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Bus passenger flow prediction is a crucial issue of public transportation planning and management. Though spatio-temporal graph convolution has shown promising results for subway passenger flow prediction, the existing spatial modeling methods based on graph convolution will bring huge spatial memory consumption for complex bus lines and larger-scale node data. Additionally, bus passenger flow is significantly influenced by immediate traffic conditions within a short time. To tackle these challenges, a multi-scale fusion and dynamic adaptive graph bus passenger flow prediction model (MFDAG) is presented. The proposed model effectively integrates passenger flow, time, and weekly information to enhance the feature dimension of the data. Moreover, it employs a dynamic adaptive graph method to learn the relationships between different stations. Furthermore, a multi-scale fusion propagation method is proposed to represent the complex spatial dependency relation, and a multi-scale convolution propagation method is designed to learn the multi-scale temporal dependency relation. The experiments are conducted by using two passenger flow datasets, and the results are compared with other traffic prediction methods. Experimental results demonstrate that the proposed bus passenger flow prediction method based on multi-scale fusion and dynamic adaptive graph exhibits higher prediction accuracy.

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Orthogonal frequency division multiplexing (OFDM) is one of the key technologies in the physical layer of the internet of things (IoT).Pilot design and channel estimation are key issues in OFDM systems.In view of the problem of performance loss by fixed pilot pattern due to the complexity and variety of IoT communication scenarios, a pilot design and channel estimation scheme based on graph signal processing (GSP) was proposed.Firstly, the time-frequency resource block was modeled as a graph signal, and the channel estimation problem was reformulated into a sampling and reconstruction problem of the graph signal.Then, considering the influence of time-frequency fading, a weighted graph adjacency matrix was designed to construct a graph topology structure based on the time-frequency position.On this basis, the pilot position is selected based on the graph signal sampling theory, a greedy pilot pattern design algorithm based on weighted graph topology was proposed.At the same time, signal reconstruction was performed based on the graph signal reconstruction method, and a channel estimation method based on the graph smoothness constraint was proposed.Compared with the conventional scheme, simulation results show that the proposed method achieves higher channel estimation accuracy in high-speed scenarios of double selective channels, and effectively reduces pilot overhead in low-speed scenarios.

Copyright of Journal of Zhejiang University (Science Edition) is the property of Journal of Zhejiang University (Science Edition) Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

When training graph embedding via neural network, high dimension feature vector and large scale graph cause data transferring from memory to GPU to be a bottleneck. Aimed to solve this problem, this paper proposed graph partition based graph representation learning. This method splitted graph nodes into blocks according to their degree. It stored several node feature matrices in buffer pool on GPU. Every epoch, it trained representation during several blocks which fitted into buffer pool to reduce the data transferred from memory to GPU. Based on block split, this method used blocked based sampling algorithm, cached block feature matrix in GPU buffer pool to reduce memory read and built hierarchical negative sampling table, which could sample nodes in const time complex. Compared to related work on several real world datasets, this method achieves competitive accuracy at downstream machine learning task and 2 ~ 7 times speedup on training process. The experiments show that graph representation learning based on partition can train model efficiently and generate accurate embedding vectors. In future work, it is worth to prove the deviation between partition based method and original method in theory. [ABSTRACT FROM AUTHOR]