Traffic mode recognition based on optimized temporal convolutional neural network by using cell phone GPS data

Yaqi Zhu; Bugao Zhang; Liang Wang; Huahua Kong; Zhenxing Yao

doi:10.1117/12.3024025

14 February 2024 Traffic mode recognition based on optimized temporal convolutional neural network by using cell phone GPS data

Yaqi Zhu, Bugao Zhang, Liang Wang, Huahua Kong, Zhenxing Yao

Author Affiliations +

Proceedings Volume 13018, International Conference on Smart Transportation and City Engineering (STCE 2023); 1301809 (2024) https://doi.org/10.1117/12.3024025
Event: International Conference on Smart Transportation and City Engineering (STCE 2023), 2023, Chongqing, China

Abstract

With the continuous development of urban transportation planning, how to accurately obtain information on individual travel patterns has become the core problem of research. However, traditional methods of residential travel surveys, such as paper questionnaires, telephone interviews, and mail inquiries, are limited by low data accuracy, organizational difficulties, and limited sample size, which cannot meet the needs of modern transportation planning. GPS-based travel survey methods have gradually become the focus of researchers' attention due to their advantages of high accuracy positioning, convenience, and no human intervention. In this paper, we propose a new method for individual travel mode recognition using cell phone GPS positioning data. Firstly, through a multi-mode travel trajectory data collection application based on smartphone GPS sensors, the characterization indexes of different travel modes are extracted. Secondly, this paper proposes a temporal convolutional neural network algorithm for travel mode recognition, which has strong stability and self-adaptability, and combined with smoothing optimization can effectively improve the accuracy rate of mode recognition. The experimental results show that the average accuracy of mode detection for walking, bicycle, bus and car reaches more than 96%, which verifies the feasibility and effectiveness of the method in this paper. The research results of this paper can provide strong support for future urban transportation planning and travel behavior research.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yaqi Zhu, Bugao Zhang, Liang Wang, Huahua Kong, and Zhenxing Yao "Traffic mode recognition based on optimized temporal convolutional neural network by using cell phone GPS data", Proc. SPIE 13018, International Conference on Smart Transportation and City Engineering (STCE 2023), 1301809 (14 February 2024); https://doi.org/10.1117/12.3024025

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KEYWORDS

Global Positioning System

Convolutional neural networks

Detection and tracking algorithms

Data modeling

Evolutionary algorithms

Mathematical optimization

Cell phones

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