Understanding Urban Dynamics via State-Sharing Hidden Markov Model

With the ever-increasing urbanization process, systematically modeling people's activities in the urban space is being recognized as a crucial socioeconomic task. It is extremely challenging due to the lack of reliable data and suitable methods, yet the emergence of population-scale urban mobility data sheds new light on it. However, recent works on discovering activity patterns from urban mobility data are still limited in terms of concisely and specifically modeling the temporal dynamics of people's urban activities. To bridge the gap, we present a State-sharing Hidden Markov Model (SSHMM), a novel time-series modeling method that uncovers urban dynamics with massive urban mobility data. SSHMM models the urban dynamics from two aspects. First, it extracts the urban states from the whole city, which captures the volume of population flows as well as the frequency of each type of Point of Interests (PoIs) visited. Second, it characterizes the urban dynamics of each urban region as the state transition on the shared-states, which reveals distinct daily rhythms of urban activities. We evaluate our method via large-scale real-life mobility dataset. The results demonstrate that SSHMM learns semantics-rich urban dynamics, which are highly correlated with the functions of the region. Besides, it recovers the urban dynamics in different time slots with RMSE of 0.0793 when only learn limited states for the whole city, which outperforms the general HMM by 54.2 percent.

Automated summary

With the increasing urbanization process, modeling people's activities in urban space is a challenging task. The State-sharing Hidden Markov Model (SSHMM) is a novel time-series modeling method that uncovers urban dynamics and learns semantics-rich dynamics highly correlated with region functions.