scholarly journals GSTNet: Global Spatial-Temporal Network for Traffic Flow Prediction

2019 ◽  
Author(s):  
Shen Fang ◽  
Qi Zhang ◽  
Gaofeng Meng ◽  
Shiming Xiang ◽  
Chunhong Pan
Keyword(s):  
Traffic Flow ◽  
Public Transportation ◽  
Transportation Network ◽  
Temporal Network ◽  
Spatial Correlations ◽  
Traffic Flow Prediction ◽  
Temporal Correlations ◽  
Global Dynamic ◽  
Real World Datasets ◽  
Temporal Dependencies

Predicting traffic flow on traffic networks is a very challenging task, due to the complicated and dynamic spatial-temporal dependencies between different nodes on the network. The traffic flow renders two types of temporal dependencies, including short-term neighboring and long-term periodic dependencies. What's more, the spatial correlations over different nodes are both local and non-local. To capture the global dynamic spatial-temporal correlations, we propose a Global Spatial-Temporal Network (GSTNet), which consists of several layers of spatial-temporal blocks. Each block contains a multi-resolution temporal module and a global correlated spatial module in sequence, which can simultaneously extract the dynamic temporal dependencies and the global spatial correlations. Extensive experiments on the real world datasets verify the effectiveness and superiority of the proposed method on both the public transportation network and the road network.

scholarly journals Improved Spatio-Temporal Residual Networks for Bus Traffic Flow Prediction

2019 ◽  
Vol 9 (4) ◽  
pp. 615 ◽  
Author(s):  
Panbiao Liu ◽  
Yong Zhang ◽  
Dehui Kong ◽  
Baocai Yin
Keyword(s):  
Traffic Flow ◽  
Traffic Congestion ◽  
Road Traffic ◽  
Transportation Network ◽  
Temporal Correlation ◽  
Traffic Flow Prediction ◽  
Temporal Correlations ◽  
Building Models ◽  
Spatio Temporal ◽  
Specific Scenario

Buses, as the most commonly used public transport, play a significant role in cities. Predicting bus traffic flow cannot only build an efficient and safe transportation network but also improve the current situation of road traffic congestion, which is very important for urban development. However, bus traffic flow has complex spatial and temporal correlations, as well as specific scenario patterns compared with other modes of transportation, which is one of the biggest challenges when building models to predict bus traffic flow. In this study, we explore bus traffic flow and its specific scenario patterns, then we build improved spatio-temporal residual networks to predict bus traffic flow, which uses fully connected neural networks to capture the bus scenario patterns and improved residual networks to capture the bus traffic flow spatio-temporal correlation. Experiments on Beijing transportation smart card data demonstrate that our method achieves better results than the four baseline methods.

MS-Net: Multi-Source Spatio-Temporal Network for Traffic Flow Prediction

2021 ◽  
pp. 1-14
Author(s):  
Shen Fang ◽  
Veronique Prinet ◽  
Jianlong Chang ◽  
Michael Werman ◽  
Chunxia Zhang ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Temporal Network ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Spatio Temporal

scholarly journals Capsules TCN Network for Urban Computing and Intelligence in Urban Traffic Prediction

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Dazhou Li ◽  
Chuan Lin ◽  
Wei Gao ◽  
Zeying Chen ◽  
Zeshen Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Traffic Flow ◽  
Geographical Location ◽  
Urban Traffic ◽  
Basic Unit ◽  
Urban Computing ◽  
Convolutional Network ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Real World Datasets

Predicting urban traffic is of great importance to smart city systems and public security; however, it is a very challenging task because of several dynamic and complex factors, such as patterns of urban geographical location, weather, seasons, and holidays. To tackle these challenges, we are stimulated by the deep-learning method proposed to unlock the power of knowledge from urban computing and proposed a deep-learning model based on neural network, entitled Capsules TCN Network, to predict the traffic flow in local areas of the city at once. Capsules TCN Network employs a Capsules Network and Temporal Convolutional Network as the basic unit to learn the spatial dependence, time dependence, and external factors of traffic flow prediction. In specific, we consider some particular scenarios that require accurate traffic flow prediction (e.g., smart transportation, business circle analysis, and traffic flow assessment) and propose a GAN-based superresolution reconstruction model. Extensive experiments were conducted based on real-world datasets to demonstrate the superiority of Capsules TCN Network beyond several state-of-the-art methods. Compared with HA, ARIMA, RNN, and LSTM classic methods, respectively, the method proposed in the paper achieved better results in the experimental verification.

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