Spatio-temporal prediction is fundamental to a wide range of applications, including traffic flow forecasting and air quality monitoring. However, real-world spatio-temporal systems are rarely governed by homogeneous or synchronized interactions. Spatial dependencies often vary across regions, temporal patterns evolve at multiple scales, and the influence of one location on another may emerge with dynamic, region-specific delays rather than in a synchronized manner. These heterogeneous and asynchronous lag characteristics pose substantial challenges to accurate prediction, whereas most existing methods rely on static spatial graphs or synchronized temporal modeling, which limits their ability to capture complex real-world dynamics. To address this, we propose the lag-heterogeneity guided spatio-temporal graph neural network (LH-GSTGNN). Rather than relying on stationary assumptions, LH-GSTGNN treats lag heterogeneity as an explicit modeling target. It characterizes evolving spatial dependencies, captures temporal dynamics across multiple ranges, and highlights delayed responses embedded in intermediate representations. In this way, the proposed framework preserves heterogeneous and asynchronous interactions that are otherwise prone to being smoothed out, yielding a more faithful representation of real-world spatio-temporal dynamics. Extensive experiments on nine real-world datasets covering traffic flow, traffic speed, and air quality prediction show that LH-GSTGNN consistently outperforms strong baselines, achieving up to 4.9% lower MAE and 2.9% lower RMSE than the second-best method. Visualization-based case studies further demonstrate its effectiveness in modeling both spatial heterogeneity and diverse lagged fluctuations.

Spatial temporal forecasting of urban sensors is essentially important for many urban systems, such as intelligent transportation and smart cities. However, due to the problem of hardware failure or network failure, there are some missing values or missing monitoring sensors that need to be interpolated. Recent research on deep learning has made substantial progress on imputation problem, especially temporal aspect (i.e., time series imputation), while little attention has been paid to spatial aspect (both dynamic and static) and long-term temporal dependencies. In this article, we proposed a spatial temporal imputation model, named Long Short-Term Graph Convolution Networks (LSTGCN), which includes gated temporal extraction (GTE) module, multi-head attention-based temporal capture (MHAT) module, long-term periodic temporal encoding (LPTE) module, and bidirectional spatial graph convolution (BSGC) module. The GTE adopts a gated mechanism to filter short-term temporal information, while the MHAT utilizes position encoding to enhance the difference of each timestamps, then use multi-head attention to capture short-term temporal dependency. The BSGC is adopted to handle with spatial relationships between sensor nodes. And we design a periodic encoding technique to process long-term temporal dependencies. The BSGC handles spatial relationships between sensor nodes, and a periodic encoding technique is used to process long-term temporal dependencies. Our experimental analysis includes completion and forecasting tasks, as well as transfer and ablation analyses. The results show that our proposed model outperforms state-of-the-art baselines on real-world datasets.

Traffic prediction is the core task of intelligent transportation system (ITS) and accurate traffic prediction can greatly improve the utilization of public resources. Dynamic interaction of multiple spatial relationships will influence the accuracy of traffic prediction. However, many existing methods only consider static spatial relationships, which restricts the accuracy of the prediction. To address the above problem, in this article, we propose the Dynamic Multi-Graph Fusion Network (DMGF-Net) to model the spatial-temporal correlations in traffic network. In the DMGF-Net, the fusion graph is designed to leverage and extract the various spatial correlations between different regions by fusing spatial graph, semantic graph, and spatial-semantic graph. Further, to dynamically learn the importance of different neighbors, we design the Dynamic Spatial-Temporal Unit (DSTU), which can adjust the aggregation weights of different neighbors by combining the convolution operation and the attention mechanism. It can selectively aggregate spatial-temporal features from different neighbors. Extensive experiments on three datasets demonstrate that effectiveness of our model, especially on PEMS08, our model achieves an increase of about 8.55% and 7.55% in terms of MAE and RMSE than the static model STGCN.

Due to sensor failure or power failure, the spatiotemporal data missing tends to have a greater impact on downstream tasks. Meanwhile, if sensors are scarce, some spatial positions without sensors need data augmentation. Existing workarounds focus on spatial information, often ignoring temporal information, or modeling the spatial and temporal domain separately for imputation. In this paper, we propose Long-term Multidimensional Spatial-Temporal Graph Convolution Network (LMSTGCN), which can not only inductively estimate some missing information, but also achieve data augmentation of target locations. It contains a gated temporal capture module and a multidimensional graph convolution module. The multidimensional graph convolution module can simultaneously model spatial and extra-short term temporal information, and can achieve exponential growth in the range of receptive fields. Corresponding to this module, we designed a spatiotemporal adjacency matrix construction method, which can generate spatiotemporal adjacency matrices of corresponding time lengths as needed. The gated temporal capture module can deal with the short term dependencies in sequences. In experimental analysis, results demonstrate that the proposed model outperforms the state-of-the-art baselines on real-world data sets.

Dynamic pricing plays an important role in solving the problems such as traffic load reduction, congestion control, and revenue improvement. Efficient dynamic pricing strategies can increase capacity utilization, total revenue of service providers, and the satisfaction of both passengers and drivers. Many proposed dynamic pricing technologies focus on short-term optimization and face poor scalability in modeling long-term goals for the limitations of solution optimality and prohibitive computation. In this article, a deep reinforcement learning framework is proposed to tackle the dynamic pricing problem for ride-hailing platforms. A soft actor-critic (SAC) algorithm is adopted in the reinforcement learning framework. First, the dynamic pricing problem is translated into a Markov Decision Process (MDP) and is set up in continuous action spaces, which is no need for the discretization of action space. Then, a new reward function is obtained by the order response rate and the KL-divergence between supply distribution and demand distribution. Experiments and case studies demonstrate that the proposed method outperforms the baselines in terms of order response rate and total revenue.

Traffic flow prediction is the upstream problem of path planning, intelligent transportation system, and other tasks. Many studies have been carried out on the traffic flow prediction of the spatio-temporal network, but the effects of spatio-temporal flexibility (historical data of the same type of time intervals in the same location will change flexibly) and spatio-temporal correlation (different road conditions have different effects at different times) have not been considered at the same time. We propose the Deep Spatio-temporal Adaptive 3D Convolution Neural Network (ST-A3DNet), which is a new scheme to solve both spatio-temporal correlation and flexibility, and consider spatio-temporal complexity (complex external factors, such as weather and holidays). Different from other traffic forecasting models, ST-A3DNet captures the spatio-temporal relationship at the same time through the Adaptive 3D convolution module, assigns different weights flexibly according to the influence of historical data, and obtains the impact of external factors on the flow through the ex-mask module. Considering the holidays and weather conditions, we train our model for experiments in Xi’an and Chengdu. We evaluate the ST-A3DNet and the results show that we have better results than the other 11 baselines.

Traffic spatial-temporal prediction is of great significance to traffic management and urban construction. In this paper, we propose a multi-task graph Synchronous neural network (MTSGNN) to synchronously predict the spatial-temporal data at the regions and transitions between regions. The method of constructing "multitask graph representation" is proposed to retain the information of regions and transitions that existing works can not reflect. Then our model synchronously captures multiple types of dynamic spatial correlations, models dynamic temporal dependencies and re-weights different time steps to solve the problem of long-term time modeling. In three real data sets, we verify the validity of the proposed model.

Detectors with high coverage have direct and far-reaching benefits for road users in route planning and avoiding traffic congestion, but utilizing these data presents unique challenges including: the dynamic temporal correlation, and the dynamic spatial correlation caused by changes in road conditions. Although the existing work considers the significance of modeling with spatial-temporal correlation, what it has learned is still a static road network structure, which cannot reflect the dynamic changes of roads, and eventually loses much valuable potential information. To address these challenges, we propose DetectorNet enhanced by Transformer. Differs from previous studies, our model contains a Multi-view Temporal Attention module and a Dynamic Attention module, which focus on the long-distance and short-distance temporal correlation, and dynamic spatial correlation by dynamically updating the learned knowledge respectively, so as to make accurate prediction. In addition, the experimental results on two public datasets and the comparison results of four ablation experiments proves that the performance of DetectorNet is better than the eleven advanced baselines.

Traffic prediction has become increasingly hot in real-world applications. However, even though massive previous works have been conducted, traffic prediction based on the sensor is still confronted with unique challenges. In a nutshell, it is difficult for us to model both spatial dependency and temporal dependency. In this paper, we propose a novel model called GraphSANet which ensures both spatial and temporal dependencies are considered. With the usage of Temporal Self Attention, the temporal dependency could be captured perfectly and effectively, even if the problem caused by long-distance dependency could be alleviated. In the end, we conducted extensive experiments on two datasets, and vastly better prediction results prove the effectiveness of our model.

To improve the performance of large graph computing, graph partitioning has become a mandatory step in distributed graph computing frameworks. Some existing frameworks partition edges of an input graph in a streaming way. As the scale of real-world graphs grows dynamically, they need to limit the increasing communication cost and time cost in graph computing by reducing vertex replicas(each vertex can be replicated to multiple partitions). In this paper, we propose a real-time edge repartitioning algorithm for dynamic graph, which reduces the vertex replicas by reassigning edges near the new edge. We find that some edges are migrated just after being assigned, which leads to unnecessary migrations. To reduce migration cost, according to the replicas distribution of neighbors of two vertices connected by the new edge, we assign the new edge to the partition where it is most likely to be located after repartitioning. Our evaluation shows that it improves the performance of graph computing by only a small amount of migration.

With the rapid development of wireless communication technologies and mobile devices, the mobile peer to peer (MP2P) network has been emerged. Since the existing MP2P architectures have high management cost, in this paper, we propose a hierarchical MP2P architecture using clustering mobile peers. The proposed method clusters the mobile peers by considering three aspects like the maximum connection time, the minimum hop count and the number of the connected peers. The connection times between the connected peers can be determined by the location, velocity vector and communication range of the mobile peers. Since the maximum connection time of the connected peers are considered, the network topology is relatively stable. Therefore, the management cost of the network is decreased and the success rate of contents search is increased. Experiments have shown that our proposed method outperforms the existing schemes.