Attention-based spatio-temporal graph convolutional network considering external factors for multi-step traffic flow prediction
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TL;DR: Zhang et al. as mentioned in this paper proposed an Attention Based Spatio-Temporal Graph Convolutional Network considering External Factors (ABSTGCN-EF) for multi-step traffic flow prediction.
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About: This article is published in Digital Communications and Networks. The article was published on 01 Jun 2022. and is currently open access. The article focuses on the topics: Computer science & Correlation.
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Multi-featured spatial-temporal and dynamic multi-graph convolutional network for metro passenger flow prediction
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TCP-BAST: A novel approach to traffic congestion prediction with bilateral alternation on spatiality and temporality
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References
Modeling and Forecasting Vehicular Traffic Flow as a Seasonal ARIMA Process: Theoretical Basis and Empirical Results
Billy M. Williams,Lester A Hoel +1 more
TL;DR: The theoretical basis for modeling univariate traffic condition data streams as seasonal autoregressive integrated moving average processes as well as empirical results using actual intelligent transportation system data are presented and found to be consistent with the theoretical hypothesis.
Data-Driven Intelligent Transportation Systems: A Survey
TL;DR: A survey on the development of D2ITS is provided, discussing the functionality of its key components and some deployment issues associated with D2 ITS Future research directions for the developed system are presented.
1.8K
Deep Architecture for Traffic Flow Prediction: Deep Belief Networks With Multitask Learning
TL;DR: It is presented that MTL can improve the generalization performance of shared tasks and a grouping method based on the weights in the top layer to make MTL more effective is proposed to take full advantage of weight sharing in the deep architecture.
1.2K
Deep learning for short-term traffic flow prediction
Nicholas G. Polson,Vadim Sokolov +1 more
TL;DR: A deep learning model is developed that combines a linear model that is fitted using l 1 regularization and a sequence of tanh layers to predict traffic flows and identifies spatio-temporal relations among predictors and other layers model nonlinear relations.
986
A bayesian network approach to traffic flow forecasting
TL;DR: Comprehensive experiments on urban vehicular traffic flow data of Beijing and comparisons with several other methods show that the Bayesian network is a very promising and effective approach for traffic flow modeling and forecasting, both for complete data and incomplete data.
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