Short-term freeway traffic flow prediction : Bayesian combined neural network approach

By Weizhong Zheng, Der-Horng Lee, Qixin Shi. 2006

451 citations recorded by the source corpus.

Subfields: Building and Construction, Control and Systems Engineering, Transportation

View Paper: https://www.semanticscholar.org/paper/1389cd1217046b47cbbe8d396e295d6f4c7270a2

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. Yunpeng Wang
4. Xiaolei Ma
5. Zhengbing He
6. Guojie Song
7. Kunqing Xie
8. Yuxing Sun

In Control and Systems Engineering

1. Yinhai Wang
2. Weibin Zhang
3. Zhifeng Bao
4. A. K. Qin
5. Farhana Murtaza Choudhury
6. Baihua Zheng
7. David Alexander Tedjopurnomo
8. Kit Yan Chan

In Transportation

1. Yinhai Wang
2. Yunpeng Wang
3. Xiaolei Ma
4. Guojie Song
5. Kunqing Xie
6. Weibin Zhang
7. Yuxing Sun
8. Biao Leng

Citations in the corpus

• Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction (2017, methodology): “[22] combined an ANN with Bayes’ theorem to predict short-term freeway traffic flow.”
• Adaptive Kalman filter approach for stochastic short-term traffic flow rate prediction and uncertainty quantification (2014, background): “, 1996), ATHENA (Danech-Pajouh and Aron, 1991), Bayesian-neural network approach (Zheng et al., 2006), hybrid fuzzy rule-based approach (Dimitriou et al.”
• Neural-Network-Based Models for Short-Term Traffic Flow Forecasting Using a Hybrid Exponential Smoothing and Levenberg–Marquardt Algorithm (2012, methodology): “Results show that generalization abilities of NNs are better than those obtained by the standard LM algorithm for time series forecasting [30], including short-term traffic flow forecasting [53].”
• A novel wavelet-SVM short-time passenger flow prediction in Beijing subway system (2015, background): “Then, several algorithms, such as Bayesian networks [15–17], neural networks [15,18,19] and support vector machines (SVM) [20–22], belong to the artificial intelligence field have been widely applied in the intelligent transportation systems.”
• Deep Architecture for Traffic Flow Prediction: Deep Belief Networks With Multitask Learning (2014, background): “Index Terms —Deep learning, multitask learning (MTL), task grouping, traffic flow prediction.”
• A Survey on Modern Deep Neural Network for Traffic Prediction: Trends, Methods and Challenges (2020, methodology): “Lee and Fambro [17] applied subset ARIMA and found that it provides stable and accurate results.”
• Supervised Weighting-Online Learning Algorithm for Short-Term Traffic Flow Prediction (2013, methodology): “In addition, Zheng et al. [40] developed two hybrid NNs, i.e., backpropagation and radial basis function (RBF) NNs, which were combined into a heuristic credit assignment algorithm based on the theory of conditional probability and Bayes’ rule.”
• An Improved Fuzzy Neural Network for Traffic Speed Prediction Considering Periodic Characteristic (2017, methodology): “vector regression [19]–[23], Kalman filter theory [24]–[29] and hybrid approaches [18], [30]–[34].”