Traffic prediction is the cornerstone of intelligent transportation system. Accurate traffic forecasting is essential for the applications of smart cities, i.e., intelligent traffic management and urban planning. Although various methods are proposed for spatio-temporal modeling, they ignore the dynamic characteristics of correlations among locations on road network. Meanwhile, most Recurrent Neural Network (RNN) based works are not efficient enough due to their recurrent operations. Additionally, there is a severe lack of fair comparison among different methods on the same datasets. To address the above challenges, in this paper, we propose a novel traffic prediction framework, named Dynamic Graph Convolutional Recurrent Network (DGCRN). In DGCRN, hyper-networks are designed to leverage and extract dynamic characteristics from node attributes, while the parameters of dynamic filters are generated at each time step. We filter the node embeddings and then use them to generate dynamic graph, which is integrated with pre-defined static graph. As far as we know, we are first to employ a generation method to model fine topology of dynamic graph at each time step. Further, to enhance efficiency and performance, we employ a training strategy for DGCRN by restricting the iteration number of decoder during forward and backward propagation. Finally, a reproducible standardized benchmark and a brand new representative traffic dataset are opened for fair comparison and further research. Extensive experiments on three datasets demonstrate that our model outperforms 15 baselines consistently. Source codes are available at https://github.com/tsinghua-fib-lab/Traffic-Benchmark.
Traffic prediction is the cornerstone of intelligent transportation system. Accurate traffic forecasting is essential for the applications of smart cities, i.e., intelligent traffic management and urban planning. Although various methods are proposed for spatio-temporal modeling, they ignore the dynamic characteristics of correlations among locations on road network. Meanwhile, most Recurrent Neural Network (RNN) based works are not efficient enough due to their recurrent operations. Additionally, there is a severe lack of fair comparison among different methods on the same datasets. To address the above challenges, in this paper, we propose a novel traffic prediction framework, named Dynamic Graph Convolutional Recurrent Network (DGCRN). In DGCRN, hyper-networks are designed to leverage and extract dynamic characteristics from node attributes, while the parameters of dynamic filters are generated at each time step. We filter the node embeddings and then use them to generate dynamic graph, which is integrated with pre-defined static graph. As far as we know, we are first to employ a generation method to model fine topology of dynamic graph at each time step. Further, to enhance efficiency and performance, we employ a training strategy for DGCRN by restricting the iteration number of decoder during forward and backward propagation. Finally, a reproducible standardized benchmark and a brand new representative traffic dataset are opened for fair comparison and further research. Extensive experiments on three datasets demonstrate that our model outperforms 15 baselines consistently. Source codes are available at https://github.com/tsinghua-fib-lab/Traffic-Benchmark.
As the development of deep neural networks, 3D object recognition is becoming increasingly popular in the computer vision community. Many multi-view-based methods are proposed to improve the category recognition accuracy. These approaches mainly rely on multi-view images that are rendered with the whole circumference. In real-world applications, however, 3D objects are mostly observed from partial viewpoints in a less range. Therefore, we propose a multi-view-based 3D convolutional neural network that takes only part of contiguous multi-view images as input and can still maintain high accuracy. Moreover, our model takes these view images as a joint variable to better learn the spatially correlated features using 3D convolution and 3D max-pooling layers. The experimental results on ModelNet10 and ModelNet40 datasets show that our MV-C3D technique can achieve outstanding performance with multi-view images that are captured from partial angles with less range. The results on 3D-rotated real image dataset MIRO further demonstrate that MV-C3D is more adaptable in real-world scenarios. The classification accuracy can be further improved with the increasing number of view images. INDEX TERMS 3D object classification, multi-view, convolutional neural network, deep learning.
It is essential to predict crowd flow precisely in a city, which is practically partitioned into irregular regions based on road networks and functionality. However, prior works mainly focus on grid-based crowd flow prediction, where a city is divided into many regular grids. Although Convolutional Neural Netwok (CNN) is powerful to capture spatial dependence from grid-based Euclidean data, it fails to tackle non-Euclidean data, which reflect the correlations among irregular regions. Besides, prior works fail to jointly capture the hierarchical spatio-temporal dependence from both regular and irregular regions. Finally, the correlations among regions are time-varying and functionality-related. However, the combination of dynamic and semantic attributes of regions are ignored by related works. To address the above challenges, in this paper, we propose a novel model to tackle the flow prediction task for irregular regions. First, we employ CNN and Graph Neural Network (GNN) to capture micro and macro spatial dependence among grid-based regions and irregular regions, respectively. Further, we think highly of the dynamic inter-region correlations and propose a location-aware and time-aware graph attention mechanism named Semantic Graph Attention Network (Semantic-GAT), based on dynamic node attribute embedding and multi-view graph reconstruction. Extensive experimental results based on two real-life datasets demonstrate that our model outperforms 10 baselines by reducing the prediction error around 8%.
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