Title: Statistical analysis for predicting residents' travel mode based on random forest

Authors: Lei Chen; Zhengyan Sun; Shunxiang Zhang; Guangli Zhu; Subo Wei

Addresses: School of Computer Science, Huainan Normal University, Huainan, China; Artificial Intelligence Research Institute of Hefei Comprehensive National Science Center, Hefei, China ' School of Computer Science and Engineering, Anhui University of Science and Technology, Huainan, China ' School of Computer Science and Engineering, Anhui University of Science and Technology, Huainan, China ' School of Computer Science and Engineering, Anhui University of Science and Technology, Huainan, China ' School of Computer Science and Engineering, Anhui University of Science and Technology, Huainan, China

Abstract: Random forest has achieved good results in the prediction task, but due to the complexity of travel mode and the uncertainty of random forest, the prediction accuracy of travel mode is low. To improve the accuracy of prediction, this paper proposes a residents' travel modes prediction method based on the random forest. To extract valuable feature information, the questionnaire survey data is collected, which is pre-processed by three kinds of appropriate methods. Then, each feature is analysed by the statistical learning method to obtain the important feature of transportation selection. Finally, a random forest is constructed to predict the travel mode of residents' selection of transportation. The parameters of random forest are modified and improved to achieve higher prediction accuracy of travel mode. The experimental results show that the method proposed in this paper effectively improves the prediction accuracy of the travel mode.

Keywords: residents' travel mode; statistical analysis; random forest.

DOI: 10.1504/IJCSE.2024.136246

International Journal of Computational Science and Engineering, 2024 Vol.27 No.1, pp.9 - 19

Received: 15 Mar 2022
Received in revised form: 07 Jun 2022
Accepted: 11 Jul 2022
Published online: 25 Jan 2024 *

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