Individual trip mode recognition based on smartphone GPS positioning data

Xiaolong Zhang

doi:10.1117/12.2627813

14 March 2022 Individual trip mode recognition based on smartphone GPS positioning data

Xiaolong Zhang

Proceedings Volume 12165, International Conference on Intelligent Traffic Systems and Smart City (ITSSC 2021); 1216525 (2022) https://doi.org/10.1117/12.2627813
Event: 2021 International Conference on Intelligent Traffic Systems and Smart City, 2021, Zhengzhou, China

Abstract

Traditional resident travel survey methods, such as paper questionnaire, telephone interview and mail inquiry, have disadvantage of low data accuracy, difficult organization and limited sampling size. GPS-based travel survey method is playing an increasingly important role in modern transportation planning. This paper proposes an innovative method for detecting individual trip mode recognition by using mobile phone GPS positioning data. First, a smartphone GPS sensorbased application is developed for multi-mode travel trajectory data collection. Data characteristics of different trip modes are deeply analyzed and the characterization indexes of different modes are put forward. Second, a support vector machine (SVM) algorithm is proposed for trip mode detection. SVM can map low-dimensional data to high-dimensional space for segmentation, is especially suitable for traffic mode recognition. Results show that the average mode detection accuracy reaches 92% for walk, bike, bus and car. This paper can provide solid data support for urban traffic planning.

Citation Download Citation

Xiaolong Zhang "Individual trip mode recognition based on smartphone GPS positioning data", Proc. SPIE 12165, International Conference on Intelligent Traffic Systems and Smart City (ITSSC 2021), 1216525 (14 March 2022); https://doi.org/10.1117/12.2627813

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