A bayesian network approach to traffic flow forecasting

By Shiliang Sun, Changshui Zhang, Guoqiang Yu. IEEE transactions on intelligent transportation systems (Print), 2006

723 citations recorded by the source corpus.

Subfields: Building and Construction, Transportation, Signal Processing

View Paper: https://www.semanticscholar.org/paper/dd945f37dd0e2cf5443605ad4047cf9ad774eb1e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Building and Construction

1. Yinhai Wang
2. Weibin Zhang
3. Yinghao Yu
4. M. Hua
5. Ling Zhao
6. Feiyue Wang
7. Jingmeng Liu
8. Zheng Zhao

In Transportation

1. Yinhai Wang
2. Yinghao Yu
3. M. Hua
4. Weibin Zhang
5. Ling Zhao
6. Feiyue Wang
7. Jingmeng Liu
8. Zheng Zhao

In Signal Processing

1. M. Hua
2. Ling Zhao
3. Vadim O. Sokolov
4. Chunhong Pan
5. Tao Lin
6. Shiming Xiang
7. Nicholas G. Polson
8. Haifeng Li

Citations in the corpus

• A3T-GCN: Attention Temporal Graph Convolutional Network for Traffic Forecasting (2020, methodology): “Common non-parametric models include k-nearest [2], support vector regression (SVR) [11, 30], fuzzy logic [34], Bayesian network[28], and neural network models.”
• A Hybrid CNN-LSTM Model for Forecasting Particulate Matter (PM2.5) (2020, methodology): “There are many mature time series prediction methods, including ARMA [10], ARIMA [10], SARIMA [11], SVR [12], BP neural network [13], Bayesian network [14] and so on.”
• Deep learning for short-term traffic flow prediction (2016, methodology): “The fused lasso Tibshirani and Taylor [2011] and `1 trend filtering Kim et al.”
• Spatio-Temporal Graph Structure Learning for Traffic Forecasting (2020, methodology): “Subsequently, many variant of ARIMA and nonparametric methods were applied on this task, including KARIMA (Van Der Voort, Dougherty, and Watson 1996), ARIMAX (Williams 2001), k -NN (Davis and Nihan 1991), Bayesian network (Sun, Zhang, and Yu 2006), and so on.”
• A novel wavelet-SVM short-time passenger flow prediction in Beijing subway system (2015, methodology): “The traffic flows among adjacent road links in a transportation network are modeled as a Bayesian network, and then the joint probability distribution between the cause nodes (data utilized for forecasting) and the effect node (data to be forecasted) in a constructed Bayesian network is described as”
• LSTM network: a deep learning approach for short-term traffic forecast (2017, background): “…this problem, researchers also paid much attention to non-parametric approaches in the traffic flow forecasting field, such as non-parametric regression [17], neural network prediction [18], support vector machine (SVM) [19], Kaman filtering [20, 21] and the combination of these algorithms [22–27].”
• T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction (2018, methodology): “model [5], [6], the Kalman filtering model [7], the support vector regression machine model [8], [9], the k-nearest neighbor model [10], the Bayesian model [11], and partial neural network model [12], [13].”
• An Aggregation Approach to Short-Term Traffic Flow Prediction (2009, background): “series analysis [6], [7], Bayesian networks [8], neural networks (NNs) [9]–[12], fuzzy NNs [13], [14], nonparametric regression”
• Short-term traffic flow prediction based on spatio-temporal analysis and CNN deep learning (2019, methodology): “Statistical nonparametric algorithms applied to traffic prediction also include Bayesian models (Sun, Zhang, and Yu 2006; Zhang et al. 2017, 2018) and K-nearest neighbor (KNN) models (Zhang et al. 2013).”
• Traffic Flow Prediction With Big Data: A Deep Learning Approach (2015, methodology): “A Bayesian network approach was proposed for traffic flow forecasting in [23].”
• Online Sparse Multi-Output Gaussian Process Regression and Learning (2019, background): “However, in practice, multi-output functions arise widely in geostatistics [16], robotics [17], sensor networks [18], chemometrics [19], traffic flow forecasting [20] and computational physics [21], [22], just to name a few.”
• Deep Architecture for Traffic Flow Prediction: Deep Belief Networks With Multitask Learning (2014, methodology): “These approaches, such as the autoregressive integrated moving average (ARIMA) model [32] that is extended from the autoregressive–moving average (ARMA) model [18] with an extra procedure of difference, focus on finding the patterns of the temporal variation of traffic flow and then use that informa”
• PPCA-Based Missing Data Imputation for Traffic Flow Volume: A Systematical Approach (2009, background): “3) How to retrieve and use the dependence relation among the spatially close (with no traffic lights locate between each other) loop detectors to boost the imputation performance [8], [41], [ 48 ] is not discussed here.”