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

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.

Accident Detection and Flow Prediction for Connected and Automated Transport Systems

“…After a trafc accident occurs, it is necessary to predict its impact and take emergency management measures to clear up the accident and reduce losses. Some studies used trafc wave models and fuid mechanics models to predict the impact of trafc accidents, while most of the others estimated the impact degree by using neural network [12], decision tree [13], and other machine learning methods [14]. Among them, Markov is one of the most popular approaches to predict trafc condition.…”

A New Ensemble Reinforcement Learning Recursive Network for Traffic Volume Forecasting in a Freeway Network

“…Kumar et al used the artificial neural network (ANN) in traffic flow prediction for a non-urban highway, which showed better predictive performance compared with analytical and statistical methods [9]. Li et al designed the adaptive differential evolution (DE) optimized backward propagation neural network (BPNN) to increase the accuracy of traffic flow prediction and avoid the local optimal results [10].…”