Improved UGRNN for short-term traffic flow prediction with multi-feature sequence inputs
Explore this paper's citation graph
- Type
- article
- Published
- 2018-01-01
- Cited by
- 9
- References
- 13
- OpenAlex
- https://openalex.org/W2798466145
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:5049125
Keywords
Autoregressive integrated moving average, Computer science, Traffic flow (computer networking), Sequence (biology), Feature (linguistics)
References
- Predicting Spatiotemporal Traffic Flow Based on Support Vector Regression and Bayesian Classifier
- Modeling and Forecasting Vehicular Traffic Flow as a Seasonal ARIMA Process: Theoretical Basis and Empirical Results
- Traffic Flow Prediction With Big Data: A Deep Learning Approach
- TRAFFIC FLOW FORECASTING: COMPARISON OF MODELING APPROACHES
- Optimized and meta-optimized neural networks for short-term traffic flow prediction: A genetic approach
- Short-term freeway traffic flow prediction : Bayesian combined neural network approach
- Comparison of parametric and nonparametric models for traffic flow forecasting
- Constrained Kalman filter combined predictor for short-term traffic flow
- Predicting Short-Term Traffic Flow by Long Short-Term Memory Recurrent Neural Network
- ARIMA model for traffic flow prediction based on wavelet analysis
- Capacity and Trainability in Recurrent Neural Networks
- Using LSTM and GRU neural network methods for traffic flow prediction
- Traffic flow prediction with Long Short-Term Memory Networks (LSTMs)
Cited by
- Neuro-Fuzzy Modeling of Data Singular Spectrum Decomposition and Traffic Flow Prediction
- Deep Architectures for Crowd Flow Prediction
- Urban Traffic Data Imputation With Detrending and Tensor Decomposition
- Short-Term Traffic Flow Prediction Based on Sparse Regression and Spatio-Temporal Data Fusion
- Evaluation of Sentiment Analysis via Word Embedding and RNN Variants for Amazon Online Reviews
- A New Spatio-Temporal Graph Convolution Network For Traffic Prediction
- Spatial-Temporal Attention Graph Convolution Network on Edge Cloud for Traffic Flow Prediction
- Ride the Flow: Dynamic Tensor and Adaptive Modeling for Short-Term Traffic Prediction
- Short-term traffic forecasting model: prevailing trends and guidelines
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