Abstract Spatio-temporal forecasting has wide applications across various domains, particularly in intelligent transportation systems, where it plays a crucial role. Traffic flow prediction, a typical spatio-temporal forecasting task, involves complex dependencies across both time and space dimensions. Current research predominantly relies on graph neural networks (GNNs) for modeling. However, deep GNN architectures often face the issue of over-smoothing. To address this challenge, recent studies have explored integrating residual connections or neural ordinary differential equations (ODEs) with GNNs. Nonetheless, existing graph ODE methods have limitations in initializing latent feature representations for time series data and capturing higher order spatio-temporal dependencies. Additionally, they struggle to extract multi-scale temporal dependencies. In this paper, we propose a framework called the Multiple Second-order Continuous Graph Neural Network. The framework utilizes a second-order continuous GNN, and experiments on four real-world datasets demonstrate that it outperforms mainstream baseline models, thereby confirming the effectiveness of the proposed method.

With the rapid development of 5G technology and deep learning, intelligent transportation systems (ITS) have been constantly improved and perfected. In the ITS, trajectory prediction, as a key part, requires the processing of a large amount of urban data, which places a high demand on computing resources. To reduce computational complexity and demand, this work proposes a lightweight spatial-temporal model based on convolutional neural networks which has fewer model parameters, faster prediction speed and higher prediction accuracy. This work transforms the complex trajectory into regular areas, reducing model computation while also more intuitively representing the change in trajectory. This work extracts multi-scale spatial features through spatial convolutions of different sizes to comprehensively understand the spatial variation of trajectory. Then, the temporal correlation of the trajectory is extracted through multi-layer time convolution. In addition, this paper introduces the trajectory relationship matrix(TRM) to enhance the global spatial relationship of the pedestrian trajectory and capture different spatial correlations for different areas. It analyzes the relationship between the areas where the trajectory is located by learning the historical trajectory. This work introduces extra factors such as pedestrian motion direction and speed to assist prediction, improving the model’s prediction ability and robustness. Experimental results show that the proposed MT-CNN model improves the accuracy by 22.7% compared to other models, with better performance and generalization ability, while also having excellent computational time and energy consumption.

The complexity of traffic scenarios, the spatial-temporal feature correlations pose higher challenges for traffic prediction research. Traffic spatial-temporal model is an essential method in this research field, primarily focusing on capturing the spatial-temporal features among nodes and their neighboring nodes. However, existing methods lack comprehensive consideration of directional and hierarchical features among traffic nodes. They are mostly applicable to scenarios with random uniform distribution of nodes, but not suitable for more complex small-scale aggregation distribution scenarios. Therefore, this study proposes the Tree Convolutional Network (TreeCN), a tree-based structure. The data design and model design of TreeCN focus on capturing the directional and hierarchical features among nodes. The directional and hierarchical relationships among nodes are represented by the plane tree matrix and constructed as the spatial tree matrix. The TreeCN, with a full convolution network, performs a bottom-up convolution structure on the tree matrix to complete the task of node feature capturing. In this study, TreeCN is thoroughly compared with statistical, machine learning, and deep learning methods in traffic time series prediction. The experimental results show that TreeCN not only performs well in scenarios with random uniform distribution but also exhibits outstanding effect in more complex small-scale aggregation distribution. Moreover, TreeCN adheres to the design principles of Graph Convolutional Networks (GCN) in capturing the spatial features of traffic nodes and can further capture directional and hierarchical features among them. This is expected to make TreeCN a new method to handle complex traffic scenarios and improve prediction accuracy.

Abstract Taxi flow forecast is significant for planning transportation and allocating basic transportation resources. The flow forecast in the urban adjacent area is different from the fixed-point flow forecast. Their data are more complex and diverse, which make them more challenging to forecast. This paper introduces a deep spatial–temporal forecast (DeepSTF) model for the flow forecasting of urban adjacent area, which divides the urban into grids and makes it have a graph structure. The model builds a spatial–temporal calculation block, which uses graph convolutional network to extract spatial correlation feature and uses two-layer temporal convolutional networks to extract time-dependent feature. Based on the theory of dilation convolution and causal convolution, the model overcomes the under-fitting phenomenon of other models when calculating with rapidly changing data. In order to improve the accuracy of prediction, we take weather as an implicit factor and let it participate in the feature calculation process. A comparison experiment is set between our model and the seven existing traffic flow forecast models. The experimental results prove that the model has better the capabilities of long-term traffic prediction and performs well in various evaluation indicators.

Traffic flow prediction has always been the focus of research in the field of Intelligent Transportation Systems, which is conducive to the more reasonable allocation of basic transportation resources and formulation of transportation policies. The spread of COVID-19 has seriously affected the normal order in the transportation sector. With the increase in the number of infected people and the government's anti-epidemic policy, human outgoing activities have gradually decreased, resulting in increasingly obvious discreteness and irregularities in traffic flow data. This article proposes a deep-space time traffic flow prediction model based on discrete wavelet transform (DSTM-DWT) to overcome the highly discrete and irregular nature of the new crown epidemic. First, DSTM-DWT decomposes traffic flow into discrete attributes, such as flow trend, discrete amplitude, and discrete baseline. Second, we design the spatial relationship of the transportation network as a graph and integrate the new crown pneumonia epidemic data into the characteristics of each transportation node. Then, we use the graph convolutional network to calculate the spatial correlation of each node, and the temporal convolutional network to calculate the temporal correlation of the data. In order to solve the problem of high discreteness of traffic flow data during the epidemic, this article proposes a graph memory network (GMN), which is used to convert discrete magnitudes separated by discrete wavelet transform into high-dimensional discrete features. Finally, use DWT to segment the predicted traffic data, and then perform the inverse discrete wavelet transform between the newly segmented traffic trend and discrete baseline and the discrete model predicted by GMN to obtain the final traffic flow prediction result. In simulation experiments, this work was compared with the existing advanced baselines to verify the superiority of DSTM-DWT.

Mobile edge computing (MEC) has become a research trend that solves effectively computationally intensive and latency-sensitive tasks. MEC environments in the real world are dynamic and uncertain and then the changes of the environments bring challenges to the generalization and robustness of offloading algorithms. In order to solve the above problem, we propose a meta-reinforcement learning task offloading algorithm GASTO based on Graph Neural Network and seq2seq network. Meta-learning can learn the optimal initialization parameter through several gradient descent steps and samples to adapt to new environments more quickly. The task generated in the user equipment is composed of multiple subtasks rather than a single task, and there are dependencies between the subtasks. Therefore, the task on the user equipment is modeled as a Directed Acyclic Graph (DAG). The connection relationship between the subtasks in DAG plays an important role. Drawing on the idea of message passing, Graph Neural Network is applied in DAG to extract the intrinsic correlation between subtasks in GASTO. In addition, Seq2Seq network can reduce the dimension of action space effectively, and the scheduling decisions of all subtasks can be generated simultaneously. Besides, in order to enhance the sampling efficiency of tasks and the robustness of GASTO, the priority of sampling tasks is adjusted dynamically during the training process. The experimental results of four algorithms in different environments show that the proposed algorithm GASTO can quickly adapt to the new environment.

Blind people have intelligent tools to rely on for travel with the development of navigation technology. The GPS navigation, blind track, etc., are tools that blind people often use when traveling outdoors. However, indoor navigation tools and technology for blind people are lacking. We propose an obstacle avoidance algorithm and a spatial‐temporal model of trajectory prediction for the indoor travel task of the blind. The focus of this work is that it enables the blind to accurately avoid obstacles and achieve high accuracy trajectory prediction aiming at the unique movement characteristics of the blind. We set up a variety of baselines to conduct an experimental evaluation on a dataset of blind trajectories in a multistorey shopping mall. The experimental results show the advantages of the data model and predictive model of this work.