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Parallel Architecture of Convolutional Bi-Directional LSTM Neural Networks for Network-Wide Metro Ridership Prediction
16 references·1 citing·29 second hop
Parallel Architecture of Convolutional Bi-Directional LSTM Neural Networks for Network-Wide Metro Ridership Prediction (2018)Parallel Architecture of Convolutional Bi…Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction (2018)Deep Multi-View Spatial-Tempo…The Simpler The Better (2017)The Simpler The BetterSpatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networks (2017)Spatiotemporal Recurrent Conv…Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction (2017)Learning Traffic as Images: A…Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting (2017)Diffusion Convolutional Recur…Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction (2017)Deep Spatio-Temporal Residual…Deep Learning: A Generic Approach for Extreme Condition Traffic Forecasting (2017)Deep Learning: A Generic Appr…Understanding commuting patterns using transit smart card data (2016)Understanding commuting patte…Long short-term memory neural network for traffic speed prediction using remote microwave sensor data (2015)Long short-term memory neural…Land use characteristics of subway catchment areas and their influence on subway ridership in Seoul (2015)Land use characteristics of s…A novel wavelet-SVM short-time passenger flow prediction in Beijing subway system (2015)A novel wavelet-SVM short-tim…Traffic Flow Prediction With Big Data: A Deep Learning Approach (2014)Traffic Flow Prediction With …Application of geographically weighted regression to the direct forecasting of transit ridership at station-level (2012)Application of geographically…Forecasting the short-term metro passenger flow with empirical mode decomposition and neural networks (2011)Forecasting the short-term me…Statistical methods versus neural networks in transportation research: Differences, similarities and some insights (2010)Statistical methods versus ne…Travel-Time Prediction With Support Vector Regression (2004)Travel-Time Prediction With S…Physical-Virtual Collaboration Modeling for Intra- and Inter-Station Metro Ridership Prediction (2020)Physical-Virtual Collaboratio…Combining kohonen maps with arima time series models to forecast traffic flow (1996)Combining neural network model with seasonal time series ARIMA model (2002)Dynamic Bus Arrival Time Prediction with Artificial Neural Networks (2002)LSTM network: a deep learning approach for short‐term traffic forecast (2017)Dynamic prediction of traffic volume through Kalman filtering theory (1984)Modeling and Forecasting Vehicular Traffic Flow as a Seasonal ARIMA Process: Theoretical Basis and Empirical Results (2003)Short-term traffic forecasting: Where we are and where we’re going (2014)Using LSTM and GRU neural network methods for traffic flow prediction (2016)GMAN: A Graph Multi-Attention Network for Traffic Prediction (2020)Data-intensive applications, challenges, techniques and technologies: A survey on Big Data (2014)T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction (2019)Support Vector Machine Regression for Volatile Stock Market Prediction (2002)Travel time data collection handbook (2010)Book Review: Time Series Analysis: Forecasting and Control (1977)Time Series Analysis: Forecasting and Control (1977)Time series forecasting using a hybrid ARIMA and neural network model (2003)Forecasting carbon price using empirical mode decomposition and evolutionary least squares support vector regression (2017)DeepPF: A deep learning based architecture for metro passenger flow prediction (2019)Prediction of human emergency behavior and their mobility following large-scale disaster (2014)Methodologies for Cross-Domain Data Fusion: An Overview (2015)Traffic prediction in a bike-sharing system (2015)Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting (2018)Graph WaveNet for Deep Spatial-Temporal Graph Modeling (2019)A Computer Movie Simulating Urban Growth in the Detroit Region (1970)Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting (2019)Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting (2019)Short-term forecasting of passenger demand under on-demand ride services: A spatio-temporal deep learning approach (2017)Deep Bidirectional and Unidirectional LSTM Recurrent Neural Network for Network-wide Traffic Speed Prediction (2018)Stacked bidirectional and unidirectional LSTM recurrent neural network for forecasting network-wide traffic state with missing values (2020)
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References

  • 2017Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting3,639 cited
  • 2014Traffic Flow Prediction With Big Data: A Deep Learning Approach3,073 cited
  • 2017Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction2,450 cited
  • 2015Long short-term memory neural network for traffic speed prediction using remote microwave sensor data2,259 cited
  • 2004Travel-Time Prediction With Support Vector Regression1,480 cited
  • 2017Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction1,442 cited
  • 2018Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction1,248 cited
  • 2010Statistical methods versus neural networks in transportation research: Differences, similarities and some insights845 cited
  • 2017Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networks623 cited
  • 2011Forecasting the short-term metro passenger flow with empirical mode decomposition and neural networks475 cited
  • 2017Deep Learning: A Generic Approach for Extreme Condition Traffic Forecasting427 cited
  • 2015A novel wavelet-SVM short-time passenger flow prediction in Beijing subway system384 cited
  • 2012Application of geographically weighted regression to the direct forecasting of transit ridership at station-level377 cited

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2016
Understanding commuting patterns using transit smart card data
352 cited
  • 2015Land use characteristics of subway catchment areas and their influence on subway ridership in Seoul326 cited
  • 2017The Simpler The Better275 cited
  • Cited by

    • 2020Physical-Virtual Collaboration Modeling for Intra- and Inter-Station Metro Ridership Prediction199 cited