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.

  • X. Xie and Z. Wang, “Multiscale crash analysis: A case study of integrating FARS, Maryland’s crash data, and Montgomery County’s traffic violation data,” in Transportation Research Board (TRB) Annual Meeting, Washington, DC, 2018. [PPT] [Bibtex]
    @InProceedings{xie2018multiscale,
    title={Multiscale crash analysis: A case study of integrating FARS, Maryland's crash data, and Montgomery County's traffic violation data},
    author={Xie, Xiao-Feng and Wang, Zun-Jing},
    Booktitle = {Transportation Research Board (TRB) Annual Meeting},
    number={18-2283},
    Address = {Washington, DC},
    PPT={http://www.wiomax.com/team/xie/demo/TRB18_demo.pdf},
    year={2018}
    }
  • Y. Gu, Z. Qian, and X. Xie, “An unsupervised learning approach for analyzing traffic impacts under arterial road closures: Case study of East Liberty in Pittsburgh,” Journal of Transportation Engineering, vol. 142, iss. 9, p. 4016033, 2016. [DOI] [Bibtex]
    @Article{Gu2016,
    Title = {An unsupervised learning approach for analyzing traffic impacts under arterial road closures: Case study of {East Liberty} in {Pittsburgh}},
    Author = {Yiming Gu and Zhen Qian and Xiao-Feng Xie},
    journal = {{Journal of Transportation Engineering}},
    Year = {2016},
    Number = {9},
    Volume = {142},
    Pages = {04016033},
    Doi={10.1061/%28ASCE%29TE.1943-5436.0000860},
    publisher={American Society of Civil Engineers}
    }
  • X. Xie and Z. Wang, “An empirical study of combining participatory and physical sensing to better understand and improve urban mobility networks,” in Transportation Research Board (TRB) Annual Meeting, Washington, DC, 2015. [PDF] [PPT] [DOI] [Bibtex]
    @InProceedings{Xie2015,
    Title = {An empirical study of combining participatory and physical sensing to better understand and improve urban mobility networks},
    Author = {Xiao-Feng Xie and Zun-Jing Wang},
    Booktitle = {{Transportation Research Board (TRB) Annual Meeting}},
    number={15-3238},
    PDF={http://www.wiomax.com/team/xie/paper/TRB15LBSN.pdf},
    PPT={http://www.wiomax.com/team/xie/demo/TRB15_demo_BigData_UrbanInformatics.pdf},
    LNK={https://trid.trb.org/View/1337999},
    Year = {2015},
    Address = {Washington, DC}
    }
  • X. Xie and Z. Wang, “Combining physical and participatory sensing in urban mobility networks,” in Workshop on Big Data and Urban Informatics (BDUIC), Chicago, IL, 2014, pp. 859-876. [PDF] [Bibtex]
    @InProceedings{Xie2014d,
    Title = {Combining physical and participatory sensing in urban mobility networks},
    Author = {Xiao-Feng Xie and Zun-Jing Wang},
    Booktitle = {Workshop on Big Data and Urban Informatics (BDUIC)},
    Year = {2014},
    Pages = {859--876},
    PDF={http://www.wiomax.com/team/xie/paper/BDUIC14.pdf},
    Address = {Chicago, IL}
    }

TrafficTopic_ELBS MediumPitts_Topic_Traffic_Weekday_PM