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Identifying trip ends from raw GPS data with a hybrid spatio-temporal clustering algorithm and random forest model: a case study in Shanghai

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  • Yang Zhou
  • Chao Yang
  • Rongrong Zhu

Abstract

Smartphones have been advocated as the preferred devices for travel behavior studies over conventional surveys. But the primary challenges are candidate stops extraction from GPS data and trip ends distinction from noise. This paper develops a Resident Travel Survey System (RTSS) for GPS data collection and travel diary verification, and then uses a two-step method to identify trip ends. In the first step, a density-based spatio-temporal clustering algorithm is proposed to extract candidate stops from trajectories. In the second step, a random forest model is applied to distinguish trip ends from mode transfer points. Results show that the clustering algorithm achieves a precision of 96.2%, a recall of 99.6%, mean absolute error of time within 3 min, and average offset distance within 30 meters. The comprehensive accuracy of trip ends identification is 99.2%. The two-step method performs well in trip ends identification and promotes the efficiency of travel survey systems.

Suggested Citation

  • Yang Zhou & Chao Yang & Rongrong Zhu, 2019. "Identifying trip ends from raw GPS data with a hybrid spatio-temporal clustering algorithm and random forest model: a case study in Shanghai," Transportation Planning and Technology, Taylor & Francis Journals, vol. 42(8), pages 739-756, November.
    • Handle: RePEc:taf:transp:v:42:y:2019:i:8:p:739-756
    DOI: 10.1080/03081060.2019.1675309
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    Cited by:

    1. Pizzol, Bruna & Strambi, Orlando & Giannotti, Mariana & Arbex, Renato Oliveira & Alves, Bianca Bianchi, 2021. "Activity behavior of residents of Paraisópolis slum: Analysis of multiday activity patterns using data collected with smartphones," Journal of choice modelling, Elsevier, vol. 39(C).
    2. Liu, Jianmiao & Li, Junyi & Chen, Yong & Lian, Song & Zeng, Jiaqi & Geng, Maosi & Zheng, Sijing & Dong, Yinan & He, Yan & Huang, Pei & Zhao, Zhijian & Yan, Xiaoyu & Hu, Qinru & Wang, Lei & Yang, Di & , 2023. "Multi-scale urban passenger transportation CO2 emission calculation platform for smart mobility management," Applied Energy, Elsevier, vol. 331(C).

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