scholarly journals Improving Urban Traffic Speed Prediction Using Data Source Fusion and Deep Learning

2019 ◽  
Author(s):  
Aniekan Essien ◽  
Ilias Petrounias ◽  
Pedro Sampaio ◽  
Sandra Sampaio
Keyword(s):  
Deep Learning ◽  
Urban Traffic ◽  
Speed Prediction ◽  
Data Source ◽  
Traffic Speed ◽  
Using Data

scholarly journals A Key Path-Based Deep Learning Approach for Urban Traffic Speed Prediction

2021 ◽  
Vol 1972 (1) ◽  
pp. 012095
Author(s):  
Wentian Chen ◽  
Jie Fang ◽  
Zhijia Liu ◽  
Mengyun Xu
Keyword(s):  
Deep Learning ◽  
Urban Traffic ◽  
Learning Approach ◽  
Speed Prediction ◽  
Traffic Speed

scholarly journals Global Spatial-Temporal Graph Convolutional Network for Urban Traffic Speed Prediction

2020 ◽  
Vol 10 (4) ◽  
pp. 1509 ◽  
Author(s):  
Liang Ge ◽  
Siyu Li ◽  
Yaqian Wang ◽  
Feng Chang ◽  
Kunyan Wu
Keyword(s):  
Weather Conditions ◽  
Urban Traffic ◽  
Traffic Data ◽  
Convolutional Network ◽  
Temporal Dimension ◽  
Temporal Correlations ◽  
Speed Prediction ◽  
External Component ◽  
Temporal Graph ◽  
Traffic Speed

Traffic speed prediction plays a significant role in the intelligent traffic system (ITS). However, due to the complex spatial-temporal correlations of traffic data, it is very challenging to predict traffic speed timely and accurately. The traffic speed renders not only short-term neighboring and multiple long-term periodic dependencies in the temporal dimension but also local and global dependencies in the spatial dimension. To address this problem, we propose a novel deep-learning-based model, Global Spatial-Temporal Graph Convolutional Network (GSTGCN), for urban traffic speed prediction. The model consists of three spatial-temporal components with the same structure and an external component. The three spatial-temporal components are used to model the recent, daily-periodic, and weekly-periodic spatial-temporal correlations of the traffic data, respectively. More specifically, each spatial-temporal component consists of a dynamic temporal module and a global correlated spatial module. The former contains multiple residual blocks which are stacked by dilated casual convolutions, while the latter contains a localized graph convolution and a global correlated mechanism. The external component is used to extract the effect of external factors, such as holidays and weather conditions, on the traffic speed. Experimental results on two real-world traffic datasets have demonstrated that the proposed GSTGCN outperforms the state-of-the-art baselines.

scholarly journals Truck Traffic Speed Prediction Under Non-Recurrent Congestion: Based on Optimized Deep Learning Algorithms and GPS Data

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 9116-9127 ◽  
Author(s):  
Jiandong Zhao ◽  
Yuan Gao ◽  
Zhenzhen Yang ◽  
Jiangtao Li ◽  
Yingzi Feng ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Algorithms ◽  
Gps Data ◽  
Truck Traffic ◽  
Speed Prediction ◽  
Traffic Speed

Rainfall-integrated traffic speed prediction using deep learning method

2017 ◽  
Vol 11 (9) ◽  
pp. 531-536 ◽  
Author(s):  
Yuhan Jia ◽  
Jianping Wu ◽  
Moshe Ben-Akiva ◽  
Ravi Seshadri ◽  
Yiman Du
Keyword(s):  
Deep Learning ◽  
Learning Method ◽  
Speed Prediction ◽  
Traffic Speed
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