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RISS 인기검색어
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- 저자 : ( Chuanhao Dong ) (검색결과 2 건)
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DeepPTP: A Deep Pedestrian Trajectory Prediction Model for Traffic Intersection
( Zhiqiang Lv ),( Jianbo Li ),( Chuanhao Dong ),( Yue Wang ),( Haoran Li ),( Zhihao Xu ) 한국인터넷정보학회 2021 KSII Transactions on Internet and Information Syst Vol.15 No.7
Compared with vehicle trajectories, pedestrian trajectories have stronger degrees of freedom and complexity, which poses a higher challenge to trajectory prediction tasks. This paper designs a mode to divide the trajectory of pedestrians at a traffic intersection, which converts the trajectory regression problem into a trajectory classification problem. This paper builds a deep model for pedestrian trajectory prediction at intersections for the task of pedestrian short-term trajectory prediction. The model calculates the spatial correlation and temporal dependence of the trajectory. More importantly, it captures the interactive features among pedestrians through the Attention mechanism. In order to improve the training speed, the model is composed of pure convolutional networks. This design overcomes the single-step calculation mode of the traditional recurrent neural network. The experiment uses Vulnerable Road Users trajectory dataset for related modeling and evaluation work. Compared with the existing models of pedestrian trajectory prediction, the model proposed in this paper has advantages in terms of evaluation indicators, training speed and the number of model parameters.
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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.
ScienceON
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