.477
0.255
0447
0
01388
1333
11415
11:11
01855
01313
12
1790
01.91
1197
0.48
1039
031
0411
CVAB
0478
3.39
0.38
0.55
0.38
0-44
0.36
0.390
0.53
0.20
0:53
0.10
0.57
0-00
GVEL
0218
0.72
0.00
2333
0,50
01440
0246
0.400
0.53
0.47
0.353
0.333
0.52
B/60
KETTE
0100
1.661
0014
0.35
0.00
0.36
0.535
0.501
0.11
0575
5364
0.7%
0.54
0151
0314
DiEvers
0469
3.45
136
0.50
0469
0.54
0.01
0482
0.991
0330
0433
3566
6353
0489
0355
056
(49)
0401
D0PM
5331
DA11
0.03
0.466
0.36
0.43
0.50
0391
0439
1310
0-200
OYAb
11412
3270
1090
1320
0397
113
0530
0131
0.:91
0337
0.31
6334
0.1
0422
0351
GMP
1.51
0.06
0.50
0.69
0.0
0.30
3.56
0.21
8.361
0.76
0.72
1363
6311
0311
0.00
(201
0554
0.28
0.24
0.352
K5TT4
0.41
0160
0.61
CYAAN
1/56
3157
1965
0300
6401
015
0.25
0146
01590
3.51
0.91
0651
(461
0.41
0.51
510)
5345
6651
6151
6005
0467
0368
0.36
0.38
0.50
5151
6.64
0.9
D606
0.67
CVAB
0358
5411
0.54
0.40
0365
0.23
0235
0.511
0.57
0455
0433
0.39
0.64
0354
CRAFY
0144
6.31
0.56
6191
0.20
0.79
0.30
0.311
0.111
0.91
3.61
61911
6196
6.38
6.97
0:36
GMBL
31.1
133
0357
0301
0356
0220
0.990
0.34
0,30
0455
0633
0.211
0353
0245
0211
KSTTE
D166
54%
1307
0.05
0.54
0.55
3.09
0263
1131
8710
035
100
CCAAN
0333
2254
050
0000
0.5
044)
0331
0334
046
3359
0403
0477
1610
1403
[401
0150
031
666
655
8:00
045
600
0.96
0.81
0353
535
6594
0.05
1409
30PM
0674
662
650
046
6099
0.50
035
033
0489
0660
036
0448
0312
BATT
5.51
CHAIT
6.798
8.595
0.37
0.53
6355
0.38
0.75
6.30
0.34
8781
0.00
0.20
6.78
8.56
6.344
6773
139
6,33
196
5461
1331
120
0471
1144
0.39
114
0351
0225
1123
0336
6314
045
1357
1223
(43)
KETEKF
3550
1003
0.05
0.5
0620
0.6
6331
0407
1456
1208
0.455
0055
545
1203
805
0481
1000
046
003
0555
0357
1473
55%
0308
0466
1409
1000
2411
D00M
2350
1850
0385
0.5
0356
0.8%
0464
0605
5485
0000
CWAE
0714
D402
075
0269
0-14
0.22
0000
0412
025
0.55
046
0287
0.96
D459
1050
C355
5450
1511
0250
630
635
1150
5350
555
0331
1133
6551
300 2000 Coltaaae 30N 1000 Cl..3 10004000000
5411
011
199
080
8666
0119
1413
0168
511
150
661
546
639
035
(55
0350
5599
6551
0111
141
0355
0.61
D00M
0350
0,55
8000
0310
0066
0.5%
0311
0373
046
0259
0377
1457
0200
BATC
0719
3377
006
010
0516
0115
116)
0.86
5117
646
1453
1/46
0.34
GM94
1311
635
0.08
015
000
0.90
0.00
5111
5161
0.50
1:00
0011
(.2)
[UKC
KSTD4E
0355
544
1344
6.5
615
432
532
6369
135
618
035
CUAAN
140
2311
196
100
0.96
113
043
0399
0411
038
1904
1954
(16)
EAT
0470
534
0.6
E354
6223
8.00
0335
0.00
0.80
0.96
510
6471
0151
5405
0335
BAGA
00317
028
0309
COAD
5455
0.53
0:21
0380
0273
0.35
0.85
0286
0470
0350
CRAFT
6734
8.155
6.81
0778
6:31
6.99
6357
6122
0.92
0.61
8118
6.87
6737
4136
0.88
636
0331
6,0
11515
5331
1351
6382
631
0112
0094
0157
0.80
0250
0377
6455
0.91
0407
0001
0311
2383
0211
0550
5100
625
0350
0.133
0.59
0162
0359
0:51
0226
0987
0.05
CLAAN
1642
2399
198
0416
036
100
0.9%
0605
11/00
0.61
0884
0392
0466
035
1340
035
2391
1444
1180
0.65
0031
0155
1360
049
047
034
0421
0.96
036
0.51
0150
0361
5448
146
CVAR
0/44
5451
036
0/45
0158
0361
6.43
0335
0331
0/21
CRAFT
0377
8312
0766
002
0.64
6131
0.99
136
0.54
8.44
1.56
6385
0146
1.14
0.28
27

---

Table 7: Data utility comparison on traffic flow prediction (LSTM)
Chicago
Washington, D.C.
Torunto
New York City
Gen
Pred
Inflow
Outflow
Inflow
Dunflow
Inflow
Oatlow
Inflow
Outfoww
MAE
RMSE
MAE
RMSE
MAE
RMSE
MAE
RMSE|
MMA
RMSE
MAB
RMSE
MME
RMSB
MAB
RMSE
Real
0.097
0.151
0.100
0.154
0.094
0.144
0.057
0.150
0.109
0.163
0.164
0.066
0.101
0,856
0.101
GMEL
0.167
0.230
0.166
0.225
0.202
0.254
0.210
0.268
0.241
0,303
0.251
0.320
0.092
0.144
(1001
0.142
DRG
0.283
0.418
0.289
0.423
0.134
0.250
0.136
0.206
0.319
0,426
0.321
0.427
0.070
0.126
0.069
0.125
KSTDAN
0.362
0,493
0.368
0.497
0.469
0.500
0.475
0.603
0.507
0.629
0.505
0.627
0.348
0.389
0.763
0.14
CGAN
LSTM
0.199
0.292
0.212
0.309
0.283
0.394
0.286
0.399
0.267
0.383
0.286
0.382
0.151
0.248
0,163
0.257
Dilfwave
0.221
0.299
0.332
0.297
0.276
0.354
0.211
0,108
0.211
DIT
0.232
0.235
01246
0.292
0.266
0.311
0.288
0.330
0,537
1:209
0.245
DDPM
0.113
0.174
0.115
0.171
0.124
0.153
0.122
0.183
0.117
0.171
0.122
0.175
0.055
0.113
0
0.110
CVAE
0,175
0.255
0.172
0.249
0.308
0.429
0.304
0:421
0.158
0,230
0.162
0.233
0.138
0.219
0.135
0.133
CRAFT
0,099
0.153
0.103
0.158
0.103
0,156
0.111
0.167
0.120
0.172
0.127
0.190
0.063
0.107
0.064
0.105
Real
0.104
0.156
0:107
0.159
0.005
0.145
0.099
0.1
0.114
0.169
0.115
0.10
0.089
0.106
0.059
0.105
GMEL
0.169
0.234
0.175
0.246
0.227
0.285
0.240
0.302
0.258
0.320
0.270
0.349
0.033
0.140
0.007
0.158
DEG
LSTN 12
0,30
0.425
0.306
0.138
0,142
0,213
0.151
0.226
0.368
0.681
0.370
0.162
0.023
0.129
0,073
0.129
KSTDiA
0.256
0,340
0.282
0.381
0.320
0,400
0.314
0.372
0.336
0.360
0.340
0.463
0.224
0,30
0,306
0.343
OGAN
0.208
0.300
0.206
0.204
0.204
Dillwgre
0.244
0.310
0.39
0.342
0.312
0.352
0.296
0.363
0.360
0.463
0.775
0,236
0,142
0.214
DIT
0.259
0.300
0.288
0.283
0.292
0.312
0.306
0.354
0.221
0,366
0.12
0.55
0.00
0.251
0,204
0.244
DEPM
0.116
0.171
0.117
0.176
0.133
0.156
0.134
0.190
0.121
0.177
0.123
0.83
0072
0.122
0,074
0.11
CVAE
0,175
0.256
0.177
0.258
0.229
0.319
02335
0.321
0.174
0.245
0.171
0.250
0.008
0.178
0.109
0.180
CRAFT
0,109
0.365
0.111
0.167
0.111
0,166
0.116
0.171
0.124
0.179
0.127
0.180
0.066
0.111
0.067
0.108
Real
0,108
0.161
0.110
0.163
0.094
0.144
0.088
0.449
0.117
0.175
0.119
0.175
0.062
0.111
0.063
0.110
GMEL
0.174
0.345
0.182
0.248
0.231
0.258
0.348
0.112
0.261
0.319
0.262
0.113
0.102
0.153
0.130
0.198
DRG
0,322
0.460
(1326
0-422
0,152
0.224
0.158
0.230
0.551
0.455
0.351
0.544
0.081
0.140
0.081
0.142
KSTDif
0,371
0.502
0.376
0.505
0.416
0.540
0.494
0617
0.480
0.608
0.400
0606
0.34
0.373
0.235
0.235:0
0,371
CGAN
0.372
0.364
0.376
0.397
0.515
0,409
0.50
0.35
0.54
0.70
0,164
0.164-00
0,232
Ditlwgre
DIT
RRIST
0.234
0.252
0.341
0.287
0282
0.366
0.321
0.287
0.319
0.297
0.378
0.335
0297
0.286
0350
0.333
0311
0.36
0.54
0.57
0320
0.000
2112
1280
0230
0149
0,229
0280
0,210
0,182
0,260
0.271
DDPM
0.116
0.172
0111
0.127
0.122
0.179
0.122
111
0,128
0.183
111
0.10
0004
0.125
0.074
0.11
CVAE
0.152
0.225
0.159
0,233
0.301
0,405
0.300
0.402
0.170
0.240
0.33
0241
0129
0.201
0.131
0.205
CRAPT
0.112
0.170
0.115
0.171
0.111
0.162
0.115
0.171
0.122
0.188
0.134
0.069
0.118
0,068
0.112
Real
0.105
0.160
0.108
0.16]
0.095
0.145
0.009
0.129
0.121
D.1K3
0.122
0062
0.111
0,063
0.110
GMEL
0.184
0.245
0.187
0.256
0,213
0.260
0.216
0.271
0.277
0,331
0.263
0312
0.90
0.152
0.105
0.155
DRG
0,306
0.445
0311
0.448
0,168
0.349
0.172
0.252
0.352
04677
03556
0472
0.089
0.154
0.089
0.154
KSTDift
0,390
0.434
0.644
0.702
0.458
0.588
0.435
0.599
0.334
0.371
0337
0388
0.677
0.819
0.390
0.448
CGAN
0.275
0.374
0.393
0.387
0.293
0.286
0.355
0314
0,409
0313
0110
0.33
0.228
0,137
0,227
Differwe
0.256
0.204
0233
0.29
0.387
0.495
0.324
0-113
0.007
0204
0358
0.51
02445
0,143
0.355
DIT'
0.231
0.284
0242
0.286
0.276
0.319
0.277
0.321
0.302
0,344
0299
0260
0220
0.297
0.15
0.219
DDPM
0.116
0.172
0.118
0.172
0.123
0.151
0.122
01181
01132
0.185
0.111
0.16
0.000
0.118
0.669
0.112
CVAE
0.151
0.217
0.155
0.222
0.214
0.304
0.220
0,310
0.166
0.239
11.64
0.34
0133
0.215
0,130
0.208
CRAFT
0.110
0.168
0.112
0.170
0.110
0.166
0.116
0.170
0.129
0,157
0.190
Real
0.107
0.161
0110
0.162
0.095
0.144
0.008
0.148
0.125
0.156
0.127
0.186
0.62
0.109
0.061
0.108
GMEL
0.154
0.342
(122)
0,327
0,209
0.262
0.238
0.313
0.261
0.317
0.263
0.317
0.086
0.157
LAM1
0.138
DRO
14 -
0,315
0.454
0.19
0.456
0,155
0.237
0.157
0.220
0.368
0.487
0.372
0.490
0.866
0, 0.145
0.086
0.145
KSTDAI
0.534
0.585
0.46
0.505
0.507
0.410
0
0.188
0.204
CGAN
0.394
0.095
0.120
0.515
0.459
0.579
0,470
0.590
0.362
0.600
0.72
0.255
0.176
0220
Diffeawe
0.244
0.335
0.122
0:425
0.200
0.358
0.285
0,366
0.333
0.40
0.666
0.50
0.67
0251
04142
0210
DIF
0.226
0.280
0.224
0.276
0.305
0.345
0.365
0.542
0.304
0,544
0311
0352
0.95
0.24
0,206
(1240
DDPM
0.117
0.176
0.121
0.183
0.123
0.152
0.125
0.165
0.132
0.188
0.137
0.194
0.0%6
0.121
0.088
0.117
CNAE
0.209
0.295
0210
0.291
0.278
0.370
0.279
0.370
0.226
0.314
0.228
0.316
0.156
0.255
0.153
0.252
CRAFT
0.113
0.170
0.115
0.170
0.114
0.169
Real
0.112
0.169
0.115
0.172
0.098
0.146
0.101
0.152
0.121
0.170
0.122
0.179
0.061
0.107
0.061
0.107
GMEL
0.204
0.250
01205
0.283
0.198
0.256
0.211
0.258
0.225
0.268
0.226
0.279
0.09%
0.148
0A077
0.148
DRG
LSTMM LUSY
0.299
0.431
0.304
0.433
0,153
0.213
0.156
0.228
0.312
0:414
0.313
0.414
0.089
0.154
0.089
0.153
KSTTHI
0.348
0.480
0.335
0.462
0.443
0.534
0.505
0.350
0.305
OGAN
0.412
0.504
0426
0.525
0.395
0.4%
0.390
0.484
0.335
0.42
0.340
0.181
0.269
0.173
0.261
DiEEowe
0.238
0.306
0.277
0.295
0.354
0.358
0.446
0.73
0.307
0.354
0.331
0.07
0.166
0.280
0.147
0.220
DIT
0.255
0.296
0.336
0.205
0.289
0.336
03385
0.324
0.315
0.351
0.517
0357
0218
0.250
0.168
0.214
DIPM
0.118
0.175
0111
0,182
0.131
0.151
0.134
0.188
0.125
0.182
0.131
0.195
0.073
0.119
0,075
0.119
CVAE
0.228
0,312
0.28
0.311
0.316
0.416
0313
0:111
0.167
0.251
0.90
0.51
0.180
0.260
0.180
0.256
CRAFT
0.114
0.170
0.117
0.173
0.111
0.165
0.115
28

---

Table 8: Data utility comparison on traffic flow prediction (Transformer)
Chicago
Washington, D.C.
Torunto
New York City
Gen
Pred
Inflow
Outflow
Inflow
Dunflow
Inflow
Oatlow
Infoow
Outloww
MAE
RMSE
MAE
RMSE
MAE
RMSE
MAE
RMSE|
MAE
RMSE
MAB
RMSE
MAE
RMSB
MAB
RMSE
Real
0.099
0.158
0.10%
0.163
0.094
0.149
0.098
0.155
0.111
0.170
0.114
0.172
0.053
0.102
0.054
0.103
GMEL
5
0.165
0.223
0.165
0.227
0.201
0.250
0.199
0.253
0.220
0.274
0.223
0.284
0.080
0.133
(1.81
0.131
DRG
0.300
0.424
0.300
0.418
0.184
0.250
0.176
0.240
0.341
0.453
0.328
0.435
0.085
0.134
0.081
0.130
KSTDAN
0.367
0.500
0:377
0.510
0.469
0.504
0.476
0.609
0.571
0.723
0.530
0.670
0.337
0.378
0.756
0.807
CGAN
Trandooss
0.159
0.221
0.171
0.257
0.282
0.379
0.284
0.382
0.239
0.310
0.235
0.304
0.128
0.193
0,135
0.201
Dilfwove
0.220
0.292
02112
0.279
0.278
0.345
0.284
0.354
0.356
0.228
0,140
0.211
DIT
0.235
0.289
01236
0.257
0.209
0.312
0.282
0.320
0.296
0.539
0.225
0:358
0.112
DDPM
0.111
0.104
0.115
0.173
0.113
0.166
0.115
0.172
0.119
0.174
0.123
0.178
0.03
0.111
0,065
0.110
CVAE
0,162
0.228
0.164
0.229
0.250
0.311
0:237
0.318
0.172
0.222
0.176
0.226
0.114
0.181
0.116
0.185
CRAFT
0,097
0.348
0.100
0.152
0.197
0,149
0.305
0,161
0.124
0.174
0.134
0.373
0.060
0.143
0
0.102
Real
0,102
0.159
0.105
0.163
0.094
0.147
0.098
0.153
0.116
0.176
0.118
0.17
0.065
0.100
0
0.100
GMEL
0.185
0.251
0.186
0.258
0.249
0.311
0.240
0.296
0.252
0,503
0.248
0.208
0.083
0.158
0.083
0.132
DEG
0,362
0.491
0.350
0.686
0.166
0.222
0.166
0.222
0.391
0,511
0.390
0.509
0.069
0.116
0.070
0.118
KSTDif
0.349
0.514
0.364
0,519
0.383
0.511
0.376
0.468
0.356
0.401
0.352
0/13
0,216
0.355
0,307
0.359
CGAN
Trans former
0.179
0.349
0.187
0.340
0.254
0.345
0,140
0.149
0.194
Dillware
0.239
0.297
0.228
0.294
0.299
0.352
0229
0.26
0.389
0.339
0282
0.43
0.11
0,209
DIT
0.243
0.291
0.225
0.28
0.285
0.328
0.287
0.331
0.221
0.664
0211
0.554
0.77
0.234
0,188
-
0.344
DIPM
0.108
0.161
0.113
0.167
0.132
0.157
0.131
0.180
0.128
0.154
0.130
0067
0.117
0.118
CVAE
0,175
0.245
0.176
0.244
0.224
0,305
0.227
0.306
0.192
0.255
0.196
0.59
0.106
0.165
0.109
0.170
CRAFT
0,103
0.155
0.106
0.158
0.106
0.158
0.309
0,161
0.121
0.174
0.134
0.176
0.060
0,104
0.060
0.104
Real
0,105
0.162
0.108
0.165
0.093
0.143
0.066
0.147
0.115
0.175
0.117
0.176
0.069
0:105
0.059
0.104
GMEI
0.186
0.356
0.180
0.240
0.218
0.276
0.232
0.294
0.211
0.281
0.236
0.786
0.091
0.141
0.145
0.184
DRG
0.341
(1467
(1323
0.442
0,175
0.237
0.168
0.228
0.354
04467
03360
0.47
0.688
0.116
(1000)
0.117
KSTDif
0,387
0.526
0.397
0.538
0.420
0.516
0.492
0616
0.088
0.564
0.444
0900
0243
0.386
0,236
0,375
CGAN
Transt Sandooeeee
0.195
0.368
0.107
0.267
0.362
0.385
0.300
0.000
0124
0.175
0,133
0,187
Ditlwgre
0.240
0.297
0.35
0.304
0.222
0.520
0224
0319
0224
0,342
0290
9240
0144
0,09
02209-000
0,129
0.205
DIT
0.231
0.287
0.227
0.280
0.285
0.327
0.276
0.321
0.306
0,566
0297
0339
0106
0208
0,151
0
0208
DEPM
0.114
0.169
0111
0.123
0.123
0.175
0.121
0.176
0.130
0.161
11118
0.10
0070
0.116
0.071
0.115
CVAE
0,153
0.214
0.158
0,219
0.255
0,332
0.261
0.356
0.170
0.230
0.374
02311
0113
0.169
0.117
0.169
CRAPT
0,106
0.160
0.108
0.162
0.104
0.150
0.108
0.158
0.122
0,178
0.128
0.183
0.063
0.109
0.064
0.108
Real
0,100
0.155
0.105
0.159
0.090
0.140
0.003
0.124
0.116
0.175
0.118
0.177
0.057
0.105
0,057
0.105
GMEL
0.192
0.256
0.193
0.26]
0.222
0.250
0.232
0.304
0.249
0.505
0.350
0.306
0.94
0.150
0.098
0.156
DRG
0,310
0.425
0.303
0.433
0,150
0.200
0.151
0.208
0.349
0.400
0.354
0465
0.71
0.122
0,072
0.133
KSTDift
0,390
0.436
0.621
0.699
0.509
0.554
0.507
0640
0.328
0.360
0.329
0.86
0.221
0.771
0,31
0.420
Differwe
CGAN
Trantorus
0,270
0.221
0.281
0.369
0222
0.382
0.284
0.390
0.270
0,283
0.375
0.342
0.786
0.662
0.121
0.009
0.349
0,500
0.61
0204
0343
0336
0.40
0.00
0.175
0200
0,109
0202
0.73
DIT'
0.222
0.280
0.22
0.276
0.265
0.318
0.268
0.315
0.306
0,349
0.000
0247
0173
0220
0,199
0208
DDPM
0,109
0.161
0.113
0.166
0.112
0.163
0.113
0.166
0.129
0.161
1.1
0.90
0067
01111
0.066
0.109
CVAEE
0,153
0.214
0.158
0.220
0.202
0,272
0.212
0.283
0.170
0.231
1.313
0.31
0.106
0.17
1.105
0.108
CRAFT
0.107
0.162
0.109
0.163
0.103
0.152
0.107
0.156
0.120
0,173
0,123
0.063
0.104
0,063
0.104
Real
0.101
0.155
0.104
0.150
0.091
0.140
0.003
0.144
0.118
0.177
0.120
0.800
0.055
0.104
0.856
0.104
GMEL
0.211
0
(1209
0.285
0,242
0.301
0.234
0.284
0.264
0.317
01263
0.312
0.088
0.140
0,000
0.142
DRG
0.323
0.450
0.307
0.427
0,183
0.248
0.165
0.247
0.300
0.531
0.301
0,510
0.069
0. 120
0.059
0.119
KSTDAI
0.524
0.586
0.45
0.510
0.488
0,166
Diffeawe
CGAN
Transton frnsstrne
0,342
0:221
0.431
0.280
0.15
0.392
0.273
0.080
0.371
0.291
0.463
0.334
03352
0.296
0.489
0.353
0.200
0.557
0.380
0.388
0288
0350
0,26
0.39
0.33
0.186
0.205
0.166
0,125
0,184
0202
DIF
0.223
0.279
0.221
0.276
0.290
0.331
0.285
0.322
0.297
0,538
0.301
0242
01447
0.205
0143
(1203
DDPM
0.113
0.167
0.117
0,173
0.122
0.173
0.124
0.176
0.127
0.161
0.131
0.185
0071
0.113
0.069
0.110
CNAE
0,192
0.260
0.195
0.26
0.277
0.359
0.283
0.363
0.234
0.297
0240
0.305
0.124
0.198
0.124
0.1%
CRAFT
0.107
0.160
0.110
0.163
0.106
0.154
0,107
0.105
Real
0.104
0.160
0.108
0.164
0.090
0.140
0.001
0.144
0.115
0.171
0.118
0.176
0.054
0.102
0.055
0.103
GMEL
0.216
0.258
0227
0.287
0,219
0.251
0.218
0.277
0.238
0.501
0.341
0.297
0.093
0.148
0,055
0.150
DRG
0,315
0.427
0.113
0.424
0,162
0.217
0.167
0.226
0.377
0.691
0.374
0.492
0.021
0.122
0,072
0.123
KSTD
0.344
0.473
0.340
0.467
0.444
0.576
0.510
0.655
0.350
0.308
DilEwwe
OGAN
Transd COnTEE
0.385
0.228
0.46)
0.288
0.385
0.211
0.47
0.283
0.324
0.28)
0.418
0.308
0.323
0.267
0.412
0.321
0.119
0,290
0.539
0.585
0.289
0.346
0.341
0.413
0.142
0.140
0.206
0.200
0.136
0.145
0202
0210
DIT
0.251
0.288
0.227
0.262
0.283
0.335
0.767
0.316
0.305
0.343
0.506
0.447
0 199
0.251
0.154
0.206
DIPM
0.115
0.109
0.119
0.174
0.129
0.178
0.122
0.182
0.127
0.17%
0.136
0.187
0.01
0.112
0.099
0.110
CVAE
0.195
0.257
0.195
0.259
0.285
0.369
0.287
0.370
0.196
0.247
0200
0.53
0.138
0.189
0.138
0.186
CRAFT
0.106
0.158
0.108
0.162
0.102
0.149
0,867
29

---

(I) CHI Real
(2) CHI GMEL
(3) CHI DFG
(4) CHI KSTDiff
(5) CHI CGAN
THE DO
(6) CHI Diffwave
(7) CHI- DIT
(8) CHI DDPM
(9) CVAE
(10) CHI CRAFT
(11) DC Real
(12) DC GMEL
(13) DC-
(14) DC KSTDiff
(15) DC CGAN
(16) -C Diffwave
(17) DC DIT
(18) DC DDPM
(19) DC CVAE
(20) DC CRAFT
(21) TRT Real
(22) TRT GMEL
(23)
(24) TRT KSTDiff
(25) TRT CGAN
(26) TRT Diffwave
(27) TRT DiT
(28) TRT DDDM
(29) TRT CVAE
(30) TRT CRAFT
(31) NYC- Real
(32) NYC GMEL
(33) DFG
(34) NYC- KSTDiff (35) NYC CGAN
(36) NYC- Diffwave
(37) NYC DiT
(38) NYC- DDPM
(39) NYC CVAE
(40) NYC CRAFT
Figure 11: Heatmap of Traffic Flow in Different Cities (CHI stands for Chicago, DC stands for Washington, D.C., TRT stands for Toronto and NYC stands for New York City)
30

---

01
110
12
(a) CHI: TEA+CCA
(b) CHIl: only CCA
(c) CHI: TEA+CCA
(d) CHI: only TFA
'""""
12
T1
C
M
(e) DC: TEA+CCA
(f) DC: only CCA
(g) DC: TFA+CCA
DC: only TFA
Tap Dayee
(i) TRT: TEA+CCA
(i) TRT: only CCA
(k) TRT: TEA+CCA
(I) TRT. only TFA
64-
13
TI
(m) NYC: TEA+CCA
(n) NYC: only CCA
(0) NYC: TEA+CCA
(p) NYC: only TFA
Figure 12: Visualization analysis for TFA and CCA (CHI stands for Chicago, DC stands for Wash-
ington, D.C., TRT stands for Toronto and NYC stands for New York City)
31


Reviews:
[{"review_id": "dG030OH7o3", "reviewer": "Reviewer_oBWv", "summary": "This 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.", "questions": "See Weaknesses.", "limitations": "Yes", "rating": 6, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 4, "significance": 4, "originality": 3, "strengths_and_weaknesses": "Strengths:\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.", "was_revised": true, "final_justification": "My major concerns on data dependency and experimental settings have been well answered. And after reading other reviewers' comments and the authors rebuttals, I believe this paper is valuable for the trafffic flow generation community.", "rebuttal": "Thanks so much for your detailed and constructive comments. Please see our below responses to your concerns.\n\n# W1: Data dependency\n\nThe data required for our model includes static geographical feature data of both the source and target cities, as well as traffic flow data of the source city.\nSpecifically, the following static geographical features must be provided to use our model, with their acquisition and processing methods as follows:\n+ **POI features**: POIs of the source and target cities can be downloaded from OpenStreetMap and matched to the city's spatial units (in our experiments, grid regions are treated as spatial units). We then count the number of various types of POIs in each region and use the TF-IDF algorithm to calculate the score of each POI type in the region. Specifically, we treat POI categories as words, all POIs within a region as a document, and all POIs in the entire city as a corpus to compute TF-IDF scores. Finally, these scores are concatenated into a feature vector.\n+ **Road network features**: All road segments in the source and target cities can be downloaded from OpenStreetMap and matched to the city regions. We then calculate the total mileage of each type of road in each region and concatenate these values into a feature vector.\n+ **Population features**: Population statistics for the source and target cities are downloaded from WorldPop. The population count and population density in each region are calculated and included as part of the basic features of the region.\nThese static geographical feature data all rely on public data sources, allowing researchers to easily obtain them for any city.\n\nIn addition, training our model requires traffic flow data covering all regions of the source city (including inflow and outflow for each region at each time slice), with a recommended time span of over 3 months. Traffic flow data can typically be derived from trajectory data, OD data statistics, or specialized traffic flow datasets. Our model is not restricted to specific traffic modes—it can handle bicycle, car, or pedestrian flow—but the generated flow for the target city should match the type used for the source city.\n\n**Extended static geographic features**: \nThe road network features used in the model do not include the topological structure of the road network. To verify whether topological structure information is helpful for traffic flow modeling, we later tried adding space syntax features (including degree, closeness and betweenness) that describe the road network topology to the geographic features and conducted the following experiments.\n\n|Target City|Method||Inflow|||Outflow||\n|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|\n|||CPC|NMAE|NRMSE|CPC|NMAE|NRMSE|\n|CHI|CRAFT|0.785|0.140|0.216|0.786|0.140|0.216|\n||CRAFT+syntax|0.814|0.121|0.187|0.815|0.123|0.189|\n|DC|CRAFT|0.815|0.158|0.240|0.816|0.159|0.240|\n||CRAFT+syntax|0.800|0.165|0.246|0.799|0.168|0.247|\n|Toronto|CRAFT|0.804|0.178|0.267|0.804|0.179|0.268|\n||CRAFT+syntax|0.827|0.159|0.239|0.827|0.159|0.240|\n|NYC|CRAFT|0.782|0.103|0.170|0.786|0.102|0.165|\n||CRAFT+syntax|0.779|0.124|0.194|0.775|0.113|0.188|\n\nWe found that on the Chicago and Toronto datasets, adding road network topological features helps improve the generation effect of the target city. However, this phenomenon does not occur on the DC dataset with a simple road network structure and the NYC dataset with a complex road network structure. When using our model, researchers can also try to expand the space syntax features of the road network.\n\n# W2: Writing\n\n**Adjacency Construction:** Each region is modeled as a graph node. If two region rectangles share a common edge (i.e., are spatially adjacent), a bidirectional edge is established between them. The adjacency relationships among regions are represented as an adjacency matrix, which is encoded by a graph transformer and incorporated into the regional geographical representation. We will clarify this definition in the Preliminary section of the final version.\n\n**Geographical features of regions:** In the Preliminary section, we only mentioned that each graph node has three types of geographical features but did not specify the details of each type. We provide formal definitions of the three feature types in the appendix and we will add explanations of these geographical features in the Preliminary section in the revised version.\n\n# W3: Experimental settings\n\nThank you for your suggestions, which are very helpful for us to improve the details and readability of the paper. Due to the length constraints of the paper, we used abbreviations for the names of cities (datasets) in some experimental figures and tables, but failed to explain them clearly. \n\nIn Figure 7 and Appendix Figures 11-12, DC, NY (NYC), and TRT stand for Washington, New York, and Toronto respectively. To ensure rigor, we will follow your suggestion to standardize the abbreviations in the experimental setup section to avoid ambiguity."}, {"review_id": "N6Is2sWy1J", "reviewer": "Reviewer_6r4v", "summary": "This 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.", "questions": "1. 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.", "limitations": "yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "The 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.", "was_revised": true, "final_justification": "The author's rebuttal has addressed most of my concerns. Thus, I will keep my positive scores.", "rebuttal": "Thanks so much for your detailed and constructive comments. Please see our below responses to your concerns.\n\n# W1 & Q1:\n\n**1) Literature review of cross-city flow generation**\n\nFlow prediction and generation are different tasks. Flow prediction leverages historical flow data to forecast future flows, focusing on modeling temporal patterns. In contrast, flow generation uses geographic features as input, mapping static spatial structure to dynamic flows. The main challenge of cross-city prediction lies in flow pattern shifts in different cities. While Cross-city generation must simultaneously handle shifts in both static geographic features and dynamic flow patterns. \n\nSome pioneer works [1][2] have explored cross-city flow prediction, the area remains underdeveloped, with existing efforts limited to specific types of data and tasks. [1] is an early effort to cross-city generation in origin-destination (OD) data, building a large-scale OD dataset for pretraining a universal OD flow model. However, OD data is a specific flow type with fixed patterns and strong periodicity, making [1] limited to OD flow and hard to generalized to tasks with broader flow types. [2]specialized for cellular signal data, as cellular stations inherently have unique priors and clear semantic links to surrounding geographic entities. Based on this character, [2] constructs a universal knowledge graph to guide cross-city transfer. Yet, this strategy is difficult to extend to general flow scenarios, where such structured priors are often unavailable.\n\nExisting efforts focus on specific types of data and tasks, our work aims to explore more general scenarios which remain under explored . Our work may serve as an initial prob to such tasks.\n\n[1]A Large-scale Benchmark Dataset for Commuting Origin-destination Matrix Generation\n\n[2]Deep Transfer Learning for City-scale Cellular Traffic Generation through Urban Knowledge Graph\n\n**2) Transfer learning in cross-city flow tasks**\n\nTransfer learning in cross-city tasks mostly focused on few-shot flow prediction but rarely applied to zero-shot flow generation. Since most methods require certain amounts of target city data during training. Specifically, they can be categorized into below three paradigms\n\n- meta-learning: [3][4] use meta-learning to obtain good model initialization from source cities. However, it requires few-shot adaptation on target cities and struggle with zero-shot tasks. But we targets zero-shot generation without any fine-tuning on target cities.\n\n- adversarial training: [5][6] leverage a domain discriminator to enforce models only extract domain-shared features. But these methods usually suffer from limited generalization ability as training discriminators is hard and typically assumes distribution gaps between domains are not too large.\n\n- feature alignment: uses an alignment loss to project source and target data into a shared feature space. Additionally, these methods typically employ feature decoupling to simplify alignment. [7], [8] decouple features along spatial-temporal dimensions, geographic structures, respectively. However, such methods are sensitive to hyper-parameters if alignment losses are too complex.\n\nIn conclusion, transfer learning can address few-shot flow prediction in cross-city scenarios, they cannot fully solve the zero-shot flow generation, as original meta-learning and adversarial training still require access to flow data of target cities during training. But transfer learning can partially support feature transfer for flow generation. Inspired by the feature alignment paradigm, we designed the Geographic Feature Alignment(GFA) module to align the static geographic features of the source and target cities.\n\n[3]Cross-City Multi-Granular Adaptive Transfer Learning for Traffic Flow Prediction\n\n[4]Spatio-Temporal Meta-Graph Learning for Traffic Forecasting\n\n[5]Domain-Adversarial Training With Knowledge Transfer for Spatio-Temporal Prediction Across Cities\n\n[6]Generating Origin-Destination Flow for New Cities Via Domain Adversarial Training\n\n[7]Transferable Graph Structure Learning for Graph-based Traffic Forecasting Across Cities\n\n[8]Exploiting Hierarchical Correlations for Cross-City Cross-Mode Traffic Flow Prediction\n\n# W2 & Q1: Why most baselines are general generative models & comparisons with task-specific baselines\n\nAs discussed in above literature review, cross-city flow generation is an emerging area with few baselines, most of which focus on specific data types. Thus, we selected general generative models as our baselines in the original paper.\n\nInspired by your comments on transfer-learning, we incorporated transfer learning techniques to enhance the general baseline, adapting it more \"task-specific\". These improvements are applied to the alignment of static geographical features between source and target cities.\n\nSpecifically, we applied two feature alignment losses:TCA and MMD, and an adapted adversarial training strategy(ADV). Experimental results are as follows:\n\n|Target City|Method||Inflow|||Outflow||\n|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|\n|||CPC|NMAE|NRMSE|CPC|NMAE|NRMSE|\n|CHI|TCA|0.585|0.205|0.307|0.581|0.209|0.310| \n||MMD|0.638|0.194|0.280|0.626|0.200|0.289|\n||ADV|0.616|0.200|0.304|0.613|0.204|0.307|\n||CRAFT|**0.785**|**0.140**|**0.216**|**0.786**|**0.140**|**0.216**|\n|DC|TCA|0.659|0.229|0.315|0.658|0.231|0.317|\n||MMD|0.696|0.236|0.301|0.708|0.237|0.295|\n||ADV|0.713|0.237|0.287|0.703|0.249|0.302| \n||CRAFT|**0.815**|**0.158**|**0.240**|**0.816**|**0.159**|**0.240**|\n|Toronto|TCA|0.692|0.274|0.359|0.688|0.280|0.360|\n||MMD|0.631|0.261|0.356|0.640|0.257|0.349|\n||ADV|0.724|0.238|0.299|0.716|0.243|0.308|\n||CRAFT|**0.804**|**0.178**|**0.267**|**0.804**|**0.179**|**0.268**|\n|NYC|TCA|0.677|0.202|0.279|0.679|0.204|0.279|\n||MMD|0.628|0.218|0.276|0.604|0.256|0.318|\n||ADV|0.617|0.224|0.284|0.594|0.268|0.329|\n||CRAFT|**0.782**|**0.103**|**0.170**|**0.786**|**0.102**|**0.165**|\n\nCompared to the original results in our paper, incorporating transfer learning indeed improves the baselines’ cross-city transfer performance, allowing them to be more task-specific. However, CRAFT still consistently retains SOTA performance due to the proposed RCA module, which provides selected priors from source cities.\n\n# Q2: Time complexity and efficiency\n\n|Method|Infer time(s)|Infer speed(samples/sec)|Training time(s)|\n|:-:|:-:|:-:|:-:|\n|GMEL|23.84|1701.63|5639|\n|CVAE|14.20|2883.65|2307|\n|CGAN|10.75|3775.23|12144| \n|DFG|46.54|871.63|36077|\n|KSTDiff|18003.47|2.13|73006|\n|Diffwave|420.83|96.40|3778|\n|DiT|1923.11|21.10|12430|\n|DDPM|1925.93|21.06|13024|\n|CRAFT-DDPM|1926.43|21.06|13492|\n|CRAFT-DDIM|21.08|1924.65|13492|\n\nIn above table, we report both inference and training times for CRAFT and the baselines. CRAFT includes two variants: CRAFT-DDPM and CRAFT-DDIM. As expected, diffusion-based models are generally slower in both training and inference than single-shot generative models. CRAFT-DDPM achieves comparable efficiency to other diffusion-based models such as DiT but remains slower than single-shot models like GMEL. CRAFT-DDIM improves the sampling strategy of the diffusion backbone in the inference stage, achieving efficiency comparable to, or even surpassing, some single-shot models (e.g., DFG).\n\n|Target City|Method||Inflow|||Outflow||\n|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|\n|||CPC|NMAE|NRMSE|CPC|NMAE|NRMSE|\n|CHI|CRAFT-DDPM|0.785|0.140|0.216|0.786|0.140|0.216|\n||CRAFT-DDIM|0.778|0.146|0.225|0.761|0.151|0.236|\n|DC|CRAFT-DDPM|0.815|0.158|0.240|0.816|0.159|0.240|\n||CRAFT-DDIM|0.791|0.172|0.253|0.788|0.176|0.257|\n|Toronto|CRAFT-DDPM|0.804|0.178|0.267|0.804|0.179|0.268|\n||CRAFT-DDIM|0.803|0.181|0.293|0.801|0.182|0.295|\n|NYC|CRAFT-DDPM|0.782|0.103|0.170|0.786|0.102|0.165|\n||CRAFT-DDIM|0.778|0.196|0.186|0.734|0.105|0.169|\n\nIn above table, we compare the performance of CRAFT-DDPM and CRAFT-DDIM. CRAFT-DDIM achieves performance close to CRAFT-DDPM and outperforms all other baselines. Therefore, for optimal performance, CRAFT-DDPM is preferred, while CRAFT-DDIM offers a better trade-off between performance and efficiency.\n\n# Q3: Can our model expand to traffic4cast?\n\nDue to limited time and computational resources, we selected Moscow and Berlin from the Traffic4cast[9] dataset to evaluate CRAFT.\nCompared to our initial datasets in the paper, Traffic4cast poses two main challenges: \n\n- finer-grained and more numerous regions result in a larger data scale that makes training more difficult\n\n- greater inter-city variation and lower similarity between regions across cities increase the risk of introducing irrelevant noise\n\nTo address these issues, we made two adaptations: \n\n- we aggregated regions at the input stage to reduce region count, and used a linear layer at the output to map predictions back to the original resolution\n\n- we halved the retrieval numbers of RCA to reduce the possibility of invovling irrelevant noise. Results are in below table\n\n|Target City|Method||Inflow|||Outflow||\n|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|\n|||CPC|NMAE|NRMSE|CPC|NMAE|NRMSE|\n|Berlin|GMEL|0.500|0.442|0.501|0.487|0.470|0.528|\n||DiT|0.436|0.353|0.444|0.453|0.494|0.571|\n||Diffwave|0.424|0.562|0.662|0.386|0.710|0.768|\n||DDPM|0.388|0.333|0.395|0.389|0.342|0.414|\n||CRAFT|**0.537**|**0.312**|**0.381**|**0.537**|**0.313**|**0.381**|\n|Moscow|GMEL|0.182|0.357|0.431|0.254|0.345|0.418|\n||DiT|0.566|0.403|0.480|0.500|0.320|0.396|\n||Diffwave|0.170|0.397|0.469|0.486|0.530|0.594|\n||DDPM|0.338|0.334|0.409|0.334|0.334|0.409|\n||CRAFT|**0.609**|**0.239**|**0.315**|**0.607**|**0.240**|**0.316**|\n\nThe experimental results demonstrate that CRAFT not only generalizes to the traffic4cast dataset but also shows bigger advantages over baselines than our original datasets, highlighting CRAFT’s strong generalization capability. We sincerely thanks for your suggestion and will include these results in the revised version. And we will also considering increasing the number of source cities in our future work.\n\n[9] NeurIPS traffic4cast 2021"}, {"review_id": "O1g44zrm91", "reviewer": "Reviewer_vAPG", "summary": "This 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.", "questions": "- 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?", "limitations": "While 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.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "### 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", "was_revised": true, "final_justification": "The authors provide sufficient evidence to allow me to adjust my score.", "rebuttal": "Thanks so much for your detailed and constructive comments. Please see our below responses to your concerns one by one.\n\n# W1: The novelty and contributions of our model \n\nCross-city flow generation is an emerging area with limited existing efforts. This is because flow generation itself is a challenging problem—it requires geographic features as input to map static spatial structures to dynamic flows. Moreover, the cross-city setting further requires the model to handle domain shifts in both static geographic features and dynamic flow patterns across different cities, even in a completely zero-shot manner.\n\nTherefore, this area remains in its early stages and is still under explored. Existing works primarily focus on specific types of data, such as origin-destination (OD) data[1] or cellular signal data[2], which often come with type-specific priors that simplify the task.\n\nIn contrast, we targets generation on general flow types which is under-explored, and address two critical challenges: “severe domain shift” and “insufficient conditions”.\n\n+ To address domain shift, we were among the early efforts to introduce optimal transport theory into flow tasks and proposed the GFA module. Compared with prior alignment approaches, GFA adaptively selects the most relevant source regions for each target region, enabling finer-grained alignment.\n\n+ To address insufficient conditions, we adapted RAG for flow generation and developed the RCA module, which integrates dynamic flow information from source cities under zero-shot settings. Most prior works relied solely on static geographic features. RCA allows our model to adapt to more complex flow patterns, resulting in more accurate flow generation.\n\n[1]A Large-scale Benchmark Dataset for Commuting Origin-destination Matrix Generation\n\n[2]Deep Transfer Learning for City-scale Cellular Traffic Generation through Urban Knowledge Graph\n\n# W2 & Limitation 2: Detailed analysis of the retrieval augmented module (RCA)\n\nFor a given target region and timestamp, RCA retrieves the most similar historical flow segments from source cities based on two features: geographic similarity and temporal proximity.\n\nWe conducted ablations on each feature to assess their individual contributions. The results are shown in the table below. Specifically, we averaged the flow segments retrieved by RCA and measured their similarity to the ground-truth flow in the target city using Pearson, Spearman, and Kendall metrics.\n\n|Target City|Method||Inflow|||Outflow||\n|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|\n|||pearson|spearman|kendall|pearson|spearman|kendall|\n|CHI|RCA w/o time|0.330|0.314|0.215|0.330|0.316|0.217|\n||RCA w/o geo|0.606|0.692|0.507|0.599|0.680|0.495|\n||RCA|0.830|0.840|0.652|0.826|0.847|0.660|\n|DC|RCA w/o time|0.280|0.270|0.188|0.268|0.249|0.172|\n||RCA w/o geo|0.573|0.624|0.451|0.575|0.629|0.453|\n||RCA|0.792|0.832|0.644|0.767|0.813|0.622|\n|Toronto|RCA w/o time|0.205|0.207|0.143|0.210|0.214|0.147|\n||RCA w/o geo|0.707|0.737|0.549|0.700|0.726|0.541|\n||RCA|0.770|0.787|0.603|0.763|0.788|0.606|\n|NYC|RCA w/o time|0.484|0.476|0.329|0.480|0.458|0.318|\n||RCA w/o geo|0.467|0.571|0.403|0.467|0.579|0.409|\n||RCA|0.865|0.845|0.657|0.867|0.848|0.660|\n\nBoth geo and temporal features contribute significantly to performance improvements, with temporal proximity often playing a more dominant role. This indicates that flow patterns across regions are more strongly influenced by temporal periodicity, while also being shaped by static geographic features.\n\n# W3 & Limitation 2:\n\n**1) Can CRAFT utilize non-grid data?**\n\nYes, actually datasets used in our paper is in graph format, where each node is a grid. CRAFT is not limited to grid-based data. It only requires that each basic geographic unit—regardless of its shape—provides complete information of POIs, roads, and population. In practice, such units are usually implemented as grids.\n\n**2) Can CRAFT generalize to more diverse cities?**\n\nWe have added experiments on the Traffic4Cast[3] dataset, which contains vehicle flow spans diverse countries across Europe and Asia and exhibits greater inter-city variation. In addition, Traffic4Cast offers larger-scale and finer-grained data than the datasets used in our main paper. Specifically, we selected Moscow and Berlin to evaluate CRAFT. The results are shown in the table below.\n\n|Target City|Method||Inflow|||Outflow||\n|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|\n|||CPC|NMAE|NRMSE|CPC|NMAE|NRMSE|\n|Berlin|GMEL|0.500|0.442|0.501|0.487|0.470|0.528|\n||DiT|0.436|0.353|0.444|0.453|0.494|0.571|\n||Diffwave|0.424|0.562|0.662|0.386|0.710|0.768|\n||DDPM|0.388|0.333|0.395|0.389|0.342|0.414|\n||CRAFT|**0.537**|**0.312**|**0.381**|**0.537**|**0.313**|**0.381**|\n|Moscow|GMEL|0.182|0.357|0.431|0.254|0.345|0.418|\n||DiT|0.566|0.403|0.480|0.500|0.320|0.396|\n||Diffwave|0.170|0.397|0.469|0.486|0.530|0.594|\n||DDPM|0.338|0.334|0.409|0.334|0.334|0.409|\n||CRAFT|**0.609**|**0.239**|**0.315**|**0.607**|**0.240**|**0.316**|\n\nThe experimental results show that CRAFT not only generalizes well to the traffic4cast dataset but also achieves larger improvements over baselines compared to our original datasets, highlighting its strong generalization capability. We sincerely thank you for the suggestion and will include these results in the revised version.\n\n[3] NeurIPS traffic4cast 2021\n\n# Q1 & Limitation 1: Enriching geographical features via network topology and intersection connectivity\n\nThank you for your comment. We computed each node’s space syntax features[4], including degree, closeness centrality, and betweenness centrality, to capture explicit road topology and intersection connectivity. Results are shown in the table below, where CRAFT + syntax denotes CRAFT augmented with explicit topology and intersection connections.\n\n|Target City|Method||Inflow|||Outflow||\n|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|\n|||CPC|NMAE|NRMSE|CPC|NMAE|NRMSE|\n|CHI|CRAFT|0.785|0.140|0.216|0.786|0.140|0.216|\n||CRAFT+syntax|0.814|0.121|0.187|0.815|0.123|0.189|\n|DC|CRAFT|0.815|0.158|0.240|0.816|0.159|0.240|\n||CRAFT+syntax|0.800|0.165|0.246|0.799|0.168|0.247|\n|Toronto|CRAFT|0.804|0.178|0.267|0.804|0.179|0.268|\n||CRAFT+syntax|0.827|0.159|0.239|0.827|0.159|0.240|\n|NYC|CRAFT|0.782|0.103|0.170|0.786|0.102|0.165|\n||CRAFT+syntax|0.779|0.124|0.194|0.775|0.113|0.188|\n\nExperimental results show that space syntax features provide certain gains on the CHI and Toronto datasets, but lead to slight performance drops on DC and NYC. This is because DC and NYC have the simplest and most complex road network structures, respectively, among the four datasets.\n\nSpecifically, flow magnitude is primarily determined by population size, while flow patterns are shaped by regional functionality. When cities have similar road network complexity, incorporating syntax features improves performance. However, when complexity differs, such features may impede cross-city spatial alignment. In our original paper, to ensure generalizability, we used only the most basic spatiotemporal features shared across all cities.\n\n[4]Space Syntax. Environment and Planning B: Planning and design\n\n# Q2 & W3 & Limitation1: Can CRAFT handle sudden changes in flow patterns, such as a holiday surge?\n\nAs existing public flow datasets lack information on incidents or extreme events. We constructed a dataset with sudden flow pattern changes based on the four datasets used in our paper.\n\nSpecifically, we kept all weekday (Monday to Friday) data in the training set, while using 80% of weekend and holiday data as the test set and adding the remaining 20% back into training. Additionally, we included a 0-1 one-hot vector as an event code to distinguish weekdays from holidays. We evaluated the impact of this event indicator on the RCA module’s performance, which is evaluated by similarity to the ground-truth flow in the target city using CPC, NMAE, NRMSE metrics. Results are in the table below:\n\n|Target City|Method||Inflow|||Outflow||\n|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|\n|||CPC|NMAE|NRMSE|CPC|NMAE|NRMSE|\n|CHI|RCA w/o event indicator|0.768|0.146|0.218|0.763|0.150|0.221|\n||RCA|0.800|0.123|0.190|0.802|0.123|0.189|\n|DC|RCA w/o event indicator|0.799|0.164|0.235|0.798|0.166|0.239|\n||RCA|0.810|0.150|0.218|0.805|0.155|0.224|\n|Toronto|RCA w/o event indicator|0.779|0.198|0.295|0.773|0.205|0.299|\n||RCA|0.783|0.188|0.279|0.788|0.185|0.275|\n|NYC|RCA w/o event indicator|0.769|0.103|0.167|0.764|0.107|0.171|\n||RCA|0.789|0.092|0.149|0.790|0.093|0.149|\n\nThank you for your detailed advice. The event indicator indeed improves the model’s ability to handle abrupt changes in flow patterns, such as holiday surges, thus enhancing CRAFT’s capacity for such scenarios. We will further extend this result and include it as a case study in the revised version.\n\n# Q3: The computational and memory costs when scaling to larger datasets\n\nTo evaluate computational cost, we tested the model’s inference time on progressively larger portions of the data—20%, 40%, 60%, 80%, and 100%—as shown in Table below.\n\n|Data portation|Train time per epoch|Valid time per epoch|Avg memory (GB)|Peak memory (GB)|\n|:-:|:-:|:-:|:-:|:-:|\n|0.20|7.362|1.032|0.343|0.553|\n|0.40|15.383|2.223|0.345|0.553|\n|0.60|24.294|3.177|0.354|0.553|\n|0.80|31.069|4.263|0.347|0.553|\n|1.00|39.687|5.294|0.345|0.553|\n\nNext, with the data scale fixed, we gradually increased the sequence length and measured inference time. Results are reported in Table below.\n\n|Seq leangth|Model size (Byte)|Train time per epoch|Valid time per epcoh|Avg memory (GB)|\n|:-:|:-:|:-:|:-:|:-:|\n|24|51781096|40.999|5.685|0.343|\n|48|51830248|41.156|5.531|0.344|\n|72|51879400|43.729|6.053|0.344|\n|96|51928552|44.555|5.631|0.345|\n|120|51977704|46.712|6.429|0.345|\n|144|52026856|48.883|6.666|0.345|\n|168|52076008|51.896|6.351|0.346|\n\nCombining the above tables, we observe that our model's computational cost grows linearly with data scale and is only slightly affected by sequence length. Memory cost is also insensitive to both data scale and sequence length."}, {"review_id": "i5PP1VA5sL", "reviewer": "Reviewer_qp6Z", "summary": "This 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.", "questions": "1. 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?", "limitations": "Yes.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths: \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).", "was_revised": true, "final_justification": "During the rebuttal process, the authors have largely addressed the concerns I raised, including the following key points:  \n\n1. **Concerns about the inference efficiency of the diffusion model**: The authors evaluated different sequence lengths and data proportions using various diffusion model backbones, demonstrating strong inference performance.  \n2. **Curiosity about broader dataset domains**: In addition to the bike-related dataset, they incorporated two datasets based on Traffic4Cast, achieving state-of-the-art (SOTA) model performance.  \n3. **Impact of generation length on the model**: The authors clarified the specific meaning of \"length\" and explained how model performance varies with different input and generation lengths.  \n4. **Other issues**: The authors also elaborated on the key differences from DDPM and the motivation behind certain feature selections.  \n\nOverall, the authors provided detailed responses to my questions and outlined plans for future improvements. I believe their work is worthy of acceptance and look forward to seeing the enhancements in the final version.", "rebuttal": "Thanks so much for your detailed and constructive comments. Please see our below responses to your concerns one by one.\n\n# W1: \n\n**1) Computational costs** \n\nTo evaluate computational cost, we tested the model’s inference time on progressively larger portions of the data—20%, 40%, 60%, 80%, and 100%—as shown in Table below.\n\n|Data portation|Train time per epoch|Valid time per epoch|Avg memory (GB)|Peak memory (GB)|\n|:-:|:-:|:-:|:-:|:-:|\n|0.20|7.362|1.032|0.343|0.553|\n|0.40|15.383|2.223|0.345|0.553|\n|0.60|24.294|3.177|0.354|0.553|\n|0.80|31.069|4.263|0.347|0.553|\n|1.00|39.687|5.294|0.345|0.553|\n\nNext, with the data scale fixed, we gradually increased the sequence length and measured inference time. Results are reported in Table below.\n\n|Seq leangth|Model size (Byte)|Train time per epoch|Valid time per epcoh|Avg memory (GB)|\n|:-:|:-:|:-:|:-:|:-:|\n|24|51781096|40.999|5.685|0.343|\n|48|51830248|41.156|5.531|0.344|\n|72|51879400|43.729|6.053|0.344|\n|96|51928552|44.555|5.631|0.345|\n|120|51977704|46.712|6.429|0.345|\n|144|52026856|48.883|6.666|0.345|\n|168|52076008|51.896|6.351|0.346|\n\nCombining the above tables, we observe that our model's computational cost grows linearly with data scale and is only slightly affected by sequence length. Memory cost is also insensitive to both data scale and sequence length.\n\n**2) Time complexity and efficiency**\n\nIn below table, we report both inference and training times for CRAFT and the baselines. CRAFT includes two variants: CRAFT-DDPM and CRAFT-DDIM. As expected, diffusion-based models are generally slower in both training and inference than single-shot generative models. CRAFT-DDPM achieves comparable efficiency to other diffusion-based models such as DiT but remains slower than single-shot models like GMEL. CRAFT-DDIM improves the sampling strategy of the diffusion backbone, achieving efficiency comparable to, or even surpassing, some single-shot models (e.g., DFG).\n\n|Method|Infer time(s)|Infer speed(samples/sec)|Training time(s)|\n|:-:|:-:|:-:|:-:|\n|GMEL|23.84|1701.63|5639|\n|CVAE|14.20|2883.65|2307|\n|CGAN|10.75|3775.23|12144| \n|DFG|46.54|871.63|36077|\n|KSTDiff|18003.47|2.13|73006|\n|Diffwave|420.83|96.40|3778|\n|DiT|1923.11|21.10|12430|\n|DDPM|1925.93|21.06|13024|\n|CRAFT-DDPM|1926.43|21.06|13492|\n|CRAFT-DDIM|21.08|1924.65|13492|\n\nIn below table, we compare the performance of CRAFT-DDPM and CRAFT-DDIM. CRAFT-DDIM achieves performance close to CRAFT-DDPM and outperforms all other baselines. Therefore, for optimal performance, CRAFT-DDPM is preferred, while CRAFT-DDIM offers a better trade-off between performance and efficiency.\n\n|Target City|Method||Inflow|||Outflow||\n|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|\n|||CPC|NMAE|NRMSE|CPC|NMAE|NRMSE|\n|CHI|CRAFT-DDPM|0.785|0.140|0.216|0.786|0.140|0.216|\n||CRAFT-DDIM|0.778|0.146|0.225|0.761|0.151|0.236|\n|DC|CRAFT-DDPM|0.815|0.158|0.240|0.816|0.159|0.240|\n||CRAFT-DDIM|0.791|0.172|0.253|0.788|0.176|0.257|\n|Toronto|CRAFT-DDPM|0.804|0.178|0.267|0.804|0.179|0.268|\n||CRAFT-DDIM|0.803|0.181|0.293|0.801|0.182|0.295|\n|NYC|CRAFT-DDPM|0.782|0.103|0.170|0.786|0.102|0.165|\n||CRAFT-DDIM|0.778|0.196|0.186|0.734|0.105|0.169|\n\n# W2 & Q3: Can CRAFT generalize to more diverse domains?\n\nWe have added experiments on the Traffic4Cast[1] dataset, which contains vehicle flow spans diverse countries across Europe and Asia and exhibits greater inter-city variation. In addition, Traffic4Cast offers larger-scale and finer-grained data than the datasets used in our main paper. Specifically, we selected Moscow and Berlin to evaluate CRAFT. The results are shown in the table below.\n\n|Target City|Method||Inflow|||Outflow||\n|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|\n|||CPC|NMAE|NRMSE|CPC|NMAE|NRMSE|\n|Berlin|GMEL|0.500|0.442|0.501|0.487|0.470|0.528|\n||DiT|0.436|0.353|0.444|0.453|0.494|0.571|\n||Diffwave|0.424|0.562|0.662|0.386|0.710|0.768|\n||DDPM|0.388|0.333|0.395|0.389|0.342|0.414|\n||CRAFT|**0.537**|**0.312**|**0.381**|**0.537**|**0.313**|**0.381**|\n|Moscow|GMEL|0.182|0.357|0.431|0.254|0.345|0.418|\n||DiT|0.566|0.403|0.480|0.500|0.320|0.396|\n||Diffwave|0.170|0.397|0.469|0.486|0.530|0.594|\n||DDPM|0.338|0.334|0.409|0.334|0.334|0.409|\n||CRAFT|**0.609**|**0.239**|**0.315**|**0.607**|**0.240**|**0.316**|\n\nThe experimental results show that CRAFT not only generalizes well to the traffic4cast dataset but also achieves larger improvements over baselines compared to our original datasets, highlighting its strong generalization capability. We sincerely thank you for the suggestion and will include these results in the revised version.\n\n[1] NeurIPS traffic4cast 2021\n\n# Q1：\n\n**1) Can CRAFT model inter-region relationships?**\n\nCRAFT has considered inter-region relationships. Specifically, our spatial encoder, a graph transformer, models the inter-region relationships among neighboring regions when representing geographic features.\n\n**2) Why does CRAFT focus on basic features such as POIs, roads, and population?**\n\nFlow magnitude is primarily determined by population size. Flow patterns are shaped by regional functionality which is indicated by the types and amounts of POIs and roads. In our original paper, to ensure the cross-city generalizability, we selelct only the most basic spatiotemporal features shared across all cities. Since cities share only a few fundamental geographic features and most other features differ significantly, incorporating too many features harms geographic feature alignment. This is a key challenge in cross-city flow generation: to reduce significant domain shifts, the generation must depend on a limited feature set.\n\n# Q2: How does the length of T affect the DDPM’s performance?\n\n**1) DDPM’s performance fluctuates with varying T**\nAs shown in paper’s Figure 7, DDPM’s performance varies within a narrow ±0.1 range as T increases. On the NY dataset, performance slightly decreases with larger T, while on the D.C. dataset, a clear improvement is observed. Among all baselines, DDPM exhibits the most stable performance trend.\n\n**2) Why does this fluctuation occur?**\nHow the performance variation with increasing T depends on the distribution of the target data. As T represents both the input sequence length (T_input) and the output sequence length (T_output). When T = 24, it denotes both input length and output length are 24. Thus, increasing T simultaneously increases task difficulty (due to longer output length per sample) but provides richer information per input sample (due to longer input length).\n\nSpecifically, detailed analysis can be shown in the below table, where we select Chicago as the target city and evaluate using NMAE (lower is better). We test various combinations of input and output lengths. For example, 24→48 denotes an input length of 24 and an output length of 48.\n\n||NMAE||NMAE|\n|:-:|:-:|:-:|:-:|\n|24->48|0.150|48->48|0.146|\n|24->72|0.154|72->72|0.147|\n|24->96|0.156|96->96|0.148|\n|24->120|0.158|120->120|0.146|\n|24->144|0.159|144->144|0.148|\n\nWithin each row, with a fixed output length, model performance improves as input length increases; Within each column, with a fixed input length, performance degrades as output length increases.\n\nTask difficulty is closely tied to the variance of the target data: higher variance—caused by larger fluctuations—leads to more challenging generation, while lower variance indicates easier generation. We computed the target variance across the four datasets, as shown in the table below：\n\n|NY|Chicago|Toronto|DC|\n|:-:|:-:|:-:|:-:|\n|0.227|0.226|0.220|0.215|\n\nAs illustrated, the NY dataset has the highest variance, making long-sequence generation most difficult. In contrast, the D.C. dataset shows the lowest variance, making the task relatively easier.\n\nTherefore, when the target data exhibits high variance, the loss from a larger output length dominates, leading to performance degradation as T increases; conversely, when the variance is low, the gain from a longer input length prevails, resulting in improved performance with increasing T. This aligns with the results in Table 7 of the paper.\n\n**3) Why is CRAFT different from DDPM?**\n\nRegardless of the length of T, CRAFT’s RCA module consistently retrieves relevant historical data from source cities as reference, effectively smoothing fluctuations and enhancing input quality. As a result, CRAFT performs more stably and achieves superior results in most cases."}]

Author rebuttal:

Meta-review: This paper introduces an improved diffusion-based approach for generating cross-city traffic flows. The key idea is to leverage retrieval-augmented generation (RAG) to incorporate historical data from source cities under similar conditions into the conditioning process.

The reviewers raised concerns regarding generalizability, time complexity, robustness, and model design. After the discussion, most of these concerns were satisfactorily addressed. With all final scores being positive, this submission represents a clear acceptance.
Decision: Accept (poster)