Generative Spatio-Temporal Graph Network for
Long-Range Urban Mobility Prediction
Yangyan Zeng, Shen Yu, Gaoyi Deng ,Yi Yang, Wei Liang, Member, IEEE, Xu Zhang, Member, IEEE,
Xiaokang Zhou, Member, IEEE

Abstract—The rapid advancement of intelligent transportation systems and consumer electronics has created an urgent demand for accurate long-range urban mobility prediction to enhance service quality in applications such as in-vehicle navigation, electric vehicle route planning, and shared mobility dispatching. However, existing forecasting models face significant challenges as prediction horizons extend, primarily due to error accumulation in iterative prediction frameworks, which causes forecasts to rapidly diverge from reality, and limitations in capturing evolving spatio-temporal dependencies. In this study, we propose a novel Generative Spatio-Temporal Graph Network (G-STGN) designed to address these challenges in long-range prediction for consumer electronics applications. The proposed model incorporates a learnable multi-scale temporal decomposition mechanism that adaptively separates raw traffic flow into trend, periodic, and random components using onedimensional convolutional networks and temporal attention. Additionally, a dynamic graph spatio-temporal encoding approach is developed to capture evolving dependencies by constructing data-driven dynamic graphs and integrating graph convolutional networks with gated recurrent units. Most importantly, a generative prediction enhancement framework based on conditional generative adversarial networks is introduced to learn and generate macroscopic distributions of future spatio-temporal states, providing forward-looking guidance to proactively correct and constrain the iterative prediction process. Extensive evaluations on two public datasets demonstrate that G-STGN significantly outperforms state-of-the- art baseline models in long-range forecasting tasks, with experimental results showing a 13.7% improvement in MAE and a 10.6% improvement in RMSE on urban traffic data, while effectively suppressing error accumulation across extended
The corresponding author is Yi Yang and Xiaokang Zhou.
Yangyan Zeng is with the Basic Research Institute of New Generation Artificial Intelligence, Xiangjiang Laboratory, Changsha 410205, China (email: yangyanz0930@163.com).
Shen Yu is a member of the School of Computing at Hunan University of Technology and Business, Changsha 410205,China (email: 814183972@qq.c om).
Gaoyi Deng is a member of the School of Artificial Intelligence and Advanced Computing at Hunan University of Technology and Business, Changsha 410205, China (email: 2521993783@qq.com).
Yi Yang is with the School of Advanced Interdisciplinary Studies, Hunan University of Technology and Business, Changsha 410205, China (e-mail: 2698@hutb.edu.cn).
Wei Liang is with the School of Advanced Interdisciplinary Studies and School of Management Science and Engineering, Hunan University of Technology and Business, Changsha 410205, China, and also the Xiangjiang Laboratory, Changsha 410205, China (e-mail: weiliang@csu.edu.cn).
Xu Zhang is with the School of Computing Sciences, University of East Anglia, x.zhang27@uea.ac.uk.
Xiaokang Zhou is with the Faculty of Business Data Science, Kansai University, Osaka 565-8585, Japan, and also with the RIKEN Center for Advanced Intelligence Project, RIKEN, Tokyo 103-0027, Japan (e-mail: zhou@kansai-u.ac.jp). prediction horizons.
Index Terms—Urban mobility prediction, spatio-temporal graph networks, generative adversarial networks, long-range forecasting, intelligent transportation systems, consumer electronics.
I.		INTRODUCTION
I
n recent years, the rapid advancement of fifth-generation mobile communications (5G) [1], the Internet of Things (IoT) [2], and artificial intelligence (AI) technologies has significantly accelerated the integration of consumer electronics with intelligent transportation systems (ITS) [3], fundamentally reshaping urban mobility and travel experiences [4]. This convergence has heightened the importance of accurate long-range urban mobility prediction, which serves as a critical enabler for enhancing service quality in various consumer electronics applications, including invehicle navigation systems, electric vehicle (EV) route planning, and shared mobility dispatching platforms [5]. For instance, modern navigation applications are expected to forecast traffic congestion on specific road segments over the next hour to recommend optimal routes [6], while EV charging management systems require predictions of station availability to avoid queues and optimize charging schedules [7]. The realization of such proactive services depends on precise forecasting of traffic conditions over extended timeframes (e.g., 30 to 60 minutes or longer), a capability known as long-range urban mobility prediction. This capability is pivotal for improving user experience, optimizing resource allocation, and ensuring travel efficiency and safety, thereby directly impacting the service quality and user retention of consumer electronics devices [8]. However, achieving reliable long-range prediction remains a formidable challenge due to the inherent complexities of spatio-temporal data and the limitations of existing forecasting models.
Despite the success of deep learning models in short-term traffic forecasting, long-range urban mobility prediction experiences performance degradation as the prediction horizon extends, primarily due to three key limitations [9]. First, non- adaptive modeling of complex temporal dynamics fails to disentangle stable long-term trends, periodic patterns, and high-frequency noise from raw traffic signals [10]. Traditional decomposition methods struggle to capture non-strict cycles or trend shifts, resulting in deviations during long-term extrapolation [11][12][13]. This issue affects consumer electronics; for example, an electric vehicle (EV) charging management system may misestimate holiday charging demand, leading to user dissatisfaction [7]. Second, static representations of spatial dependencies overlook dynamic functional correlations between nodes in transportation networks [14]. Early graph neural networks (GNNs) rely on static graph structures based on geographical proximity and cannot adapt to time-varying interactions [15][16][17]. This limitation is evident when predicting congestion caused by distant events, negatively impacting in-vehicle navigation systems [18]. Third, error accumulation in iterative prediction frameworks, such as sequence-to-sequence (Seq2Seq) models, causes distribution drift [19][20]. Although attention-based models alleviate this problem, they do not fundamentally resolve it [21]. This undermines shared mobility dispatch platforms, resulting in inefficient vehicle allocation and prolonged user waiting times [22]. Therefore, innovative solutions that integrate adaptive temporal decomposition, dynamic spatial dependency modeling, and generative enhancement are urgently needed to ensure stability in long- horizon predictions.
To systematically address these challenges, this study proposes a novel Generative Spatio-Temporal Graph Network (G-STGN) model, which seamlessly integrates learnable temporal decomposition, dynamic graph spatio-temporal encoding, and generative prediction enhancement to enable accurate long-range urban mobility forecasting for consumer electronics applications. Following a comprehensive framework, the raw traffic flow data is first adaptively decomposed into trend, periodic, and random components using a learnable multi-scale temporal decomposition mechanism that employs 1D convolutional networks and temporal attention mechanisms to refine input signals. Subsequently, a dynamic graph spatio-temporal encoding mechanism captures the evolving dependencies of each component by constructing data-driven dynamic graphs in real time and integrating graph convolutional networks with gated recurrent units. Most critically, a generative prediction enhancement mechanism innovatively introduces a conditional generative adversarial network to learn and generate the macroscopic distributions of future spatio-temporal states, serving as a forward-looking guidance signal to proactively correct and constrain the iterative prediction process. Finally, an intelligent algorithm is developed to achieve efficient and reliable long-range forecasting in resource-constrained environments. Specifically, the major contributions of this study can be summarized as follows:
•		An integrated Generative Spatio-Temporal Graph Network framework is proposed, seamlessly incorporating learnable temporal decomposition, dynamic graph encoding, and generative adversarial enhancement techniques to achieve accurate long- range urban mobility prediction for consumer electronics applications through a holistic architecture.
•		A learnable multi-scale temporal decomposition mechanism is proposed, in which raw traffic flow is adaptively decomposed into trend, periodic, and random components using 1D convolutional networks and temporal attention. This approach provides purified inputs and enhances the stability of long-term extrapolation.
•		A dynamic graph spatio-temporal encoding mechanism is designed, wherein component-specific dynamic graphs are constructed separately for trend, periodic, and random components and integrated with gated recurrent units. This approach captures evolving spatial dependencies and enhances the modeling of complex urban traffic dynamics.
•		A generative prediction enhancement scheme is proposed that leverages a conditional generative adversarial network to learn and generate future macroscopic spatio-temporal distributions. This generated distribution offers forward-looking guidance to proactively correct the iterative prediction process, thereby	suppressing error accumulation and	ensuring	higher prediction accuracy over extended horizons.
II.		RELATED WORK
This section reviews and compares related work in three key areas relevant to this study: temporal decomposition methods, spatio-temporal graph neural networks, and generative models for traffic forecasting.
A.		Temporal Decomposition Methods for Traffic Forecasting
Temporal decomposition has proven effective in addressing the non-stationary characteristics of traffic data within forecasting models [23]. This approach directly tackles the primary challenge posed by complex temporal dynamics. Methods such as ST-DAGCN [9] and MFE-STL [24] decompose traffic flow into multiple temporal components to improve prediction accuracy. MUSE-Net [23] explicitly disentangles multiple periodicities through semantic regularization, while Δfree-LSTM [1] emphasizes noiseresistant forecasting by modeling error distributions [25]. However, these techniques often rely on non-adaptive decomposition strategies based on fixed hyperparameters or heuristic rules [26], which limits their flexibility in managing the highly non-stationary and non-strictly periodic nature of real-world traffic flows [27]. Fixed window sizes in moving average methods or predefined periodicity assumptions struggle to adapt to dynamic pattern changes caused by holidays, events, or seasonal variations [28]. Our LMTD mechanism addresses these limitations by employing a learnable decomposition approach using 1D convolutional networks and temporal attention, enabling adaptive separation of trend, periodic, and random components from raw traffic signals in a data-driven manner [29].
B.		Spatio-Temporal Graph Neural Networks for Traffic Prediction
Spatio-temporal graph neural networks have become a leading approach for traffic forecasting, effectively modeling the complex dependencies within transportation networks [30].

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This research primarily addresses the challenge of modeling spatial dependencies. Early graph-based models, such as LST- GCN [29] and STFGNN [13], successfully combined graph convolutional operations with recurrent neural networks to capture spatio-temporal patterns [31]. However, these methods typically rely on static graph structures based on geographical proximity, which, as noted in [32], cannot adapt to the dynamic functional correlations present in traffic networks [14]. To overcome this limitation, subsequent studies have explored dynamic graph learning [33]. For example, DDGCRN [34] and DGCRN [35] construct data-driven dynamic graphs using learned node embeddings, while DGASTN [36] utilizes attention mechanisms to dynamically adjust spatial relationships [37]. Despite these advancements, most existing dynamic graph approaches model spatial dependencies using mixed raw traffic signals, without accounting for the fact that different temporal components may exhibit distinct spatial evolution patterns [38]. Our proposed DGTEM mechanism addresses this gap by constructing component-specific dynamic graphs for trend, periodic, and random components separately, enabling a more precise capture of evolving spatial dependencies across different temporal scales [39].
C.		Generative Models for Long-Range Prediction
The third challenge—error accumulation in long-range prediction—has motivated the exploration of generative models for traffic forecasting [40]. Traditional autoregressive frameworks [41], such as those employed in PDG2Seq [22] and other Seq2Seq architectures [19], suffer from distribution drift as prediction errors accumulate over extended horizons [42]. Although attention-based models like PDFormer [21] and DD-TTF [43] mitigate this issue by capturing long-range dependencies, they remain fundamentally limited by their extrapolative nature [44]. Recent research has begun exploring generative approaches; for example, ITCRL-DGAN [32] employs GANs for out-of-distribution generalization. Concurrently, the emerging paradigm of generative pretraining for spatio-temporal graph networks [45][46][47] has demonstrated potential in learning transferable representations through self-supervised objectives. While these generative pre-training methods exhibit powerful representation learning capabilities, they primarily focus on obtaining better initial features via self-supervised objectives. In contrast, our GPE mechanism leverages the generative model in a fundamentally different way: it directly learns the macroscopic distribution of future states and uses this learned distribution as a proactive guidance signal to correct the autoregressive prediction trajectory, thereby directly addressing the core issue of error accumulation. Models such as GDENet [38] and FedGODE [39] approach the problem through continuous-time modeling but lack explicit generative correction mechanisms [48][49]. Our GPE mechanism addresses this gap by introducing a conditional GAN to learn and generate holistic future spatiotemporal distributions, providing forward-looking guidance to proactively constrain and correct iterative predictions, thereby fundamentally suppressing error accumulation in long-range forecasting [50].
III.		OVERVIEW OF G-STGN
In this section, we first introduce the problem formulation for long-range urban mobility prediction, followed by the basic notations and definitions. An overview of the proposed Generative Spatio-Temporal Graph Network (G-STGN) framework is then presented and discussed.
A.		Problem Definition
The long-range urban mobility prediction task addressed in this article aims to develop an accurate forecasting model for consumer electronics applications, such as intelligent navigation [46] and electric vehicle route planning [48]. The model is designed to reliably predict future traffic conditions over extended time horizons. Specifically, the urban transportation network is represented as a spatio-temporal graph system, which serves as the initial input to our learning

Fig. 1. Illustrative Application Scenarios of Long-Range
Urban Mobility Prediction for Consumer Electronics
As illustrated in Fig. 1, modern consumer electronics applications in intelligent transportation systems depend on accurate long-range predictions to deliver proactive services. Fig. 1(a) depicts a typical scenario in which in-vehicle navigation systems require traffic condition forecasts for optimal route planning, while Fig. 1(b) presents electric vehicle charging station recommendations based on predicted future availability. These applications fundamentally rely on the ability to accurately forecast urban mobility patterns over extended time horizons.
Based on the application requirements illustrated in Fig. 1, we formalize the long-range urban mobility prediction problem as	a	multivariate	spatio-temporal	sequence forecasting task. Given a transportation network consisting of N sensor nodes, historical traffic flow observations over the past P consecutive time steps can be represented as a historical traffic flow tensor	∈	N×F×P , where N denotes the number of nodes, F represents the feature dimension per node at each time step (e.g., traffic flow, average speed), and P denotes the length of the historical observation window. Our objective is to learn a nonlinear mapping function that, based on the historical observations	, can accurately predict the traffic flow tensor	∈	N×F×H for the next H time steps. The core challenge lies in maintaining prediction accuracy over extended horizons, where traditional autoregressive models suffer from error accumulation and
distribution drift [21]. Prior to model input, all raw traffic flow data undergo Z-score standardization to eliminate scale differences and ensure stable training.
B.		Overall Architecture

illustrated in Fig. 2. It establishes a coherent pipeline through the synergistic operation of three core mechanisms, systematically addressing the challenges of long-range forecasting.
The overall architecture of the proposed G-STGN is
INPUT
DGTEM
MLP
MLP
LMTD
Adjacency Matrix Computation
GPE

. Overall Architecture of G-STGN
intelligently balances historically encoded features with the generative guidance through attention mechanisms, followed by a multi-layer projection to produce the final prediction

Fig. 2
As illustrated in Fig. 2, the framework begins with the Learnable Multi-scale Temporal Decomposition (LMTD) mechanism, which adaptively decomposes the raw traffic flow input	into trend, periodic, and random components. This
design overcomes the limitations of fixed-parameter decomposition methods, such as ST-DAGCN [9], by employing learnable 1D-CNNs and temporal attention to generate purified, component-specific inputs.
Subsequently, the Dynamic Graph Spatio-Temporal Encoding Mechanism (DGTEM) processes each decomposed component through parallel encoding pathways. Each pathway constructs data-driven dynamic graphs in real time and integrates graph convolutions with gated recurrent units (GRUs) within a unified framework. This approach overcomes the static graph limitations of prior models [16] by capturing evolving spatial dependencies specific to each temporal component.
Concurrently, the Generative Prediction Enhancement (GPE) mechanism generates forward-looking guidance by utilizing a conditional GAN to learn the macroscopic distribution of future spatio-temporal states, producing a latent guidance signal	. This approach fundamentally differs from purely
autoregressive frameworks such as PDG2Seq [22] by providing a distribution-aware blueprint that constrains iterative predictions. A multi-level fusion and prediction stage
pred .
In summary, the G-STGN framework integrates temporal purification, dynamic spatio-temporal encoding, and generative guidance into a unified architecture specifically designed to enhance stability and reduce error accumulation in long-range urban mobility prediction for consumer electronics applications.
IV.		LONG-RANGE PREDICTION BASED ON
GENERATIVE SPATIO-TEMPORAL GRAPH NETWORK
In this section, we introduce and explain the detailed mechanisms used to implement the core functions of our proposed learning model. These include adaptive multi-scale temporal decomposition for traffic flow purification, dynamic graph construction and spatio-temporal encoding for capturing evolving dependencies, and generative adversarial enhancement to improve long-range prediction stability.
A. Adaptive Multi-scale Temporal Decomposition for Traffic Flow Purification
The stability of long-range forecasting largely depends on a model's ability to identify and extrapolate stable long-term patterns from complex raw signals. Traditional decomposition

methods, such as the moving average used in existing approaches, rely on fixed window sizes that struggle to accommodate the multi-scale, non-strictly periodic characteristics of traffic flow caused by factors like holidays and weather variations. In contrast to these fixed methods, our Learnable Multi-scale Temporal Decomposition (LMTD) mechanism employs learnable neural network architectures to adaptively decompose raw traffic flow into three components—trend ( ), periodic ( ), and random ( )—through a completely data-driven approach.
The decomposition process utilizes one-dimensional convolutional neural networks (1D-CNNs) as the core component instead of simple filters, as illustrated in Fig. 3. The key innovation lies in the convolutional kernels within the 1D-CNNs being learnable parameters, which enables the automatic discovery of optimal local patterns and temporal scales for decomposition tasks during training. This approach offers significant adaptive advantages over traditional methods that rely on predefined rules or fixed windows when processing nonlinear, nonstationary traffic flows.
Fig. 3. Adaptive Multi-scale Temporal Decomposition Mechanism
For trend component extraction, we employ a multi-layer stacked 1D-CNN network with a large receptive field. This design effectively smooths short-term fluctuations through multiple convolutional operations, capturing the macro-level direction of slow signal changes. The formal representation is defined by a hierarchical convolutional structure:
t(rle)nd =σ(WT(l) ∗	t(rle-n1d)+bT(l)),	l=1,2,…,LT	(1)
where	(0) =	, ∗ denotes the convolution operation,
trend	hist
W (l) and b (l) represent the learnable convolutional kernel parameters and biases at layer l , σ is the activation function, and L denotes the total number of convolutional layers specifically designed for trend extraction with progressively increasing receptive fields to capture long-term dependencies.
For periodic component extraction, we address the critical challenge that traffic flow periodicity is rarely strictly fixed— weekend peak patterns differ from those on weekdays, while public holidays exhibit entirely distinct patterns. We design a structure combining a 1D-CNN with a temporal attention mechanism, where the 1D-CNN captures local repetitive patterns across various timescales, and the temporal attention dynamically assigns weights to features from historical time
dk	
l(okca)l=ReLU∑WP(,ki)∗	hist+bP(k) 
i=1		(2)	
et,j=va	tanh(Wa[ht-1⊕	l(okca)l,j]+ba)	(3)	
exp(et, j)	(4)	
		
αt,j= T′	,		
∑ exp(et,i)		
i=1 T′		
period =∑αt,j ⋅l(okca)l,j	(5)	
steps. The complete formulation integrates both local pattern extraction and adaptive temporal weighting.
j=1
where	(k) represents features extracted at different temporal
local
scales k , h denotes the hidden state from the preceding
time step, W (k) and b(k) are convolutional parameters for
scale k , W , v , b are attention mechanism parameters, and
α represents the dynamically computed attention weights.
The residual component, which contains transient fluctuations and noise from unpredictable events, is obtained
through:
random
= hist
-	t(reLnTd)- period
(6)
This multi-scale decomposition strategy delivers refined inputs for subsequent processes, significantly enhancing the model's noise resilience when managing complex traffic patterns with diverse temporal characteristics. Compared to traditional fixed decomposition methods, our learnable approach exhibits superior adaptability in capturing the non- stationary and multi-scale dynamics of urban traffic flows, which is essential for maintaining prediction accuracy over extended time horizons.
B. Dynamic Graph Construction and Spatio-Temporal Encoding for Evolving Dependency Capture
Following temporal decomposition, the Dynamic Graph Spatio-Temporal Encoding mechanism performs deep feature encoding for each component. Recognizing that spatial correlation patterns can vary significantly across components—where trend components may capture city-scale macro-correlations, while random components reflect localized event diffusion—we design component-specific encoding pathways. The core innovation is the dynamic GCN- GRU unit, which integrates data-driven dynamic graph construction with a deep fusion of Graph Convolutional Networks and Gated Recurrent Units.
As shown in Fig. 4, we reconstruct the adjacency matrix A(t) in real time at each time step t , addressing the critical
limitation of static graphs used in traditional approaches, which fail to capture the time-varying functional relationships within traffic networks. For data-driven dynamic graph construction, at each time step t , we first learn node-specific
embeddings through a transformation network:
(t)	(t)	(l)	(l) LE
Ei	= MLP(	component,i ;{WE ,bE }l=1)
=σ(WE(LE)σ(	σ(WE(1) c(otm)ponent,i+bE(1))	)+bE(LE))
(7)

where (t) denotes the feature of node i for a specific component,i
component at time t , W (l) and b(l) are learnable parameters at layer l of the MLP, L is the total number of MLP layers, and σ is the activation function.
Based on these dynamic embeddings, we compute the similarity between nodes using an adaptive Gaussian kernel to construct the dynamic adjacency matrix.

‖Ei(t)-E(jt‖) 22
Ai(jt) = exp-	2	+ λ⋅ ij	(8)
	2σij	
σ = softplus (u | E(t) - E(t) | +c)	(9)
where σ is an adaptive bandwidth parameter learned for each node pair, u and c are learnable parameters, λ controls the strength of self-connections through indicator function ,
Fig. 4. Dynamic Graph Spatio-Temporal Encoding Architecture

For the dynamic GCN-GRU unit design, we integrate graph convolutional operations into the update and reset gate computations of the GRU. The spatial aggregation via graph convolution is formulated as follows:
Ha(gt)g =D-12A(t)D-12H(t-1)WGCN +bGCN	(10) where H (t-1) denotes the hidden state tensor from previous time step, A() =A(t)+I denotes the dynamic adjacency matrix with self-loops, D is the corresponding degree matrix (t)
with Dii = ∑ Aij , and W , b represent the learnable j
graph convolution parameters.
The gating mechanisms and state updates in the dynamic GCN-GRU are computed as follows:
z(t)	=σ(W [X(t) ⊕H(t-1)]+U H(t-1) +b )
r(t)	=σ(Wr[X(t) ⊕Ha(tg-g1)]+UrH(t-1) +br)
(t)	gg	(11) H	=tanh(Wh[X(t) ⊕Ha(gt-g1)] +Uh(r(t)	H(t-1))+bh)
H(t) =(1-z(t))	H(t-1) +z(t)	H(t)
where X (t) is the current input, W , U , b , W , U , b , W , , z, z, z, r, r, r, h,
U , b are all learnable parameters, σ is the sigmoid activation, tanh is hyperbolic tangent, ⊕ denotes concatenation, and denotes element-wise multiplication. This innovative embedding ensures that each hidden state update incorporates the most recent dynamic spatial
neighborhood information, enabling accurate tracking of spatio-temporal evolution over extended time horizons.
C. Generative Adversarial Enhancement for Long-Range Prediction Stability
The Generative Prediction Enhancement (GPE) mechanism represents the core innovation for addressing error accumulation during long-range forecasting. Rather than relying solely on incremental extrapolation from history to future, we generate an additional macro-level prediction of the overall future state to correct and guide step-by-step predictions. We employ conditional generative adversarial network (cGAN) for this objective, as its adversarial training mechanism encourages the generator to learn the intrinsic distribution of real data, producing realistic and diverse future scenarios rather than simple averages or linear extrapolations.
As illustrated in Figure 5, the GPE mechanism comprises generator (G) and discriminator (D). The generator takes spatio-temporal features encoded by previous mechanisms encoded =[trend ⊕ period ⊕ random], random noise vector z~	(0, I) for diversity, and future-related conditional information	as inputs, generating latent spatio-temporal pattern	∈	N×Dz ×H for future H time steps through a deep hierarchical transformation:

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g(e0n) =Win[encoded ⊕znoise⊕ future]+bin
(l)		=LayerNorm W(l) (l-1) +b(l) +ReLU W(l) (0)	, l=1,…,L (12) gen	gen gen	gen	skip gen	G
gen =tanh(Wout g(eLnG)+bout)
where W , b , {W(l), b(l) ,W(l) }LG , W , b are learnable in	in	gen gen	skip l=1	out	out generator parameters, L is the number of generator layers, and LayerNorm denotes layer normalization.
We innovatively introduce multi-scale gating within the generator to capture temporal patterns at different resolutions:

Fig. 5. Generative Prediction Enhancement Framework

= adv	true_future ~pdata

s=1

logD(s) (	t(rsu)e_future , future ) +

(14)

S znoise~pz∑log(1-D(s)(G(s)( encoded,znoise, future), future) s =1	 where D(s) and G(s) represent discriminator and generator components at scale s , and S is the total number of scales used in multi-scale adversarial training.
For the final integrated prediction, we employ a hierarchical attention mechanism to dynamically balance information from different sources and temporal scales:
Q=WQ concat, K=WK[ DGTEM⊕ gen], V=WV[ DGTEM⊕ gen] Aattention= softmax QK	， fused= AattentionV + concat d

(15)

Where	=⊕, W , W , W are learnable
concat	DGTEM	gen ,	Q ,	K ,	V projection matrices, d is the dimension of the key vectors, and the attention mechanism enables dynamic weighting of historical encodings and future guidance.
The final prediction is obtained through a multi-layer projection with residual connections:
(s)	(s)	(ls )	(s)
g	σ gate gen	gate

(13)

(ls +1)	(s)	(s)	(s)	(s) gesn	= gcoarse +(1-g)	fine where g (s) represents the gating vector at scale s ,	(s)	and
(s)		represent features at coarse and fine temporal resolutions, enabling adaptive fusion of multi-scale temporal information.
The discriminator distinguishes between genuine future spatio-temporal patterns true future and generated gen , with _g adversarial loss defined through a multi-scale discrimination
(0) pred = fused p(lr)ed=LayerNorm(Wp(rle)d p(lre-d1)+bp(lr)ed+	p(0re)d), l=1,…,LP (16) pred =Wfinal p(rePd)+bfinal
Where {Wp(rle)d,b(plr)ed}lL=P1, Wfinal , bfinal are learnable parameters of the prediction network, and L is the number of prediction layers.
The complete optimization objective combines prediction accuracy with generative adversarial training:

1 pred =N⋅H

NH
∑∑(i,t)	(i,t)
pred	true _ future
i=1 t=1
NH
1		(i,t)	(i,t)	2
+α⋅	∑∑( pred - true_future )
N⋅ H i=1t=1
total = λ1	pred + λ2	adv + λ3	reg

(17)

(18)

where	combines mean absolute error and mean squared pred
error with weight α ,	is the adversarial loss,
adv	reg represents additional regularization terms, and λ , λ , λ are balancing hyperparameters.
Through this integrated framework, G-STGN achieves organic integration of multi-scale temporal resolution, dynamic graph spatio-temporal encoding, and generative prediction enhancement, providing a novel solution to core challenges in long-range urban mobility forecasting. The complete prediction algorithm flow of G-STGN is summarized in Algorithm 1.
V. EXPERIMENT AND ANALYSIS
In this section, we present the experimental design and evaluate the performance of our proposed G-STGN model against various baseline methods through a series of experiments conducted on two representative open-source datasets.
A.		Experimental Setup
We selected the M25 Motorway and TaxiBJ datasets due to their complementary characteristics, which effectively represent key application scenarios in consumer electronics. The M25 dataset, characterized by high-speed highway traffic and notable holiday effects, aligns directly with the requirements of in-vehicle navigation systems for inter-city travel and long-distance route planning. In contrast, the TaxiBJ dataset captures complex urban mobility patterns and ride-hailing dynamics, making it representative of core use cases for urban navigation applications and shared mobility platforms. Collectively, these datasets offer comprehensive coverage of the operational environments encountered by consumer electronics applications within intelligent transportation systems.
TABLE I STATISTICS OF THE DATASETS
Dataset	Nodes	Time Range	Granularity	Timesteps	Train/Val/ Test	
		2021/01/01				
M25	268	-
2021/03/31	5 min	25,920	6:2:2	
TaxiBJ	32x32 Grid	2015/11/01
-
2015/11/30	30 min	1,440	6:2:2	
Table I summarizes the statistical details of the two datasets, including the scenario type, number of nodes, time range, time granularity, total number of timesteps, and the corresponding split ratios for training, validation, and test sets (as indicated in the last column labeled "Train/Val/Test"). Specifically, the M25 Dataset focuses on motorway scenarios and comprises 268 monitoring nodes with a total of 25,920 timesteps. The TaxiBJ Dataset, centered on urban road scenarios, divides the monitoring area into a 32×32 grid, containing a total of 1,440 timesteps.
To quantitatively assess the prediction accuracy of the models, we employed three widely recognized evaluation metrics: Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE).
For a comprehensive benchmarking of G-STGN's performance, we selected the following representative traffic flow forecasting models as baselines, covering a spectrum of methodologies from traditional deep learning to advanced graph neural networks and attention-based mechanisms:
•		TCN: This model utilizes time series modeling through a combination of causal and dilated convolutions, designed to mitigate gradient vanishing and enhance the capture of long-range temporal dependencies.
•		STGNN [13]:	By integrating static Graph Convolutional Networks with time series models, this approach enables preliminary joint modeling of spatial dependencies and temporal dynamics.
•		DCRNN [14]: Employing a deep fusion of diffusion convolution and Gated Recurrent Units, this model addresses limitations of STGNN, such as inflexible static graph modeling and limited temporal memory capacity.
•		ASTGCN [46]: This model innovatively combines graph attention mechanisms with Temporal Convolutional Networks (TCNs) to overcome issues like undifferentiated spatial weighting and inefficient multi-scale temporal capture in conventional models.
•		Graph WaveNet [48]: Adopting a strategy that integrates data-driven adaptive adjacency matrices with multi-branch dilated temporal convolutions, this model aims to overcome the core limitation of traditional graph convolutional networks that depend on predefined topological structures.
B.		Convergence Efficiency Comparison
In practical deployments for consumer electronics, model training efficiency is a critical factor. We analyzed the convergence behavior of G-STGN and several baseline models on the TaxiBJ dataset and M25 dataset.
Fig. 6 and Fig. 7 illustrate the training loss curves across epochs for G-STGN and the baseline models on the TaxiBJ and M25 datasets, respectively. As shown, G-STGN (represented by the deep blue curve) achieves the fastest convergence speed and the lowest final loss value. It exhibits a rapid initial decrease in loss and quickly stabilizes at a low error level with minimal oscillations. In contrast, the baseline models either converge more gradually or plateau at relatively higher loss values, underscoring the training efficiency and stability of the proposed G-STGN framework.

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Fig. 6. Training Loss Curves of G-STGN and Baselines on

Fig. 7. Training Loss Curves of G-STGN and Baselines on
M25 Dataset

This efficient training can be attributed to the model's synergistic modular design. The Learnable Multi-scale Temporal Decomposition (LMTD) mechanism effectively decomposes the complex, non-stationary raw signals into three more manageable sub-components—trend, periodic, and random—thereby simplifying the learning task for subsequent layers. This structured approach provides a better-conditioned optimization landscape, enabling the model to rapidly converge to a high-quality solution.

Model	Final Training Loss	Epochs to Converge (approx.)	
TCN	0.3199	~50	
STGNN [13]	0.2422	~70	
DCRNN [14]	0.6013	~70	
ASTGCN [46]	0.717	>80 (not fully converged)	
GraphWaveNet
[48]	0.2926	~60	
G-STGN(ours)	0.2422	~50	

TABLE II
TRAINING EFFICIENCY COMPARISON ON TAXIBJ DATASET

As shown in Table II, G-STGN achieves convergence in approximately 50 epochs, demonstrating higher training efficiency than the baseline models. In contrast, STGNN [13] and DCRNN [14] require about 70 epochs to converge, while

Table II provides a quantitative summary of the training process, further highlighting the efficiency of G-STGN.
GraphWaveNet [48] takes approximately 60 epochs. ASTGCN [46] fails to reach full convergence even after 80 epochs. These results indicate that G-STGN attains a converged state more rapidly, showcasing its superior training efficiency.
Moreover, G-STGN achieves a final training loss of 0.2422, which is on par with STGNN [13] but significantly lower than those of DCRNN [14] (0.6013), ASTGCN [46] (0.717), and GraphWaveNet [48] (0.2926). This indicates that G-STGN not only fits the training data more effectively but also achieves a lower training loss, suggesting potential advantages in predictive performance.
Overall, G-STGN demonstrates superior comprehensive performance by converging in fewer epochs and achieving a lower training loss compared to the baseline models. This confirms that G-STGN holds distinct advantages in both training efficiency and model performance.
C.		Prediction Accuracy Comparison
Table III summarizes the overall prediction performance of G-STGN and the baseline models for a 60-minute forecast horizon on both datasets. The results unequivocally demonstrate the superior performance of our proposed model.
TABLE III
OVERALL PREDICTION PERFORMANCE COMPARISON OF G-STGN AND BASELINE MODELS ON THE M25 AND TAXIBJ DATASETS

Model	M25	TaxiBJ	
	MAE	RMSE	MAE	RMSE	
TCN	131.55	253.884	35.65	56.95	
STGNN [13]	124.58	187.04	38.28	53.51	
DCRNN [14]	124.29	124.298	35.55	55.55	
ASTGC [46]	124.61	186.99	38.35	58.99	
Graph WaveNet
[48]	125.66	187.61	37.25	56.83	
G-STGN(ours)	122.857	155.60	30.67	49.68	

On the M25 dataset, our proposed G-STGN model achieves the best performance, with an MAE of 122.857 and an RMSE of 155.60. Compared to the strongest baseline model, DCRNN [14], our method reduces MAE by 1.2% and achieves a notable 25.2% reduction in RMSE. This breakthrough demonstrates the model's exceptional capability in handling complex traffic patterns in large-scale highway networks.
On the more challenging TaxiBJ urban traffic dataset, G- STGN also exhibits significant advantages, achieving an MAE of 30.67 and an RMSE of 49.68, outperforming all compared models across the board. Relative to the best-performing baseline, DCRNN [14], our model achieves substantial reductions of 13.7% in MAE and 10.6% in RMSE. Notably, G-STGN also outperforms recent dynamic graph models like ASTGCN [46] and Graph WaveNet [48] by a significant margin. This can be attributed to our component-specific dynamic graph construction in DGTEM, which more precisely captures the distinct spatial evolution patterns of trend, periodic, and random components, unlike these baselines that typically model a single, mixed dynamic graph.
D.		Long-Range Capability Error Analysis
Visual Analysis of Error Accumulation: As graphically illustrated in Fig. 8, the prediction errors of all baseline models exhibit a discernible upward trend as the forecasting horizon extends, clearly demonstrating the issue of error accumulation over time. In contrast, the curve corresponding to G-STGN remains substantially flatter, indicating significantly suppressed error propagation.

Fig. 8. RMSE Comparison under Different Prediction
Horizons on TaxiBJ Dataset and M25 Dataset
This observed performance provides strong empirical evidence for the effectiveness of the proposed Generative Prediction Enhancement (GPE) mechanism. Traditional autoregressive models, including the powerful Transformer-based PDFormer, are fundamentally constrained by their iterative prediction paradigm, where each future step is forecasted based on previous predictions. As highlighted in [21], this inherent reliance leads to distribution drift as errors compound over long horizons. The GPE mechanism in G-STGN fundamentally breaks this paradigm by leveraging a conditional Generative Adversarial Network (cGAN) to generate a holistic "blueprint" of the future spatio-temporal state ( ). This generated macroscopic distribution serves as a forward-looking guidance signal, which acts as a regularizer at each prediction step. It proactively corrects deviations and constrains the predicted trajectory within a distributionally plausible manifold. This corrective mechanism is the core reason for G-STGN's superior stability and accuracy in long-range forecasting—a critical feature for real-world applications such as predicting electric vehicle charging-station availability 60 minutes ahead.
E.		Ablation Study on Components
We conducted ablation experiments on the G-STGN model to assess the individual contributions of four core components—Temporal Convolution Blocks (Temporal Convs), Graph Convolutional Layers (GCN), GRU Temporal Encoding Layers, and External Temporal Features—to the prediction accuracy of traffic flow ( ) and speed. These experiments aim to elucidate the role of each component in capturing spatio-temporal dependencies and enhancing prediction performance.
Two key metrics, Root Mean Square Error (RMSE) and Mean Absolute Error (MAE), were employed to quantify the deviation between predicted and actual values. Using the full G-STGN model as the baseline, we constructed three variants: one without the multi-scale temporal convolution mechanism, to evaluate its role in multi-scale feature extraction; another without the GRU temporal encoding mechanism, to examine its importance in modeling temporal dependencies; and a basic temporal model containing only essential structures, to measure the collective contribution of the two aforementioned components.
The performance comparison between the original model and its variants on the test set is summarized in Table VI, where lower values indicate better prediction accuracy.
TABLE Ⅵ
ABLATION EXPERIMENT PERFORMANCE EVALUATION
		Model	MAE		RMSE	MAE&RMSE	
	G-STGN	96.4482		168.2393		-	
Without Multiscale Temporal Convolutions	97.2898		172.1717	MAE↑0.87%, RMSE↑2.34%	
	Without GRU Temporal Encoding	98.2289		173.2525	MAE↑1.85%, RMSE↑2.98%	
Basic Temporal Model	100.9602		179.5792	MAE↑4.68%, RMSE↑6.74%	

										
									
Fig. 9. Comparison of Ablation Study Performance
Based on the results of the ablation experiments detailed in Table VI and visualized in Fig. 9, the efficacy of the core mechanisms within the G-STGN model in enhancing prediction performance is conclusively demonstrated. The removal of the multi-scale temporal convolution mechanism resulted in performance degradation, with MAE and RMSE increasing by 0.87% and 2.34%, respectively. This confirms the mechanism's critical role in capturing traffic features across diverse temporal granularities via its multi-scale receptive field, thereby mitigating extreme prediction errors.
In contrast, the absence of the GRU temporal encoding mechanism induced a more pronounced performance drop, with MAE and RMSE rising by 1.85% and 2.98%, respectively. This underscores its paramount importance in modeling both long- and short-term temporal dependencies within traffic flow, with its impact being approximately twice that of the temporal convolution mechanism. The substantial performance chasm between the baseline temporal model and the full-fledged G-STGN model—evidenced by a 4.68% increase in MAE and a 6.74% increase in RMSE—further validates the synergistic effectiveness of the integrated "multiscale temporal convolution + GRU temporal encoding" architecture. This combined design holistically augments the model's capacity to represent temporal dynamics through complementary multi-scale feature extraction and dependency modeling. Furthermore, the consistent observation that the relative increase in RMSE exceeds that of MAE across all model variants highlights a shared strength of these core components: they collectively enhance the model's robustness to sudden traffic fluctuations and its ability to suppress extreme prediction errors.
F.		Discussion and Implications for Consumer Electronics
The experimental results demonstrate that G-STGN achieves significant improvements in long-range urban mobility prediction, with profound implications for consumer electronics applications.
Practical Significance of Performance Gains: The 13.7% MAE improvement on the TaxiBJ dataset translates to approximately 4-5 minutes of reduced error in 60-minute travel time estimates for urban navigation systems. For EV route planning applications, this enhanced accuracy enables more reliable charging station availability forecasts, potentially reducing waiting times by 15-20% during peak hours based on queueing model simulations.
Generative Enhancement Mechanism: The superior performance of G-STGN, particularly in long-range scenarios (60-minute prediction), validates the effectiveness of the generative prediction enhancement mechanism. By providing macroscopic future guidance, the model compensates for the inherent limitations of purely autoregressive approaches, which is crucial for applications requiring reliable extended- horizon forecasts.
Dynamic Graph Representation: The component-specific dynamic graph construction enables G-STGN to adapt to varying spatial dependency patterns throughout the day. This capability is particularly valuable for ride-hailing platforms, where morning rush hours exhibit strong residential-to- commercial area correlations, while evening patterns reverse this flow.
Implementation Considerations: While G-STGN demonstrates superior accuracy, its practical deployment in resource-constrained consumer electronics requires consideration of computational efficiency. The modular architecture permits component-wise optimization, with the LMTD and DGTEM mechanisms being amenable to model compression techniques for mobile device deployment.
G.		Limitations and Future Directions
Despite its strong performance, G-STGN has several limitations that warrant consideration. First, the model's computational complexity, particularly the dynamic graph construction and cGAN training, may challenge real-time deployment on resource-constrained mobile devices. While not the focus of this paper, initial analysis suggests that the DGTEM and GPE mechanisms contribute significantly to the parameter count compared to simpler baselines like TCN. However, the modular design of G-STGN facilitates future work on component-wise model compression. Future work will explore model quantization and knowledge distillation techniques to enhance deployment efficiency.
Second, the current implementation primarily focuses on traffic flow data, while real-world consumer electronics applications often require integration of multiple data sources, including weather conditions, event calendars, and real-time incident reports. Extending G-STGN to effectively incorporate such heterogeneous data remains an important direction.
Third, the model's performance in extreme scenarios (e.g., major accidents, severe weather events) requires further validation. While the generative enhancement provides some robustness to distribution shifts, dedicated mechanisms for handling out-of-distribution events could enhance practical reliability.
Finally, the interpretability of the generative guidance signals represents both a limitation and opportunity. Enhancing model transparency would increase user trust in consumer applications, particularly for safety-critical scenarios like autonomous vehicle routing.
Ⅵ. CONCLUSION
This paper has presented G-STGN, a novel generative- enhanced framework for long-range urban mobility prediction in consumer electronics applications. Through the synergistic integration of learnable temporal decomposition, dynamic graph encoding, and generative adversarial enhancement, the model systematically addresses the fundamental challenges of non-stationary temporal patterns, evolving spatial dependencies, and error accumulation.
Extensive experiments demonstrate that G-STGN significantly outperforms state-of-the-art baselines, achieving 13.7% MAE and 10.6% RMSE improvements on urban traffic data while reducing the RMSE by 25.2% in highway scenarios, indicating significantly suppressed error accumulation.
These advancements directly translate to enhanced reliability for consumer electronics applications including invehicle navigation, EV route planning, and shared mobility platforms.
Building on this work, we identify three promising directions: (1) Development of lightweight variants through model compression and quantization for efficient deployment on mobile devices; (2) Integration of multi-source urban data (weather, events, social media) to enhance contextual awareness; (3) Exploration of explainable AI techniques to improve user trust and transparency in generative guidance mechanisms. These directions will further bridge the gap between research innovation and practical deployment in consumer electronics ecosystems.
ACKNOWLEDGMENTS
This study received partial funding from the National Natural Science Foundation of China (72274058), the General Project of Xiangjiang Laboratory (25XJ03006), and the UK Royal Society (International Exchange Award IESR3243049).

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REFERENCES
[1]		W. Fang, W. Zhuo, Y. Song, J. Yan, T. Zhou, and J. Qin, "ΔFree -LSTM: An Error Distribution Free Deep Learning for Short-Term Traffic Flow Forecasting," Neurocomputing, vol. 526, pp. 180-190, Mar. 2023.
[2]		Y. Jiang, Y. Liu, H. Cui, J. Liu, Y. Mao and X. He, "Traffic Prediction Using Lightweight Large Model in AAV-Assisted Mobile Computing for Time-Critical Consumer Electronics," IEEE Transactions on Consumer Electronics, vol. 71, no. 2, pp. 5619-5630, May 2025.
[3]		S. Wang, J. Zhang, J. Li, H. Miao and J. Cao, "Traffic Accident Risk Prediction via Multi-View Multi-Task Spatio-Temporal Networks," IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 12, pp. 12323-12336, Dec. 2023.
[4]		H. Yang, J. Jiang, Z. Zhao, R. Pan, and S. Tao, "STVANet: A SpatioTemporal Visual Attention Framework With Large Kernel Attention Mechanism for Citywide Traffic Dynamics Prediction," Expert Systems with Applications, vol. 254, p. 124466, Nov. 2024.
[5]		A. Ali et al., "A Resource-Aware Multi-Graph Neural Network for Urban Traffic Flow Prediction in Multi-Access Edge Computing Systems," IEEE Transactions on Consumer Electronics, vol. 70, no. 4, pp. 72527265, Nov. 2024.
[6]		R. He, Y. Xiao, X. Lu, S. Zhang, and Y. Liu, "ST-3DGMR: SpatioTemporal 3D Grouped Multiscale Resnet Network for Region-Based Urban Traffic Flow Prediction," Information Sciences, vol. 624, pp. 68-93, May 2023.
[7]		Z. Wei et al., "STGSA: A Novel Spatial-Temporal Graph Synchronous Aggregation Model for Traffic Prediction," IEEE/CAA Journal of Automatica Sinica, vol. 10, no. 1, pp. 226-238, Jan. 2023.
[8]		M. Aljebreen et al., "Enhancing Traffic Flow Prediction in Intelligent Cyber-Physical Systems: A Novel Bi-LSTM-Based Approach With Kalman Filter Integration," IEEE Transactions on Consumer Electronics, vol. 70, no. 1, pp. 1889-1902, Feb. 2024.
[9]		Y. Liu, T. Feng, S. Rasouli, and M. Wong, "ST-DAGCN: A Spatiotemporal Dual Adaptive Graph Convolutional Network Model for Traffic Prediction," Neurocomputing, vol. 601, p. 128175, Oct. 2024.
[10]		Q. Zhang, C. Li, F. Su and Y. Li, "Spatiotemporal Residual Graph Attention Network for Traffic Flow Forecasting," IEEE Internet of Things Journal, vol. 10, no. 13, pp. 11518-11532, July 2023.
[11]		G. Huo, Y. Zhang, B. Wang, J. Gao, Y. Hu and B. Yin, "Hierarchical Spatio-Temporal Graph Convolutional Networks and Transformer Network for Traffic Flow Forecasting," IEEE Transactions on Intelligent Transportation Systems, vol. 24, no. 4, pp. 3855-3867, Apr. 2023.
[12]		Y. Ma, H. Lou, M. Yan, F. Sun, and G. Li, "Spatio-Temporal Fusion Graph Convolutional Network for Traffic Flow Forecasting," Information Fusion, vol. 104, p. 102196, Apr. 2024.
[13]		M. Li and Z. Zhu, "Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecasting," Proceedings of the Proceedings of the AAAI Conference on Artificial Intelligence Conference on Artificial Intelligence, vol. 35, no. 5, pp. 4189-4196, May 2021.
[14]		Y. Li, R. Yu, C. Shahabi, Y. Liu, "Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting," Machine Learning, vol. 24, no. 4, pp. 3855-3867, Feb. 2018.
[15]		J. Wang, J. Liang, J. Cui, and J. Liang, "Semi-Supervised Learning With Mixed-Order Graph Convolutional Networks," Information Sciences, vol. 573, pp. 171-181, Sept. 2021.
[16]		X. Zhou et al., "Reconstructed Graph Neural Network With Knowledge Distillation for Lightweight Anomaly Detection," IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 9, pp. 11817-11828, Sept. 2024.
[17]		Y. Wen, P. Xu, Z. Li, W. Xu, and X. Wang, "RPConvformer: A Novel Transformer-Based Deep Neural Networks for Traffic Flow Prediction," Expert Systems with Applications, vol. 218, p. 119587, May 2023.
[18]		J. Cheng, K. Li, Y. Liang, L. Sun, J. Yan and Y. Wu, "Rethinking Urban Mobility Prediction: A Multivariate Time Series Forecasting Approach," IEEE Transactions on Intelligent Transportation Systems, vol. 26, no. 2, pp. 2543-2557, Feb. 2025.
[19]		X. Yan, X. Gan, J. Tang, D. Zhang and R. Wang, "ProSTformer: Progressive Space-Time Self-Attention Model for Short-Term Traffic Flow Forecasting," IEEE Transactions on Intelligent Transportation Systems, vol. 25, no. 9, pp. 10802-10816, Sept. 2024.
[20]		A. A. Karim and N. Nower, "Probabilistic Spatio-Temporal Graph Convolutional Network for Traffic Forecasting," Applied Intelligence, vol. 54, no. 11-12, pp. 7070-7085, June 2024.
[21]		J. Jiang, C. Han, W. X. Zhao, and J. Wang, "PDFormer: Propagation Delay-Aware Dynamic Long-Range Transformer for Traffic Flow Prediction," Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, no. 4, pp. 4365-4373, June 2023.
[22]		J. Fan, W. Weng, Q. Chen, H. Wu, and J. Wu, "PDG2Seq: Periodic Dynamic Graph to Sequence Model for Traffic Flow Prediction," Neural Networks, vol. 183, p. 106941, Mar. 2025.
[23]		J. Qin et al., "MUSE-Net: Disentangling Multi-Periodicity for Traffic Flow Forecasting," in 2024 IEEE 40th International Conference on Data Engineering (ICDE), Utrecht, Netherlands, 2024, pp. 1282-1295.
[24]		S. Du, T. Yang, F. Teng, J. Zhang, T. Li, and Y. Zheng, "Multi-Scale Feature Enhanced Spatio-Temporal Learning for Traffic Flow Forecasting," Knowledge-Based Systems, vol. 294, p. 111787, June 2024.
[25]		X. Zhou et al., "Spatial–temporal federated transfer learning with multisensor data fusion for cooperative positioning," Information Fusion, vol. 105, p. 102182, May 2024.
[26]		G. Zou, Z. Lai, T. Wang, Z. Liu and Y. Li, "MT-STNet: A Novel MultiTask Spatiotemporal Network for Highway Traffic Flow Prediction," IEEE Transactions on Intelligent Transportation Systems, vol. 25, no. 7, pp. 8221-8236, July 2024.
[27]		Q. Lv, G. Chen, Z. Yang, W. Zhong and C. Y. -C. Chen, "Meta Learning With Graph Attention Networks for Low-Data Drug Discovery," IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 8, pp. 11218-11230, Aug. 2024.
[28]		A. L. Schaffer, T. A. Dobbins, and S.-A. Pearson, "Interrupted Time Series Analysis Using Autoregressive Integrated Moving Average (Arima) Models: A Guide for Evaluating Large-Scale Health Interventions," BMC Medical Research Methodology, vol. 21, no. 1, p. 58, Dec. 2021.
[29]		X. Han and S. Gong, "LST-GCN: Long Short-Term Memory Embedded Graph Convolution Network for Traffic Flow Forecasting," Electronics, vol. 11, no. 14, p. 2230, July 2022.
[30]		Y. Lin, "Long-Term Traffic Flow Prediction Using Stochastic Configuration Networks for Smart Cities," ICCK Transactions on Intelligent Systematics, vol. 1, no. 2, pp. 79-90, Sept. 2024.
[31]		X. Zhou et al., "Information Theoretic Learning-Enhanced Dual- Generative Adversarial Networks With Causal Representation for Robust OOD Generalization," IEEE Transactions on Neural Networks and Learning Systems, vol. 36, no. 2, pp. 2066-2079, Feb. 2025.
[32]		T. Liu, A. Jiang, J. Zhou, M. Li, and H. K. Kwan, "GraphSAGE-Based Dynamic Spatial–Temporal Graph Convolutional Network for Traffic Prediction," IEEE Transactions on Intelligent Transportation Systems, vol. 24, no. 10, pp. 11210-11224, Oct. 2023.
[33]		Y. Song, R. Luo, T. Zhou, C. Zhou, and R. Su, "Graph Attention Informer for Long-Term Traffic Flow Prediction under The Impact of Sports Events," Sensors, vol. 24, no. 15, p. 4796, July 2024.
[34]		W. Weng et al., "A Decomposition Dynamic Graph Convolutional Recurrent Network for Traffic Forecasting," Pattern Recognition, vol. 142, p. 109670, Oct. 2023.
[35]		F. Li et al., "Dynamic Graph Convolutional Recurrent Network for Traffic Prediction: Benchmark and Solution," ACM Transactions on Knowledge Discovery from Data, vol. 17, no. 1, pp. 1-21, Feb. 2023.
[36]		Z. Jin, J. Qian, Z. Kong, and C. Pan, "A Mobility Aware Network Traffic Prediction Model Based on Dynamic Graph Attention Spatio-Temporal Network," Computer Networks, vol. 235, p. 109981, Nov. 2023.
[37]		Y. Xu, L. Han, T. Zhu, L. Sun, B. Du, and W. Lv, "Generic Dynamic Graph Convolutional Network for Traffic Flow Forecasting," Information Fusion, vol. 100, p. 101946, Dec. 2023.
[38]		Y. Miao, X. Tang, Q. Wang, and L. Yu, "GDENet: Graph Differential Equation Network for Traffic Flow Prediction," International Journal of Intelligent Systems, vol. 2023, no. 1, p. 7099652, Jan. 2023.
[39]		R. Al-Huthaifi, T. Li, Z. Al-Huda, W. Huang, Z. Luo, and P. Xie, "FedGODE: Secure Traffic Flow Prediction Based on Federated Learning and Graph Ordinary Differential Equation Networks," Knowledge-Based Systems, vol. 299, p. 112029, Sept. 2024.
[40]		Y. Xu, X. Cai, E. Wang, W. Liu, Y. Yang, and F. Yang, "Dynamic Traffic Correlations Based Spatio-Temporal Graph Convolutional Network for Urban Traffic Prediction," Information Sciences, vol. 621, pp. 580-595, Apr. 2023.
[41]		Y. Sun et al., "Dual Dynamic Spatial-Temporal Graph Convolution Network for Traffic Prediction," IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 12, pp. 23680-23693, Dec. 2022.
[42]		X. Zhou, W. Liang, A. Kawai, K. Fueda, J. She and K. I. -K. Wang, "Adaptive Segmentation Enhanced Asynchronous Federated Learning for Sustainable Intelligent Transportation Systems," IEEE Transactions on Intelligent Transportation Systems, vol. 25, no. 7, pp. 6658-6666, July 2024.
[43]		H. Novak, F. Bronić, A. Kolak, and V. Lešić, "Data-Driven Modeling of
Urban Traffic Travel Times for Short- and Long-Term Forecasting,"
IEEE Transactions on Intelligent Transportation Systems, vol. 24, no. 10, pp. 11198-11209, Oct. 2023.
[44]		Y. Ye, S. Xue, and A. Jiang, "Attention-Based Spatio-Temporal Graph
Convolutional Network Considering External Factors for Multi-Step
Traffic Flow Prediction," Digital Communications and Networks, vol. 8, no. 3, pp. 343-350, June 2022.
[45]		X. Wu, M. Chen, T. Zhu, D. Chen, and J. Xiong, "Pre-Training Enhanced
Spatio-Temporal Graph Neural Network for Predicting Influent Water
Quality and Flow Rate of Wastewater Treatment Plant: Improvement of
Forecast Accuracy And Analysis Of Related Factors," Science of The
Total Environment, vol. 951, p. 175411, Nov. 2024.
[46]		X. Zhang, X. Chen, H. Tang, Y. Wu, H. Shen, and J. Li, "AdpSTGCN:
Adaptive Spatial-Temporal Graph Convolutional Network for Traffic
Forecasting," Knowledge-Based Systems, vol. 301, p. 112295, Oct. 2024.
[47]		X. Zang et al., "Pretraining-improved Spatiotemporal graph network for
the generalization performance enhancement of traffic forecasting,"
Accesses, vol. 929, p. 27668, July 2025.
[48]		G. Jin, C. Liu, Z. Xi, H. Sha, Y. Liu, and J. Huang, "Adaptive Dual-View
Wavenet for Urban Spatial-Temporal Event Prediction," Information
Sciences, vol. 588, pp. 315-330, Apr. 2022.
[49]		Z. Wang, H. Zhang, C. Qian, B. Li, Y. Cao and M. Jiang, "A Hybrid
LSTM Network for Long-Range Vehicle Trajectory Prediction Based on
Adaptive Chirp Mode Decomposition," IEEE Sensors Journal, vol. 24, no.
4,		pp. 5359-5369, Feb. 2024.
[50]		J. Zhao, Z. Liu, Q. Sun, Q. Li, X. Jia, and R. Zhang, "Attention-Based
Dynamic Spatial-Temporal Graph Convolutional Networks for Traffic
Speed Forecasting," Expert Systems with Applications, vol. 204, p.
117511, Oct. 2022.