Title: Traffic flow prediction model of urban traffic congestion period based on internet of vehicles technology

Authors: Xiaofeng Shi; Yaohong Zhao

Addresses: Centre of Modern Education Technology, Changchun Institute of Technology, Changchun 130012, China ' Faculty of Computer Science and Technology, Changchun University, Changchun 130022, China

Abstract: There are some problems in the existing traffic flow forecasting models, such as low prediction accuracy and high time cost. RFID technology is used to transmit traffic flow data information during urban traffic congestion, and extract information to control the running state of vehicles on the road. The basic parameters of traffic flow are set, vehicle RFID data source is used to preprocess duplicate data and missing data of traffic flow, and differential stationarity and normalisation are processed; LSTM neural network is used to train traffic flow data iteratively and output estimate results. The comparison shows that the MAPE, RMSE and Mae of the proposed model are 12.34%, 23.18% and 15.87% respectively, which improves the prediction accuracy, and the shortest prediction time is about 22 ms.

Keywords: internet of vehicles technology; traffic flow; radio frequency identification technology; LSTM neural network; normalisation processing.

DOI: 10.1504/IJICT.2022.126430

International Journal of Information and Communication Technology, 2022 Vol.21 No.4, pp.429 - 444

Received: 17 Nov 2020
Accepted: 23 Dec 2020
Published online: 26 Oct 2022 *

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