A Multiscale and High-Precision LSTM-GASVR Short-Term Traffic Flow Prediction Model

Short-term traffic flow has the characteristics of complex, changeable, strong timeliness, and so on. So the traditional prediction algorithm is difficult to meet its high real-time and accuracy requirements. In this paper, a multiscale and high-precision LSTM-GASVR short-term traffic flow prediction algorithm is proposed. This method uses 15 min traffic flow data of the first 16 sections as input and completes the data preprocessing operation through reconstruction, normalization, and rising dimension by working day factor; establishing the prediction model based on the long- and short-term memory network (LSTM) and inverse normalization; and proposing the GA-SVR model to optimize the prediction results, so as to realize the real-time high-precision prediction of traffic flow. The prediction experiment is carried out according to the charge data of a toll station in Xi’an, Shaanxi Province, from May 2018 to May 2019. The comparison and analysis of various algorithms show that the prediction algorithm proposed in this paper is 20% higher than the LSTM, GRU, CNN, SAE, ARIMA, and SVR, and the R2 can reach 0.982, the explanatory variance is 0.982, and the MAPE is 0.118. The proposed traffic flow prediction algorithm provides strong support for traffic managers to judge the state of the road network to control traffic and guide traffic flow.

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SAn Urban Short-term Traffic Flow Prediction Model Based on Wavelet Neural Network with Improved Whale Optimization Algorithm

Sustainable Cities and Society ◽

10.1016/j.scs.2021.102858 ◽

2021 ◽

pp. 102858

Author(s):

Wangdi Du ◽

Qingyong Zhang ◽

Yuepeng Chen ◽

Ziliu Ye

Keyword(s):

Neural Network ◽

Prediction Model ◽

Traffic Flow ◽

Optimization Algorithm ◽

Wavelet Neural Network ◽

Whale Optimization Algorithm ◽

Short Term ◽

Traffic Flow Prediction ◽

Flow Prediction ◽

Whale Optimization

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Traffic Flow Prediction with Relevance Vector Machine

10.21203/rs.3.rs-547121/v1 ◽

2021 ◽

Author(s):

W.-Z. Xiong ◽

X.-M. Shen ◽

H.-J. Li ◽

Z. Shen

Keyword(s):

Prediction Model ◽

Real Time ◽

Traffic Flow ◽

Sequence Data ◽

Machine Method ◽

Traffic Flow Prediction ◽

Correlation Vector ◽

Model Based ◽

Flow Prediction ◽

Short Period

Abstract Real-time prediction of traffic flow values in a short period of time is an importantelement in building a traffic management system. The uncertainty, complexity andnonlinearity of traffic flow data make it difficult to predict traffic flow in real time,and the accurate traffic flow prediction has been an urgent problem in the industry.Based on the research of scholars, a traffic flow prediction model based on thecorrelation vector machine method is constructed. The prediction accuracy of thecorrelation vector machine is better than that of the logistic regression and supportvector machine methods, and the correlation vector machine method has the functionof generating prediction error range for the actual traffic sequence data. Theprediction results are very satisfactory, and the prediction speed is significantlyfaster than the other two models, which meets the requirement of real-time trafficflow prediction and is suitable for real-time online prediction, and the predictionaccuracy of the used method is relatively high. The three-way comparison analysisshows that the traffic flow prediction by the correlation vector machine methodcan describe the nonlinear characteristics of traffic flow change more accurately,and the model performance and real-time performance are better. The case studyshows that the traffic flow prediction model based on the correlation vector machinecan improve the speed and accuracy of prediction, which is very suitablefor traffic flow prediction estimation with real-time requirements, and provides ascientific method for real-time traffic flow measurement.

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Short-Term Traffic Flow Prediction and Its Application Based on the Basis-Prediction Model and Local Weighted Partial Least Squares Method

2019 14th International Conference on Computer Science & Education (ICCSE) ◽

10.1109/iccse.2019.8845432 ◽

2019 ◽

Author(s):

Zhiyang Gu ◽

Sun Zhou

Keyword(s):

Prediction Model ◽

Least Squares ◽

Traffic Flow ◽

Partial Least Squares ◽

Least Squares Method ◽

Short Term ◽

Traffic Flow Prediction ◽

Flow Prediction ◽

Partial Least Squares Method

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Research on short-term traffic flow prediction model based on RNN-LSTM

IOP Conference Series Materials Science and Engineering ◽

10.1088/1757-899x/806/1/012017 ◽

2020 ◽

Vol 806 ◽

pp. 012017

Author(s):

Shuying Liu ◽

Zhang Li ◽

Hangfu Li

Keyword(s):

Prediction Model ◽

Traffic Flow ◽

Short Term ◽

Traffic Flow Prediction ◽

Model Based ◽

Flow Prediction

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An New Approach of Real-Time Traffic Flow Prediction Based on Intelligent Transportation Technology

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.988.715 ◽

2014 ◽

Vol 988 ◽

pp. 715-718

Author(s):

Jia Yang Li ◽

Qin Xue ◽

Jin De Liu

Keyword(s):

Real Time ◽

Traffic Flow ◽

Intelligent Transportation System ◽

Intelligent Transportation ◽

Short Term ◽

New Approach ◽

Traffic Flow Prediction ◽

Real Time Traffic ◽

Flow Prediction ◽

Combination Approach

Short-term traffic flow forecasting is a core problem in Intelligent Transportation System .Considering linear and nonlinear, this paper proposes a short-term traffic flow intelligent combination approach. The weight of four forecasting model is given by the correlation coefficient and standard deviation method. The experimental results show that the new approach of real-time traffic flow prediction is higher precision than single method.

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