Bus Arrival Time Prediction Based on Pca-Ga-SVM

Peng, Zixuan; Jiang, Yonglei; Yang, Xiaoli; Zhang, Liu; Wang, Yitian

doi:10.14311/nnw.2018.28.005

Cited by 13 publications

(18 citation statements)

References 32 publications

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“…From the research results at this stage, we can see that in the current process of mathematics classroom design, there are few application rules of modern teaching methods based on the PCA algorithm or other data processing methods [ 17 ]. On the other hand, in the process of intelligent classroom design, there are many differences in its internal relevance [ 18 ].…”

Section: Related Workmentioning

confidence: 99%

[Retracted] Design and Research of Mathematics Teaching Intelligent Classroom Based on PCA‐NN Algorithm

Zhang

2021

Computational Intelligence and Neuroscience

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With the increasing importance of mathematics in basic education, how to evaluate and analyze the intelligent effect of mathematics teaching classroom through scientific methods has become one of the indicators to evaluate the intelligent classroom. This paper studies the design and application of mathematics teaching intelligent classroom based on the PCA-NN (principal component analysis-neural network) algorithm. Firstly, this paper briefly describes the current research status of mathematics teaching intelligent classroom design and PCA-NN algorithm. Secondly, combined with the key factors of mathematics teaching, it formulates specific standards and puts forward an adaptive strategy of intelligent and personalized intelligent mathematics teaching classroom. Finally, the algorithm is verified by experiments. The results show that, for students with different mathematics basic levels, the mathematics teaching intelligent classroom based on the PCA-NN algorithm can effectively improve the quality of mathematics classroom teaching. Through the research on the factors such as teaching quality, effect, and interaction mode involved in the process of mathematics teaching classroom design, the intelligent classroom design factors affecting teaching quality are determined. This paper analyzes and studies the system from different angles. The research results provide some help for the current quality evaluation of classroom teaching and use the PCA-NN algorithm to make quantitative analysis and multivariate verification of mathematics classroom teaching effect.

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Section: Related Workmentioning

confidence: 99%

[Retracted] Design and Research of Mathematics Teaching Intelligent Classroom Based on PCA‐NN Algorithm

Zhang

2021

Computational Intelligence and Neuroscience

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“…Yang, M., et al presents a prediction model of bus arrival time based on Support Vector Machine with a genetic algorithm (GA-SVM) [6]. Peng, Z., et al proposed a forecasting method based on principal component analysisgenetic algorithm-support vector machine (PCA-GA-SVM) to improve the precision of bus arrival time prediction [7]. B.…”

Section: A Support Vector Machinementioning

confidence: 99%

Bus Arrival Time Prediction Based on LSTM and Spatial-Temporal Feature Vector

Liu

et al. 2020

IEEE Access

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Bus arrival prediction has important implications for public travel, urban dispatch, and mitigation of traffic congestion. The factors affecting urban traffic conditions are complex and changeable. As the predicted distance increases, the difficulty of traffic prediction becomes more difficult. Forecast based on historical data responds quite slowly for changes under the short-term conditions, and vehicle prediction based on real-time speed is not sufficient to predict under long-term conditions. Therefore, an arrival prediction method based on temporal vector and another arrival prediction method based on spatial vector is proposed to solve the problems of remote dependence of bus arrival and road incidents, respectively. In this paper, combining the advantages of the two prediction models, this paper proposes a long short-term memory (LSTM) and Artificial neural networks (ANN) comprehensive prediction model based on spatialtemporal features vectors. The long-distance arrival-to-station prediction is realized from the dimension of time feature, and the short-distance arrival-to-station prediction is realized from the dimension of spatial feature, thereby realizing the bus-to-station prediction. Besides, experiments were conducted and tested based on the entity dataset, and the result shows that the proposed method has high accuracy among bus arrival prediction problems. INDEX TERMS Artificial neural networks, bus arrival prediction, LSTM, spatial-temporal feature vector.

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“…Let H be a model; λ be a regularized parameter; w be a k-dimensional parameter vector of the model. Then, the prior distribution of w can usually be expressed as [21][22][23][24]: (…”

Section: Bayesian Evidence Frameworkmentioning

confidence: 99%

Prediction of Energy-Efficient Production of Coalbed Methane Based on Chaotic Time Series and Bayes-Least Squares-Support Vector Machine

Wang¹,

Man²,

Lu³

2020

IJHT

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The productivity of coalbed methane (CBM) depends heavily on the heat environment, and directly reflects the quality of the well. Following the theories of phase space reconstruction and Bayesian evidence framework, this paper puts forward a Bayes-least squares-support vector machine (Bayes-LS-SVM) model for the prediction of energy-efficient productivity of CBM under Bayesian evidence network based on chaotic time series. The energy-efficient productivity stands for the gas and water production of CBM wells at a low energy consumption, despite the disturbance from the heat environment. The proposed model avoids the local optimum trap of backpropagation neural network (BPNN), and overcomes the main defects of the SVM: high time consumption of parameter determination, and proneness to overfitting. In our model, the model parameters are optimized through three-layer Bayesian evidence inference, and the input vector for prediction is selected adaptively. In this way, the model construction is not too empirical, and the constructed model is highly adaptive. Then, the theory on phase space reconstruction was applied to investigate the chaotic property of the time series on CBM production, and the Bayes-LS-SVM was adopted to predict the time series after phase space reconstruction, in comparison with neural network prediction methods like SVM and BPNN. Experimental results show that the proposed model boast quick computing, accurate fitting, flexible structure, and strong generalization ability.

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Bus Arrival Time Prediction Based on Pca-Ga-SVM

Cited by 13 publications

References 32 publications

[Retracted] Design and Research of Mathematics Teaching Intelligent Classroom Based on PCA‐NN Algorithm

[Retracted] Design and Research of Mathematics Teaching Intelligent Classroom Based on PCA‐NN Algorithm

Bus Arrival Time Prediction Based on LSTM and Spatial-Temporal Feature Vector

Prediction of Energy-Efficient Production of Coalbed Methane Based on Chaotic Time Series and Bayes-Least Squares-Support Vector Machine

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