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Review of Data Fusion Methods for Real-Time and Multi-Sensor Traffic Flow Analysis
14 references·0 citing·55 second hopsampled
Review of Data Fusion Methods for Real-Time and Multi-Sensor Traffic Flow Analysis (2021)Review of Data Fusion Methods for Real-Ti…Spatial temporal incidence dynamic graph neural networks for traffic flow forecasting (2020)Spatial temporal incidence dy…A deep-learning model for urban traffic flow prediction with traffic events mined from twitter (2020)A deep-learning model for urb…An Improved Bayesian Combination Model for Short-Term Traffic Prediction With Deep Learning (2019)An Improved Bayesian Combinat…Multi-source heterogeneous data fusion (2018)Multi-source heterogeneous da…Towards unravelling the relationship between on-body, environmental and emotion data using sensor information fusion approach (2017)Towards unravelling the relat…Deep learning networks for stock market analysis and prediction: Methodology, data representations, and case studies (2017)Deep learning networks for st…Multimodal Data Fusion for Big Events (2016)Multimodal Data Fusion for Bi…k-Nearest Neighbor Model for Multiple-Time-Step Prediction of Short-Term Traffic Condition (2016)k-Nearest Neighbor Model for …Improving Traffic Flow Prediction With Weather Information in Connected Cars: A Deep Learning Approach (2016)Improving Traffic Flow Predic…A new metric of absolute percentage error for intermittent demand forecasts (2016)A new metric of absolute perc…Social big data: Recent achievements and new challenges (2015)Social big data: Recent achie…Methodologies for Cross-Domain Data Fusion: An Overview (2015)Methodologies for Cross-Domai…A spatiotemporal correlative k-nearest neighbor model for short-term traffic multistep forecasting (2015)A spatiotemporal correlative …Behavioural data mining of transit smart card data: A data fusion approach (2014)Behavioural data mining of tr…Investigating Causal Relations by Econometric Models and Cross-spectral Methods (1969)Data fusion (2009)Discovering regions of different functions in a city using human mobility and POIs (2012)Machine Learning With Big Data: Challenges and Approaches (2017)Predicting citywide crowd flows using deep spatio-temporal residual networks (2018)Observing the rhythms of daily life: A six-week travel diary (2002)Smart card data use in public transit: A literature review (2011)Mining smart card data for transit riders’ travel patterns (2013)Clustering Smart Card Data for Urban Mobility Analysis (2016)Understanding commuting patterns using transit smart card data (2016)Public transport for smart cities: Recent innovations and future challenges (2022)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)Long short-term memory neural network for traffic speed prediction using remote microwave sensor data (2015)Road Traffic Forecasting: Recent Advances and New Challenges (2018)A graph CNN-LSTM neural network for short and long-term traffic forecasting based on trajectory data (2020)A Survey on Modern Deep Neural Network for Traffic Prediction: Trends, Methods and Challenges (2020)Dynamic prediction of traffic volume through Kalman filtering theory (1984)Traffic Flow Forecasting: Comparison of Modeling Approaches (1997)Comparison of parametric and nonparametric models for traffic flow forecasting (2002)Spatiotemporal traffic forecasting: review and proposed directions (2018)Evaluation of statistical and machine learning models for time series prediction: Identifying the state-of-the-art and the best conditions for the use of each model (2019)Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems (2020)Users of the world, unite! 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(1985)Hierarchical structure in financial markets (1999)Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average? (2007)CNNpred: CNN-based stock market prediction using a diverse set of variables (2019)Applications of deep learning in stock market prediction: Recent progress (2021)Generación de series de tiempo financieras sintéticas para "data augmentation" usando redes neuronales generativas adversarias (GAN) (2021)Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting (2017)Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting (2018)Exploiting dynamic spatio-temporal correlations for citywide traffic flow prediction using attention based neural networks (2021)Traffic prediction using artificial intelligence: Review of recent advances and emerging opportunities (2022)Using support vector regression and K-nearest neighbors for short-term traffic flow prediction based on maximal information coefficient (2022)Designing a neural network for forecasting financial and economic time series (1996)Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction (2017)Traffic flow prediction models – A review of deep learning techniques (2021)Research on the Nonlinear Random Matrix Model for Urban Information Model and Modern Urban Governance (2022)
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References

  • 2016A new metric of absolute percentage error for intermittent demand forecasts1,183 cited
  • 2017Deep learning networks for stock market analysis and prediction: Methodology, data representations, and case studies854 cited
  • 2015Social big data: Recent achievements and new challenges849 cited
  • 2015A spatiotemporal correlative k-nearest neighbor model for short-term traffic multistep forecasting471 cited
  • 2015Methodologies for Cross-Domain Data Fusion: An Overview454 cited
  • 2016Improving Traffic Flow Prediction With Weather Information in Connected Cars: A Deep Learning Approach376 cited
  • 2020Spatial temporal incidence dynamic graph neural networks for traffic flow forecasting347 cited
  • 2014Behavioural data mining of transit smart card data: A data fusion approach283 cited
  • 2016k-Nearest Neighbor Model for Multiple-Time-Step Prediction of Short-Term Traffic Condition242 cited
  • 2020A deep-learning model for urban traffic flow prediction with traffic events mined from twitter209 cited
  • 2019An Improved Bayesian Combination Model for Short-Term Traffic Prediction With Deep Learning160 cited
  • 2018Multi-source heterogeneous data fusion126 cited
  • 2017Towards unravelling the relationship between on-body, environmental and emotion data using sensor information fusion approach118 cited
  • 2016Multimodal Data Fusion for Big Events

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