Journal ArticleDOI
Long short-term memory neural network for traffic speed prediction using remote microwave sensor data
TLDR
A comparison with different topologies of dynamic neural networks as well as other prevailing parametric and nonparametric algorithms suggests that LSTM NN can achieve the best prediction performance in terms of both accuracy and stability.Abstract:
Neural networks have been extensively applied to short-term traffic prediction in the past years. This study proposes a novel architecture of neural networks, Long Short-Term Neural Network (LSTM NN), to capture nonlinear traffic dynamic in an effective manner. The LSTM NN can overcome the issue of back-propagated error decay through memory blocks, and thus exhibits the superior capability for time series prediction with long temporal dependency. In addition, the LSTM NN can automatically determine the optimal time lags. To validate the effectiveness of LSTM NN, travel speed data from traffic microwave detectors in Beijing are used for model training and testing. A comparison with different topologies of dynamic neural networks as well as other prevailing parametric and nonparametric algorithms suggests that LSTM NN can achieve the best prediction performance in terms of both accuracy and stability.read more
Citations
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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
Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction.
TL;DR: Wang et al. as mentioned in this paper proposed a convolutional neural network (CNN)-based method that learns traffic as images and predicts large-scale, network-wide traffic speed with a high accuracy.
Proceedings ArticleDOI
Using LSTM and GRU neural network methods for traffic flow prediction
Rui Fu,Zuo Zhang,Li Li +2 more
TL;DR: This paper uses Long Short Term Memory and Gated Recurrent Units (GRU) neural network methods to predict short-term traffic flow, and experiments demonstrate that Recurrent Neural Network (RNN) based deep learning methods such as LSTM and GRU perform better than auto regressive integrated moving average (ARIMA) model.
Posted Content
Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction
TL;DR: The CNN can train the model in a reasonable time and, thus, is suitable for large-scale transportation networks and outperforms other algorithms by an average accuracy improvement of 42.91% within an acceptable execution time.
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.
References
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Journal ArticleDOI
Long short-term memory
TL;DR: A novel, efficient, gradient based method called long short-term memory (LSTM) is introduced, which can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units.
Journal ArticleDOI
A tutorial on support vector regression
TL;DR: This tutorial gives an overview of the basic ideas underlying Support Vector (SV) machines for function estimation, and includes a summary of currently used algorithms for training SV machines, covering both the quadratic programming part and advanced methods for dealing with large datasets.
Journal ArticleDOI
A cellular automaton model for freeway traffic
Kai Nagel,Michael Schreckenberg +1 more
TL;DR: A stochastic discrete automaton model is introduced to simulate freeway traffic and shows a transition from laminar traffic flow to start-stop- waves with increasing vehicle density, as is observed in real freeway traffic.
Proceedings ArticleDOI
Learning to forget: continual prediction with LSTM
TL;DR: This work identifies a weakness of LSTM networks processing continual input streams without explicitly marked sequence ends and proposes an adaptive "forget gate" that enables an L STM cell to learn to reset itself at appropriate times, thus releasing internal resources.
Journal ArticleDOI
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.
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Modeling and Forecasting Vehicular Traffic Flow as a Seasonal ARIMA Process: Theoretical Basis and Empirical Results
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