3.8 Proceedings Paper

Understanding Urban Dynamics via State-sharing Hidden Markov Model

Publisher

ASSOC COMPUTING MACHINERY
DOI: 10.1145/3308558.3313453

Keywords

Urban Dynamics; Mobility; Hidden Markov Model

Funding

  1. National Key Research and Development Program of China [2017YFE0112300]
  2. National Nature Science Foundation of China [61861136003, 61621091, 61673237]
  3. Beijing National Research Center for Information Science and Technology [20031887521]

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Modeling people's activities in the urban space is a crucial socioeconomic task but extremely challenging due to the deficiency of suitable methods. To model the temporal dynamics of human activities concisely and specifically, we present State-sharing Hidden Markov Model (SSHMM). 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 a large-scale real-life mobility dataset and 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 an error of 0.0793, which outperforms the general HMM by 54.2%.

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