Abstract
In order to accurately analyse the impact of the rainy environment on the characteristics of highway traffic flow, a short-term traffic flow speed prediction model based on gate recurrent unit (GRU) and adaptive nonlinear inertia weight particle swarm optimization (APSO) was proposed. Firstly, the rainfall and highway traffic flow data were cleaned, and then they are matched according to the spatiotemporal relationship. Secondly, through the method of multivariate analysis of variance, the significance of the impact of potential factors on traffic flow speed was explored. Then, a GRU-based traffic flow speed prediction model in rainy environment is proposed, and the actual road sections under different rainfall scenarios were verified. After that, in view of the problem that the prediction accuracy of the GRU model was low in the continuous rainfall scenario, the APSO algorithm was used to optimize the parameters of the GRU network, and the APSO-GRU prediction model was constructed and verifications under the same road section and rain scene were carried out. The results show that the APSO-GRU model has significantly improved prediction stability than the GRU model and can better extract rainfall features during continuous rainfall, with an average prediction accuracy rate of 96.74%.
Highlights
Rainfall is the most frequently occurring severe weather, which brings serious impact to highway traffic safety
Cai et al [17] proposed a neural network based on improved cuckoo algorithm with optimized radial basis function (CS-RBF) for highway traffic flow prediction under heavy rainfall, and the study showed that the algorithm has better prediction accuracy and convergence speed
Under the noncontinuous rainfall scenario, the traffic flow speed of highway is obviously disturbed during the rainfall, and the change
Summary
Rainfall is the most frequently occurring severe weather, which brings serious impact to highway traffic safety. The data analysis and modelling system for the impact of weather factors on highway traffic flow is well established. Cai et al [17] proposed a neural network based on improved cuckoo algorithm with optimized radial basis function (CS-RBF) for highway traffic flow prediction under heavy rainfall, and the study showed that the algorithm has better prediction accuracy and convergence speed. Meng proposed the LSTM-GRU combined model to predict the short-term traffic flow speed of highways in rainy days. (1) For the research on the influence of rainy weather environment on traffic flow characteristics, most domestic and foreign scholars divide the rainfall intensity into levels [22]. We add rainfall features to the deep learning model to carry out the prediction of highway traffic flow speed under rainy environment. In view of the fact that the PSO algorithm can adjust the hyperparameters of the deep learning model and bring better prediction performance, this article will build the APSO-GRU model
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