Open Access

ARTICLE

Prediction of Traffic Volume of Motor Vehicles Based on Mobile Phone Signaling Technology

by Jin Shang^1,*, Hailong Su^2,*, Kai Hu³, Xin Guo³, Defa Sun³

1 School of Architecture, The University of Sheffield, Sheffield, S10 2TN, England
2 Department of Environmental Science and Engineering, Fudan University, Shanghai, 200438, China
3 Fudan Planning and Architectural Design Institute, Shanghai, 200433, China

* Corresponding Authors: Jin Shang. Email: ; Hailong Su. Email:

Computers, Materials & Continua 2023, 75(1), 799-814. https://doi.org/10.32604/cmc.2023.035729

Received 01 September 2022; Accepted 18 November 2022; Issue published 06 February 2023

Abstract

Urban traffic volume detection is an essential part of traffic planning in terms of urban planning in China. To improve the statistics efficiency of road traffic volume, this thesis proposes a method for predicting motor vehicle traffic volume on urban roads in small and medium-sized cities during the traffic peak hour by using mobile signal technology. The method is verified through simulation experiments, and the limitations and the improvement methods are discussed. This research can be divided into three parts: Firstly, the traffic patterns of small and medium-sized cities are obtained through a questionnaire survey. A total of 19745 residents were surveyed in Luohe, a medium-sized city in China and five travel modes of local people were obtained. Secondly, after the characteristics of residents’ rest and working time are investigated, a method is proposed in this study for the distribution of urban residential and working places based on mobile phone signaling technology. Finally, methods for predicting traffic volume of these travel modes are proposed after the characteristics of these travel modes and methods for the distribution of urban residential and working places are analyzed. Based on the actual traffic volume data observed at offline intersections, the project team takes Luohe city as the research object and it verifies the accuracy of the prediction method by comparing the prediction data. The prediction simulation results of traffic volume show that the average error rate of traffic volume is unstable. The error rate ranges from 10% to 30%. In this thesis, simulation experiments and field investigations are adopted to analyze why these errors occur.

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Keywords

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