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

Yang Zhou · Chao Yang

13 citations0/0 files storedTransportation Planning and Technology

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Human Mobility and Location-Based Analysis Transportation Planning and Optimization Data Management and Algorithms

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.

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References

2014Review of GPS Travel Survey and GPS Data-Processing Methods 2009Processing Raw Data from Global Positioning Systems without Additional Information 2011A GPS/GIS method for travel mode detection in New York City 2013Future Mobility Survey 2015Travel mode detection based on GPS track data and Bayesian networks 2006Increasing the accuracy of trip rate information from passive multi-day GPS travel datasets: Automatic trip end identification issues 2006Comparative Analysis of Global Positioning System-Based and Travel Survey-Based Data 2013Detecting activity locations from raw GPS data: a novel kernel-based algorithm

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2022Towards big data behavioral analysis: rethinking GPS trajectory mining approaches from geographic, semantic, and quantitative perspectives 2022Trip end identification based on spatial-temporal clustering algorithm using smartphone positioning data 2021Activity behavior of residents of Paraisópolis slum: Analysis of multiday activity patterns using data collected with smartphones

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