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Improved Deep Hybrid Networks for Urban Traffic Flow Prediction Using Trajectory Data
3 references·4 citing·33 second hop
Improved Deep Hybrid Networks for Urban Traffic Flow Prediction Using Trajectory Data (2018)Improved Deep Hybrid Networks for Urban T…Spatio-Temporal Analysis of Passenger Travel Patterns in Massive Smart Card Data (2017)Spatio-Temporal Analysis of P…End-to-End Multimodal Emotion Recognition Using Deep Neural Networks (2017)End-to-End Multimodal Emotion…Citywide Traffic Volume Estimation Using Trajectory Data (2016)Citywide Traffic Volume Estim…DDP-GCN: Multi-graph convolutional network for spatiotemporal traffic forecasting (2021)DDP-GCN: Multi-graph convolut…Urban flow prediction from spatiotemporal data using machine learning: A survey (2020)Urban flow prediction from sp…A graph CNN-LSTM neural network for short and long-term traffic forecasting based on trajectory data (2020)A graph CNN-LSTM neural netwo…Deep learning models for traffic flow prediction in autonomous vehicles: A review, solutions, and challenges (2019)Deep learning models for traf…A Compressive Sensing Approach to Urban Traffic Estimation with Probe Vehicles (2012)Urban link travel time estimation using large-scale taxi data with partial information (2013)Methodologies for Cross-Domain Data Fusion: An Overview (2015)Traffic Flow Prediction Based on Deep Learning in Internet of Vehicles (2020)Learning Dynamics and Heterogeneity of Spatial-Temporal Graph Data for Traffic Forecasting (2021)Smart card data use in public transit: A literature review (2011)Estimation of a disaggregate multimodal public transport Origin–Destination matrix from passive smartcard data from Santiago, Chile (2012)Mining smart card data for transit riders’ travel patterns (2013)Modeling real-time human mobility based on mobile phone and transportation data fusion (2018)Smart Card Data Mining of Public Transport Destination: A Literature Review (2018)Understanding Private Car Aggregation Effect via Spatio-Temporal Analysis of Trajectory Data (2021)A Concordance Correlation Coefficient to Evaluate Reproducibility (1989)Vocal communication of emotion: A review of research paradigms (2003)The Geneva Minimalistic Acoustic Parameter Set (GeMAPS) for Voice Research and Affective Computing (2015)Speech Emotion Recognition Using Deep Learning Techniques: A Review (2019)Speech emotion recognition: Emotional models, databases, features, preprocessing methods, supporting modalities, and classifiers (2019)A systematic review on affective computing: emotion models, databases, and recent advances (2022)Introduction to Time Series and Forecasting (2002)Traffic Flow Prediction With Big Data: A Deep Learning Approach (2014)Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting (2018)Traffic prediction using artificial intelligence: Review of recent advances and emerging opportunities (2022)Automatic Time Series Forecasting: The forecast Package for R (2008)Forecasting principles and practice (2018)T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction (2019)How to Build a Graph-Based Deep Learning Architecture in Traffic Domain: A Survey (2020)A Deep Gravity model for mobility flows generation (2021)A Computer Movie Simulating Urban Growth in the Detroit Region (1970)A Three-Stage Iterative Procedure for Space-Time Modeling (1980)Spatio-temporal autocorrelation of road network data (2011)Deep Learning on Traffic Prediction: Methods, Analysis, and Future Directions (2022)Graph neural network for traffic forecasting: A survey (2022)Deep Learning Architecture for Short-Term Passenger Flow Forecasting in Urban Rail Transit (2020)A Review of Traffic Congestion Prediction Using Artificial Intelligence (2021)
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References

  • 2017End-to-End Multimodal Emotion Recognition Using Deep Neural Networks749 cited
  • 2017Spatio-Temporal Analysis of Passenger Travel Patterns in Massive Smart Card Data192 cited
  • 2016Citywide Traffic Volume Estimation Using Trajectory Data164 cited

Cited by

  • 2020A graph CNN-LSTM neural network for short and long-term traffic forecasting based on trajectory data389 cited
  • 2020Urban flow prediction from spatiotemporal data using machine learning: A survey311 cited
  • 2019Deep learning models for traffic flow prediction in autonomous vehicles: A review, solutions, and challenges306 cited
  • 2021DDP-GCN: Multi-graph convolutional network for spatiotemporal traffic forecasting155 cited

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