Abstract
Signaling positioning technology provides a new opportunity to understand an individual’s travel characteristics. In recent studies, the travel parameters obtained are mainly macroscopic travel information. However, extracting detailed trip chain information, such as the trip mode and mode-switching time point, remains a challenge. Furthermore, because of the iterative development of wireless networks, existing communication operators usually store different frequencies and accuracy (2G/3G and 4G) of signaling data simultaneously, making the refined identification of travel information more difficult. Therefore, this paper proposes a new method. First, we use the shortest distance algorithm to match the signaling data with the road network. Second, a wavelet transform modulus maximum (WTMM) algorithm is proposed to divide multimodal travel trajectories into single-mode trip segments; thus, spatiotemporal information related to mode transfer can be obtained. Finally, an unsupervised fuzzy kernel c-means clustering (FKCM) algorithm is proposed to distinguish travel modes. As comparison data, smartphone GPS and travel log data are also collected to analyze the detection result and improve the method. The identification errors of mode-switching time points at different frequencies are all less than 360 s. The average correct rate of traffic mode identification for 2G is 65.1%, and the average correct rate of traffic mode identification for 3G is 78.2%. 4G intensive cellular positioning data has a significantly better recognition effect than low-frequency data; the average trip mode detection accuracy reaches 89.6%, and the mode-switching time point detection errors are within 300 s.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: Transportation Research Record: Journal of the Transportation Research Board
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.