Prediction of Road Network Traffic State Using the NARX Neural Network

Abstract

To provide reliable traffic information and more convenient visual feedback to traffic managers and travelers, we proposed a prediction model that combines a neural network and a Macroscopic Fundamental Diagram (MFD) for predicting the traffic state of regional road networks over long periods. The method is broadly divided into the following steps. To obtain the current traffic state of the road network, the traffic state efficiency index formula proposed in this paper is used to derive it, and the MFD of the current state is drawn by using the classification of the design speed and free flow speed of the classified road. Then, based on the collected data from the monitoring stations and the weighting formula of the grade roads, the problem of insufficient measured data is solved. Meanwhile, the prediction performance of NARX, LSTM, and GRU is experimentally compared with traffic prediction, and it is found that NARX NN can predict long-term flow and the prediction performance is slightly better than both LSTM and GRU models. Afterward, the predicted data from the four stations were integrated based on the classified road weighting formula. Finally, according to the traffic state classification interval, the traffic state of the road network for the next day is obtained from the current MFD, the predicted traffic flow, and the corresponding speed. The results indicate that the combination of the NARX NN with the MFD is an effective attempt to predict and describe the long-term traffic state at the macroscopic level.

Sugarcane Yield Estimation of Yamunanagar and Panipat District of Haryana Using NARX Model

“…Ji et al [5] [11] predicted wheat yield in Kanpur district of Uttar Pradesh by taking into account the most significant meteorological variable, i.e., maximum temperature at Critical Root Initiation (CRI) stage of wheat crop, which occurs about 21 days after sowing of the crop. Song et al [12] examined the combination of the NARX model with the MFD as exogenous variable is an effective attempt to predict and describe the long-term traffic state at the macroscopic level. Verma et al [13] constructed model employing regression techniques for the spring and autumn seasons revealed a strong correlation between predicted and measured values of sugarcane yield.…”

Prediction of ionospheric total electron content data using NARX neural network model

“…NARX was successfully tested for prediction of rainfall and showed improved results compared to linear algorithms like support vector machines [16]. NARX was used for traffic prediction and had superior prediction accuracy to RNN models [17]. The ability to predict symmetric horizontal (SYM-H) storm time index using NARX showed better accuracy compared to back propagation and recurrent Elman networks [18].…”