Short-Term Traffic Flow Prediction Using the Modified Elman Recurrent Neural Network Optimized Through a Genetic Algorithm

Abolghasem Sadeghi-Niaraki,Parima Mirshafiei,Soo-Mi Choi,Maryam Shakeri

doi:10.1109/access.2020.3039410

Abstract

Traffic stream determining is an essential part of the intelligent transportation management system. Precise prediction of traffic flow provides a basis for other tasks, like forecasting travel time. While traditional methods have some merits for improving traffic prediction precision in some ways, high precision, considering different circumstances, is still difficult to achieve. This article presents a short-term traffic flow prediction model based on the Modified Elman Recurrent Neural Network model (GA-MENN) to deal with this practical problem. In GA-MENN, the algorithm of Elman Recurrent Neural Network is modified, optimized through the Genetic Algorithm (GA) and considered weather conditions, weekday, hour and day’s classification to forecast the vehicle velocity in Tehran streets and highways. The traffic data were collected from the online Google Map API service for 139 routs in 7 districts in Tehran. The method improves prediction precision and also lowers the prediction error rate, according to experimental results. Exploratory outcomes verify the superior performance of the proposed traffic condition prediction model over Regression Multi-layer Perceptron, Linear Regression, Logistic Regression, Probabilistic Neural Network, Regression Generalized Feedforward, Time-lag Recurrent Network, Support Vector Machine model, Elman neural network, K- NN model, ARIMA, Kalman filter model, Convolutional Neural Networks (CNNs), SARIMA, and Long Short-Term Memory (LSTM) model. To the best of our knowledge, this is the first occasion when that traffic stream is gauged in urban roads and avenues in this specific way.

Highlights

With the growth of city-dwelling populations, cities are in need of increasingly sophisticated transportation systems to address the transport needs of its citizens[1, 2]
This paper presents a short-term traffic flow prediction model based on the Modified Elman Recurrent Neural Network model (GA-MENN) to deal with this practical problem
Exploratory outcomes verify the superior performance of the proposed traffic condition prediction model over Regression Multi-layer Perceptron, Linear Regression, Logistic Regression, Probabilistic Neural Network, Regression Generalized Feedforward, Time-lag Recurrent Network, Support Vector Machine model, Elman neural network, K-nearest neighbors (K-NN) model, autoregressive integrated moving average (ARIMA), Kalman filter model, Convolutional Neural Networks (CNNs), Seasonal ARIMA (SARIMA), and Long Short-Term Memory (LSTM) model

Summary

Introduction

Given the end effects of traffic congestion such as air pollution, prolonged travel time, and dissatisfaction of citizens, the prediction of traffic flows on the city streets has become a popular subject of research. Traffic flow forecasting methods can be categorized based on whether they are model-based or data-driven, direct or indirect, and parametric or nonparametric. Given the reliance of this method on estimates, it is more complex than the other approaches and requires more careful supervision. Notable examples of this approach include DyanMIT-R, SBOTTP, TOPL (CTM) and OLSM [7]. Traffic flow predictions are based on the differences in inputs (traffic flow, velocity, volume, etc.), but in the direct approach, traffic flow is predicted based on the past data.

Methods

Results

Conclusion