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- 저자 : ( Xiufang Sun Jianbo Li ) (검색결과 2 건)
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Traffic Flow Prediction Model Based on Spatio-Temporal Dilated Graph Convolution
( Xiufang Sun Jianbo Li ),( Zhiqiang Lv ),( Chuanhao Dong ) 한국인터넷정보학회 2020 KSII Transactions on Internet and Information Syst Vol.14 No.9
With the increase of motor vehicles and tourism demand, some traffic problems gradually appear, such as traffic congestion, safety accidents and insufficient allocation of traffic resources. Facing these challenges, a model of Spatio-Temporal Dilated Convolutional Network (STDGCN) is proposed for assistance of extracting highly nonlinear and complex characteristics to accurately predict the future traffic flow. In particular, we model the traffic as undirected graphs, on which graph convolutions are built to extract spatial feature informations. Furthermore, a dilated convolution is deployed into graph convolution for capturing multi-scale contextual messages. The proposed STDGCN integrates the dilated convolution into the graph convolution, which realizes the extraction of the spatial and temporal characteristics of traffic flow data, as well as features of road occupancy. To observe the performance of the proposed model, we compare with it with four rivals. We also employ four indicators for evaluation. The experimental results show STDGCN’s effectiveness. The prediction accuracy is improved by 17% in comparison with the traditional prediction methods on various real-world traffic datasets.
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Next Location Prediction with a Graph Convolutional Network Based on a Seq2seq Framework
( Jianwei Chen ),( Jianbo Li ),( Manzoor Ahmed ),( Junjie Pang ),( Minchao Lu ),( Xiufang Sun ) 한국인터넷정보학회 2020 KSII Transactions on Internet and Information Syst Vol.14 No.5
Predicting human mobility has always been an important task in Location-based Social Network. Previous efforts fail to capture spatial dependence effectively, mainly reflected in weakening the location topology information. In this paper, we propose a neural network-based method which can capture spatial-temporal dependence to predict the next location of a person. Specifically, we involve a graph convolutional network (GCN) based on a seq2seq framework to capture the location topology information and temporal dependence, respectively. The encoder of the seq2seq framework first generates the hidden state and cell state of the historical trajectories. The GCN is then used to generate graph embeddings of the location topology graph. Finally, we predict future trajectories by aggregated temporal dependence and graph embeddings in the decoder. For evaluation, we leverage two real-world datasets, Foursquare and Gowalla. The experimental results demonstrate that our model has a better performance than the compared models.
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