Big Data’s Implications for Transportation Operations: An Exploration

The purpose of this white paper is to expand the understanding of big data for transportation operations, the value it could provide, and the implications for the future direction of the U.S. Department of Transportation (USDOT) Connected Vehicle Real-Time Data Capture and Management (DCM) Program. Big data is an approach to generating knowledge in which a number of advanced techniques are applied to the capture, management and analysis of very large and diverse volumes of data – data so large, so varied and analyzed at such speed that it exceeds the capabilities of traditional data management and analysis tools. This paper is not intended as a primer or “how to” on big data, per se, but rather is intended to explore the potential value of big data approaches in a future connected vehicle environment. Big data is a process of knowledge generation that features the following approaches: (1) Data capture that includes massive datasets encompassing all or most of the population being studied (as opposed to small samples); use of data from both purpose-specific and repurposed data collection; and utilization of crowdsourced and “electronic breadcrumb” data. (2) Data management that features storage in decentralized and virtual locations (i.e., the cloud) and handles both structured and unstructured data. (3) Data analysis that is often automated, with computers doing more of the work to find complex patterns among a large number of variables. Big data approaches are needed to contend with the coming volume of connected vehicle and traveler data, to: enable a wide range of new strategies that are expected to provide safety, mobility and environmental benefits, and reduce the need for traditional data collection mechanisms (e.g., connected vehicle probes replacing traffic detectors). This paper identifies two additional, broad areas where big data analytical approaches may be able to provide further value: 1) Transportation System Monitoring & Management; and 2) Traveler-Centered Transportation Strategies.

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• Record URL:
  https://rosap.ntl.bts.gov/view/dot/3542
• Corporate Authors:

  Volpe National Transportation Systems Center

  Cambridge, MA  United States  02142

  Department of Transportation

  Intelligent Transportation Systems Joint Program Office
  1200 New Jersey Avenue, SE
  Washington, DC  United States  20590
• Authors:
  • Burt, Matthew
  • Cuddy, Matthew
  • Razo, Michael
• Publication Date: 2014-12

Language

• English

Media Info

• Media Type: Digital/other
• Edition: White Paper
• Features: Appendices; Figures; References; Tables;
• Pagination: 56p

Subject/Index Terms

• TRT Terms: Data analysis; Data collection; Data management; Intelligent transportation systems; Mobile communication systems; Real time information
• Subject Areas: Data and Information Technology; Highways; Planning and Forecasting; I72: Traffic and Transport Planning;

Filing Info

• Accession Number: 01576249
• Record Type: Publication
• Report/Paper Numbers: FHWA-JPO-14-157, VNTSC-FHWA-14-13
• Files: NTL, TRIS, RITA, ATRI, USDOT
• Created Date: Sep 25 2015 4:17PM