Abstract
Urban subway is taken by people in different frequencies, thus leading them to present different dependency varieties on this mode. Yet, how those passengers who possess low dependency on urban subway travel is less investigated. Under this background, we propose a framework to uncover passengers’ dependency variety on stations’ functions in urban subway. To begin with, nine features regarding travel frequency and time are extracted from 100 million transaction records generated by 11.45 million passengers in a month. Thus, their travel dependency on urban subway is quantified. These features are clustered into 5 distinct levels via the k-means algorithm, before an inference of subway stations’ functions from 236,040 POI data sources via the LDA approach. In this way, passengers’ travel purposes can be identified. How passengers with different dependency levels behave in subway stations in space and time is further explored in a visualization way. The intuitive experimental results, validated by priori user experiences and land-use plan of Beijing, show that among the 5 levels of dependency varieties, passengers in the first two groups present a relatively strong dependency on urban subway. Meanwhile, passengers in the rest three groups possess a low dependency on urban subway and display extreme travel patterns in time and frequency, greatly increasing management difficulties for transit operators. Findings in this research help distinguish passengers with low levels of subway dependency and grasp how those passengers without striking dependency travel by subway and what for so that practitioners can conduct an accurate risk assessment on them.
Highlights
Urban subway is taken by individuals in megacities in different frequencies, which correlates to passengers’ dependency levels on this transportation mode to some extent [1]
They may increase Journal of Advanced Transportation the population density of a station so that deadly human stampedes may be triggered. erefore, it is necessary to grasp the knowledge that how do passengers without striking dependency on urban subway travel and what for so that practitioners can conduct an accurate risk assessment on this type. eir low dependency towards urban subway may increase the difficulty of solving the above questions; a prevalence of big smart card (SC) data recording passengers’ digital footprints can relieve it
Uncovering the function of a subway station is of great importance to demonstrate their potential travel orientations, yet few research have combined this specific index into mobility discovery. is results in a further blurry examination of passengers’ mobility patterns without dominant travel patterns in public transit. us, it is urgent to distinguish those passengers without striking dependency from commuters and uncover how they travel by subway and what for
Summary
Urban subway is taken by individuals in megacities in different frequencies, which correlates to passengers’ dependency levels on this transportation mode to some extent [1]. Apart from the above studies, some more research gave specific attention to characterizing mobility patterns for certain special groups, such as tourists [2], people who go shopping [11], the elderly [12], pickpockets [13,14], or passengers with extreme travel patterns [15] They did not quantify their travel dependency on urban subway. Solving the above problems may guide transit operators to design tailored management of specific passengers, e.g., the elderly, tourists, or pickpockets, and enhance a benign interaction between human mobility styles and mature land-use plan Under this background, we propose a comprehensive framework to uncover passengers’ dependency varieties on subway stations with different functions.
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