TRB15_demo_BigData_UrbanInformaticsThe rapid rise of location-based services provides us an opportunity to achieve the information of human mobility, in the form of participatory sensing, where users can share their digital footprints (i.e., checkins) at different geo-locations (i.e., venues) with timestamps. These checkins provide a broad citywide coverage, but the instant number of checkins in urban areas is still limited. Smart traffic control systems can provide abundant traffic flow data by physical sensing, but each controlled region only covers a small area, and there is no user information in the data.

We present a study combining participatory and physical sensing data, based on 3.4 million checkins collected in the Pittsburgh metropolitan area, and 125 million vehicle records collected in a sub-area controlled by an adaptive urban traffic control system. Our aim is to disclose how we could utilize the combined data for a better understanding on urban mobility networks and activity patterns in urban environments, and how we may take advantage of such combined data and use data analytics and machine learning to improve urban mobility applications such as anomaly traffic detection and reasoning, topic-based nontrivial traffic information extraction, and traffic demand analysis.

These knowledge would significantly contribute to deeper understandings and potential improvements that lead to smart cities with sustainable urban mobility.

  • Y. Gu, Z. Qian, X.-F. Xie, et al. An unsupervised learning approach for analyzing traffic impacts under arterial road closures: Case study of East Liberty in Pittsburgh. Journal of Transportation Engineering ASCE, 2016, 142(9): 04016033.
  • X.-F. Xie, Z. J. Wang. An empirical study of combining participatory and physical sensing to better understand and improve urban mobility networks. Transportation Research Board (TRB) Annual Meeting, Washington, DC, USA, 2015.
  • X.-F. Xie, Z. J. Wang. Combining physical and participatory sensing in urban mobility networks. Workshop on Big Data and Urban Informatics (BDUIC), Chicago, IL, USA, 2014: 859-876.

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