Prediction of Traffic Flow Speed Under Multiple Influencing Factors
Timely and accurate traffic flow prediction plays an important role in navigation planning and intelligent traffic dispatch.Road traffic is not only temporal and spatial correlation,but also a variety of environmental factors will have an important impact on traffic conditions.In order to improve the accuracy of road traffic flow speed prediction,firstly,the data of rainfall degrees and air pollution levels are graded,and the time period is divided into working days and non-working days.Then combined with Bidirectional Long-Term and Short-Term Memory network(BiLSTM)and feature engineering technology,we need to establish a Multi-Factor-based Traffic Flow Speed Prediction Model(MF-TPM),and model and analyze the public regional traffic speed time series datasets and weather datasets.Finally,experiments based on large-scale real traffic data show that the prediction accuracy of MF-TPM is 2.20％,4.94％and 0.63％higher than the commonly used Long-Short-Term Memory network(LSTM),Convolutional Neural Network(CNN)and BiLSTM network models,respectively.MF-TPM also has the best prediction performance under different rainfall levels and air pollution levels.
万方数据
维普
