Journal ArticleDOI
A bayesian network approach to traffic flow forecasting
TLDR
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.Abstract:
A new approach based on Bayesian networks for traffic flow forecasting is proposed. In this paper, traffic flows among adjacent road links in a transportation network are modeled as a Bayesian network. The joint probability distribution between the cause nodes (data utilized for forecasting) and the effect node (data to be forecasted) in a constructed Bayesian network is described as a Gaussian mixture model (GMM) whose parameters are estimated via the competitive expectation maximization (CEM) algorithm. Finally, traffic flow forecasting is performed under the criterion of minimum mean square error (mmse). The approach departs from many existing traffic flow forecasting models in that it explicitly includes information from adjacent road links to analyze the trends of the current link statistically. Furthermore, it also encompasses the issue of traffic flow forecasting when incomplete data exist. 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 dataread more
Citations
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Journal ArticleDOI
Traffic Flow Prediction With Big Data: A Deep Learning Approach
TL;DR: A novel deep-learning-based traffic flow prediction method is proposed, which considers the spatial and temporal correlations inherently and is applied for the first time that a deep architecture model is applied using autoencoders as building blocks to represent traffic flow features for prediction.
Journal ArticleDOI
LSTM network: a deep learning approach for short-term traffic forecast
TL;DR: A novel traffic forecast model based on long short-term memory (LSTM) network is proposed, which considers temporal-spatial correlation in traffic system via a two-dimensional network which is composed of many memory units.
Journal ArticleDOI
T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction
TL;DR: In this article, a novel neural network-based traffic forecasting method, the temporal graph convolutional network (T-GCN) model, which is combined with the graph convolutionsal network and the gated recurrent unit (GRU), is proposed.
Journal ArticleDOI
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.
Journal ArticleDOI
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.
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