Title: A new prediction method of short-term traffic flow at intersection based on internet of vehicles

Authors: Ying Zheng; Ying Zhou

Addresses: School of Computer Science and Technology, Huaibei Normal University, Huaibei, Anhui 235000, China; School of Information Technology, Huaibei Normal University, Huaibei, Anhui 235000, China ' School of Computer Science and Technology, Huaibei Normal University, Huaibei, Anhui 235000, China; School of Information Technology, Huaibei Normal University, Huaibei, Anhui 235000, China

Abstract: In order to overcome the problems of large error and long time-consuming in the prediction of short-term traffic flow at intersections, a new short-term traffic flow prediction method based on internet of vehicles is proposed in this paper. In the environment of internet of vehicles, the training samples are input into the prediction model of internet of vehicles, the output value is calculated, and the error is obtained. Then, the weights and wavelet factors of the network are modified by gradient descent algorithm. When the network error reaches the set accuracy or reaches the maximum training times, the training is stopped to get the predicted short-term traffic flow. The experimental results show that the mean square percentage error is about 0.01%, and the longest prediction time is 0.878 min. the fitting degree between the predicted value and the actual value of traffic flow is high, and the prediction effect is ideal.

Keywords: internet of vehicles; intersection; short-term traffic flow; prediction.

DOI: 10.1504/IJVICS.2022.127408

International Journal of Vehicle Information and Communication Systems, 2022 Vol.7 No.3, pp.228 - 243

Received: 10 Jul 2020
Accepted: 15 Sep 2020
Published online: 05 Dec 2022 *

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