Short-Term Traffic Prediction considering Spatial-Temporal Characteristics of Freeway Flow

Jiaqi Wang,Xianling Yang,Yingying Ma,Haoxi Wei,Teng Li,Feng-Jang Hwang

doi:10.1155/2021/5815280

Jiaqi Wang, Xianling Yang + Show 4 more

Open Access

https://doi.org/10.1155/2021/5815280

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Abstract

This paper presents a short-term traffic prediction method, which takes the historical data of upstream points and prediction point itself and their spatial-temporal characteristics into consideration. First, the Gaussian mixture model (GMM) based on Kullback–Leibler divergence and Grey relation analysis coefficient calculated by the data in the corresponding period is proposed. It can select upstream points that have a great impact on prediction point to reduce computation and increase accuracy in the next prediction work. Second, the hybrid model constructed by long short-term memory and K-nearest neighbor (LSTM-KNN) algorithm using transformed grey wolf optimization is discussed. Parallel computing is used in this part to reduce complexity. Third, some meaningful experiments are carried out using real data with different upstream points, time steps, and prediction model structures. The results show that GMM can improve the accuracy of the multifactor models, such as the support vector machines, the KNN, and the multi-LSTM. Compared with other conventional models, the TGWO-LSTM-KNN prediction model has better accuracy and stability. Since the proposed method is able to export the prediction dataset of upstream and prediction points simultaneously, it can be applied to collaborative management and also has good potential prospects for application in freeway networks.

Highlights

Intelligent transportation system (ITS) has become an effective way to reduce pollution and improves the performance of freeways, while the short-term traffic flow prediction is an important part to support the smart management and control of freeways. e trend of shortterm traffic flow prediction is changing from parametric statistical models to nonparametric models and mixed models
E accuracy of Long short-term memory (LSTM)-K-Nearest Neighbor (KNN) can reach the level of the popular model. e accuracy of transformed grey wolf optimizer (TGWO)-LSTM-KNN can be improved by 15.27% compared with single-LSTM, 9.47% compared with BP, and 43.12% compared with poly-Support vector machines (SVM)
The TGWO-LSTM-KNN prediction model with Gaussian mixture model (GMM) classification considering spatial-temporal characteristics under the concept of attention mechanism is proposed. e time series is divided into parts by using the temporal characteristic of the prediction point

Summary

Introduction

Intelligent transportation system (ITS) has become an effective way to reduce pollution and improves the performance of freeways, while the short-term traffic flow prediction is an important part to support the smart management and control of freeways. e trend of shortterm traffic flow prediction is changing from parametric statistical models to nonparametric models and mixed models. With the rapid development of ITS and improvement of data quality, more nonparametric prediction methods are used in the prediction of traffic flow. Particle swarm optimization (PSO) and other optimization algorithms were applied to SVM because of small model calculation and good prediction performance [18]. Smith and Demetsky [19] used backpropagation (BP) neural network to do the prediction Optimization algorithms such as PSO and genetic algorithm (GA) were applied to BP, and the effect is obvious [20, 21]. Improvements and combinations with other models have been proposed in many fields, from application in large-scale data problems [32] to the prediction of traffic flow, such as using GA to optimize the LSTM hyperparameters to get better performance [33].

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