Predicting human mobility remains a fundamental challenge, especially when individuals deviate from routine patterns due to exploration, disruptions, or rare events. While sequential models like Transformers excel at capturing regular movement patterns, their performance often degrades under nonroutine scenarios involving rare or unfamiliar transitions. To address this, we propose ROAM (Routine-Oriented Adaptive Mobility Predictor), a novel framework that jointly models human mobility from both sequential and relational perspectives, enabling adaptive handling of both routine and nonroutine behaviors during prediction. ROAM combines a sequential encoder that captures historically frequent transitions with a complementary graph-based relational reasoning module that encodes both user-specific and group-level mobility structures. To dynamically integrate these views, we introduce a hierarchical confidence-aware gating mechanism that adaptively balances sequential and relational predictions based on their internal reliability. Extensive experiments on real-world mobility datasets show that ROAM consistently outperforms state-of-the-art baselines in next location prediction. Further analysis reveals that the combination of sequential and relational reasoning substantially improves robustness, particularly under out-of-routine scenarios.

Learning urban region embeddings has substantially advanced urban analysis, but their typical focus on individual cities leads to disparate embedding spaces, hindering cross-city knowledge transfer and the reuse of downstream task predictors. To tackle this issue, we present Consistency Region Embedding (CoRE), a unified framework integrating region embedding learning with cross-city latent space alignment. CoRE first embeds regions from two cities into separate latent spaces, followed by the alignment of latent space manifolds and fine-grained individual regions from both cities. This ensures compatible and comparable embeddings within aligned latent spaces, enabling predictions of various socioeconomic indicators without ground truth labels by migrating knowledge from label-rich cities. Extensive experiments show CoRE outperforms competitive baselines, confirming its effectiveness for cross-city knowledge transfer via aligned latent spaces.

In urban planning, traffic flow prediction, a core component of Intelligent Transportation Systems, has made significant progress with the development of deep learning. The key problem of traffic flow prediction lies in capturing the complex spatio-temporal correlations in traffic flow. In recent years, more and more research has tended to apply Transformer-based models to solve this problem. However, Transformer-based models have two major limitations for traffic flow prediction: i) Most methods only focus on extracting data features within the attention head, while ignoring the correlation between these heads, making it difficult to integrate the multi-features of traffic data; ii) Most methods do not recognize the unique impact of nodes that serve as pivotal traffic hubs in the traffic networks, which cause the Transformer to excessively focus on the influence of non-pivotal nodes. In this study, we propose a novel Transformer-based model, the Multi-Feature Fusion Transformer (MF2former), aimed at addressing the above limitations of traffic flow prediction. MF2former incorporates the Augmented Synergistic Transformer Module to achieve a comprehensive multi-feature fusion of traffic data by enhancing the information capacity within self-attention heads and performing information fusion between these heads. Additionally, our model incorporates the Pivotal Node Module, which extracts pivotal nodes from all nodes and masks the global receptive field of the Transformer to enhance its focus on pivotal nodes in the traffic network. Our model is evaluated using two real-world traffic datasets, demonstrating superior performance compared to existing methods. This study provides a stable framework for accurate traffic flow prediction, offering valuable insights for urban planners and commuters.

As a crucial component of intelligent transportation systems (ITS), accurate traffic flow prediction has attracted considerable attention. Numerous popular models, such as recurrent neural networks (RNN) and graph convolution networks (GCN), have been extensively applied to this task. However, due to the constraints of the topology of urban road network and the law of dynamic change with time, the single-layer network model cannot capture the dynamic correlation between the spatial–temporal and the traditional road network characteristics for traffic flow. In this article, we propose a spatial–temporal twolayer synchronous network (ST2SNet) for traffic flow prediction. First, we introduce a novel network structure comprising a spatial–temporal fusion layer and a situation awareness layer, where temporal and spatial correlations are fused synchronously using a transformer architecture. Next, we incorporate the residual gated GCN structure and a 2-D convolution network to effectively integrate road network information and temporal trends for spatial–temporal traffic flow prediction. Finally, we evaluate ST2SNet on two benchmark datasets, PeMSD4, and PeMSD8. Compared to baseline methods, our model achieves MAE improvements ranging from 4.00% to 48.30% on PeMSD4 and from 4.88%to 54.13%on PeMSD8. These outstanding experimental results demonstrate that ST2SNet significantly enhances prediction performance.

3-D action recognition become a fast-pacing field in recent years. However, traditional approaches have limitations. They either focus on modeling overly detailed yet redundant information by reconstructing the coordinates of each body joint. Alternatively, they treat actions as a whole, overlooking the spatial and temporal variations in the semantic locality of actions. To address these limitations, we propose representing long-term actions as contexts of short-term actions organized by locality-aware graphs. In our framework, we take the inspiration that the continuity of motion and pose variations generate higher correlations. These correlations occur among spatio-temporally adjacent joints. Built upon this, we craft short-term actions as embeddings using spatio-temporal graph convolutions. This graph-based encoding not only captures richer high-level semantics but also maintains an awareness of the topology. To capture long-term action dynamics effectively, we integrate a graph convolutional gated recurrent unit (GraphGRU) for the fusion of action embeddings. Additionally, we introduce the context-aware topological attention (CTA) mechanism. Positioned between embedding encoding and context aggregation phases, CTA amplifies the features of context-relevant nodes. Lastly, we create self-supervision by contrasting predicted embeddings with actual encoded embeddings. This approach explicitly learns changes in dynamics to obtain distinct embeddings. Empirical evaluations demonstrate that our approach outperforms mainstream unsupervised 3-D action recognition methods.

Traffic forecasting is the foundation of intelligent transportation systems (ITS). In recent, graph neural networks (GNNs) have successfully captured spatial-temporal dependencies to forecast traffic conditions by transforming traffic data in the graph domain. Nevertheless, the existing methods focus only on learning informative graph representations and fail to model informative graph structures, which hinders the capture of dynamic spatial-temporal dependencies caused by dynamic factors such as weather, accidents, and special events. In this paper, we propose a novel task-oriented Spatial Graph Structure Learning (SGSL) method, which aims to capture dynamic dependencies by jointly learning graph structures and graph representations. Compared to methods that use spectral graph representations, we exploit a learnable spatial graph to effectively model dynamic dependencies in traffic data. Moreover, we directly define graph convolutions on spatial relations to specify different edge weights when aggregating the information of spatial neighbours. Thus, the graph structure alterations, i.e., the relation changes, and the time-varying weights of relations can be encapsulated, thereby effectively representing dynamic dependencies. The gradient descent strategy is introduced to periodically learn a spatial graph through joint optimization with a newly designed deep graph learning model named GAT-nLSTM. In this manner, the intrinsic behaviours of nodes are learned to capture correlations across periods. Notably, the optimization process is performed under the traffic forecasting constraint to ensure that the learned spatial graph is specific to this task. Compared with those of state-of-the-art baselines, the experimental results obtained on real-world traffic datasets show significant improvement, which verifies the superiority of the proposed SGSL.

Human mobility flow prediction forecasts the number of passengers coming into (inflows) or leaving from (outflows) every region of a city. It is crucial for many applications such as mobile marketing or route optimization. People have proposed deep learning methods to predict human mobility flows. However, existing methods neglect the hierarchical nature of human mobility behaviors. Each of us is unique, and we live beyond our neighborhoods. In the process of cross-regional activities, humans migrate in the hierarchical structure of buildings, neighborhoods, regions, cities, and countries. In this article, we propose a predictive framework, hierarchical fuzzy C-means (Hierarchical-FCM)-residual networks (ResNets), to capture the hierarchical structure of human mobility for prediction. First, we offer a Hierarchical-FCM clustering algorithm that is trained to learn the relationship between proximity and road network hierarchically on large human mobility data. Second, we design a fusion model to incorporate the knowledge learned from hierarchical clusters into deep ResNets to improve prediction accuracy. We compare our framework with 25 existing state-of-the-art models, ranging from traditional time-series and machine learning predictors, such as ARIMA and RNN, to the latest deep learning methods designed for human mobility prediction, such as ST-ResNets and DeepST. Our framework outperforms all existing models, by a margin of 2%–68% in terms of prediction accuracy, showing its effectiveness. We are among the first to incorporate the hierarchical structure of human mobility into location clustering for human mobility flow prediction. We empirically show that incorporating such knowledge significantly improves prediction performance.

Human mobility prediction is crucial for epidemic control, urban planning, and traffic forecasting systems. We observe urban traffic flow prediction has a hierarchical structure, in which human mobility prediction should consider not only the spatial and the temporal relationships, but also the high-level mobility trend between individuals and regions. In this paper, we propose a human mobility clustering algorithm based on trend iteration of spectral clustering (TISC) to incorporate the high-level human mobility trend between individuals and regions. We integrate our TISC clustering algorithm with two existing urban traffic flow predictive models: namely, deep spatio-temporal residual network (ST-ResNet) and deep spatio-temporal 3D network (ST-3DNet). By adapting our TISC clustering algorithm, the prediction accuracy of both algorithms has been improved significantly (30.96$\%$for ST-ResNet and 24.66$\%$for ST-3DNet). We also compare the TISC-based predictive framework with 26 state-of-the-art human mobility prediction algorithms. We observe that our TISC algorithm considerably outperforms all 26 methods, reducing the predictive error from 6.93% to 69.55$\%$.