Uncovering Spatio-temporal Travel Patterns Using a Tensor-based Model from Metro Smart Card Data in Shenzhen, China

Jinjun Tang, Xiaolu Wang, Fang Zong and Zheng Hu
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Jinjun Tang: Smart Transport Key Laboratory of Hunan Province, School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China
Xiaolu Wang: Smart Transport Key Laboratory of Hunan Province, School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China
Fang Zong: College of Transportation, Jilin University, Changchun 130012, China
Zheng Hu: School of Intelligent transportation, Hunan Communication Engineering Polytechnic, Changsha 410132, China

Sustainability, 2020, vol. 12, issue 4, 1-16

Abstract: Individual mobility patterns are an important factor in urban traffic planning and traffic flow forecasting. How to understand the spatio-temporal distribution of passengers deeply and accurately, so as to provide theoretical support for the planning and operation of the metro network, is an urgent issue of wide concern. In this paper, we applied NCP decomposition to uncover the characteristics of travel patterns from temporal and spatial dimensions in the metro network of Shenzhen City. Utilizing matrix factorization and correlation analysis, we extracted several stable components from the collective mobility and find that the departure and arrival mobility patterns have different characteristics in both the temporal and spatial dimension. According to the point of interest (POI) data in the Shenzhen City, the function attributes of the station are identified and then we found that the spatial distribution characteristics of different patterns are different. We explored the distribution of travel time classified according to the spatio-temporal characteristics of stable patterns. The proposed method can decompose stable travel patterns from the collective mobility and the results in this study can help us to better understand different mobility patterns in both spatial and temporal dimensions.

Keywords: tensor decomposition; urban mobility; spatial and temporal characteristics; metro network; point of interest (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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