The Use of Large Scale Datasets for Understanding Traffic Network State

The goal of this proposal is to develop novel modeling techniques to infer individual activity patterns from the large scale cell phone datasets and taxi data from New York City (NYC). As such large scale, disaggregate data provides a unique perspective to understand the complex interactions among human behavior, urban environments and traffic patterns. Urban development shapes the transportation systems, it determines what kind of transportation system a city has, and what does it look like. As an important dynamic component in urban systems, activities of transportation systems in turn capture the dynamics of the entire urban system and enhance knowledge about the complex urban system. This will ultimately contribute to the improvement of level of service and policy making on transportation systems. Taxi as a transportation tool has its unique characteristics. It is capable of capturing urban movement patterns both spatially and temporally since they serve as real‐time probes in the network. Moreover, one may examine the pulse of the city, the gap between supply and demand, real time road congestion and even more. On the other hand, accurate estimation and prediction of urban link travel times are important for improving urban traffic operations and identifying key bottlenecks in the traffic network. They can also benefit users by providing accurate travel time information, thereby allowing better route choice in the network and minimizing overall trip travel time. However, to accurately assess link travel times, it is important to have good real-time information from either in-road sensors such as loop detectors, microwave sensors, or roadside cameras, or mobile sensors (e.g. floating cars) or Global Positioning System (GPS) devices (e.g. cell phones). In most of these cases, only limited information is available related to speed or location, hence, one has to develop appropriate methodologies to accurately estimate the performance metric of interest at the link, path or network level. Taxis equipped with GPS units provide a significant amount of data over days and months thereby providing a rich source of data for estimating network wide performance metrics. However, currently there are limited methodologies making use of this new source of data to estimate link or path travel times in the urban network. Within this context, this study proposes a new method for estimating hourly urban link travel times using large-scale taxicab data with partial information. The taxicab data used in this research provides limited trip information, which only contains the origin and destination location coordinates, travel time and distance of a trip. However, the extensive amount of data records compensates for the incompleteness of the data and makes the link travel time estimation possible. A novel algorithm for estimating the link travel times is also proposed and tested in this research.

  • Record URL:
  • Supplemental Notes:
    • This document was sponsored by the U.S. Department of Transportation, University Transportation Centers Program. Cover title: The Use of Large-scale Datasets for Understanding Network State.
  • Corporate Authors:

    City College of New York of the City University of New York

    Department of Civil Engineering
    160 Convent Avenue
    New York, NY  United States  10031

    University Transportation Research Center

    City College of New York
    Marshak Hall, Suite 910, 160 Convent Avenue
    New York, NY  United States  10031

    Research and Innovative Technology Administration

    1200 New Jersey Avenue, SE
    Washington, DC  United States  20590
  • Authors:
    • Ukkusuri, Satish V
    • Kamga, Camille
    • Zhan, Xianyuan
    • Qian, Xinwu
  • Publication Date: 2013-9


  • English

Media Info

  • Media Type: Digital/other
  • Edition: Final Report
  • Features: Figures; References; Tables;
  • Pagination: 66p

Subject/Index Terms

Filing Info

  • Accession Number: 01516418
  • Record Type: Publication
  • Contract Numbers: 49111‐21‐22
  • Created Date: Feb 19 2014 11:41AM