A Short-Term Traffic Flow Reliability Prediction Method considering Traffic Safety

Shaoqian Li,Zhenyuan Zhang,Yang Liu,Zixia Qin,Yong Chen

doi:10.1155/2020/6682216

Abstract

With the rapid development and application of intelligent traffic systems, traffic flow prediction has attracted an increasing amount of attention. Accurate and timely traffic flow information is of great significance to improve the safety of transportation. To improve the prediction accuracy of the backward-propagation neural network (BPNN) prediction model, which easily falls into local optimal solutions, this paper proposes an adaptive differential evolution (DE) algorithm-optimized BPNN (DE-BPNN) model for a short-term traffic flow prediction. First, by the mutation, crossover, and selection operations of the DE algorithm, the initial weights and biases of the BPNN are optimized. Then, the initial weights and biases obtained by the aforementioned preoptimization are used to train the BPNN, thereby obtaining the optimal weights and biases. Finally, the trained BPNN is utilized to predict the real-time traffic flow. The experimental results show that the accuracy of the DE-BPNN model is improved about 7.36% as compared with that of the BPNN model. The DE-BPNN is superior to the performance of three classical models for short-term traffic flow prediction.

Highlights

In recent years, with the development of traffic detection technology, big data technology, and data mining technology, accurate and real-time traffic flow operation data and traffic accident data are easy to collect [1]
Comparative Analysis of Models. eoretically, the adaptive differential evolution (DE)-backward-propagation neural network (BPNN) model proposed in this paper offers higher convergence rates and smaller prediction errors compared with the use of the BPNN alone
To compare the predictive performance of the DE-BPNN model, three classical prediction models were selected for comparison, including the autoregressive integrated moving average (ARIMA)-based, wavelet neural networks (WNNs)-based, and BPNN-based models. e results of the short-term traffic flow predictions performed with the ARIMA, BPNN, and WNN models are given below. e data in Figures 7–9 include the actual values, the predicted values, and the prediction errors, including the emergence of morning and evening peaks

Summary

Introduction

With the development of traffic detection technology, big data technology, and data mining technology, accurate and real-time traffic flow operation data and traffic accident data are easy to collect [1]. By studying the changing characteristics of traffic flow before and after traffic accidents, the traffic safety status is analyzed, evaluated, and forewarned according to the collected real-time traffic flow data. E accuracy of short-term traffic flow prediction directly affects the effects of traffic flow guidance and traffic control, which is of great significance for maintaining traffic safety. Real-time and accurate traffic flow prediction is the premise and key to the realization of both traffic flow guidance systems and traffic control systems [2]

Methods

Results

Conclusion