Investigating physical encounters of individuals in urban metro systems with large-scale smart card data

Abstract Investigating physical encounters among individuals is important for various applications such as infectious disease modeling and friend recommendation. As enclosed and densely populated spaces, public transit systems (e.g., buses and metros) in densely populated areas are locations where physical encounters occur numerously. The encounter networks on buses have been investigated with the help of smart card data (SCD); however, to the best of our knowledge, no attempts have been made to explore the physical encounters inside urban metro systems, which is a challenging task, as the travel behaviors of a metro passenger are complex and are not recorded in the SCD directly. This study proposed a novel framework to extract and measure physical encounters of individuals in urban metro systems with SCD. First, we developed a method to match passengers to specific trains, which can allow the segmentation of individual trips inside a metro system. Second, we proposed an approach to measuring the encounter frequencies and durations of passenger pairs by synthesizing the passengers’ encounter behaviors in not only the train space, but also the entering/exiting and transfer spaces. Finally, using the SCD of Shenzhen, China, this study analyzed the physical encounter patterns at a population scale, and demonstrated the potential of applying the encounter network to trace the spread of infectious diseases. Overall, this study provided a framework for evaluating physical encounters in metro systems with SCD, and revealed the underlying physical encounter patterns of the metro system in a densely populated city, which is of considerable application value.