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.

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.

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.