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.
Highlights
Urban transportation is an important support for the economic development of the cities, which profoundly affects the living quality of citizens, the efficiency of logistics, as well as other factors related with transport
Negative values are difficult to explain in the matrix elements of tensor decomposition, so we apply negative CANDECOMP/PARAFAC (NCP) decomposition [35] with alternating Poisson regression on a two-dimensional tensor constructed from smart card data to analyze the spatial-temporal characteristics of passengers’ travel
The urban mobility discussed in this study refers to a specific travel mode, and this paper is dedicated to explore passengers’ travel patterns based on smart card data from an urban metro network
Summary
Urban transportation is an important support for the economic development of the cities, which profoundly affects the living quality of citizens, the efficiency of logistics, as well as other factors related with transport. Negative values are difficult to explain in the matrix elements of tensor decomposition, so we apply NCP decomposition [35] with alternating Poisson regression on a two-dimensional tensor constructed from smart card data to analyze the spatial-temporal characteristics of passengers’ travel. The urban mobility discussed in this study refers to a specific travel mode, and this paper is dedicated to explore passengers’ travel patterns based on smart card data from an urban metro network. We applied NCP decomposition to uncover the characteristic of travel patterns from temporal and spatial dimension in the metro network of Shenzhen City. Based on the POI data collected in Shenzhen City, the function of the station is set and we further analyze the spatial distribution characteristics of different travel patterns.
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