Big data’s implications for transportation operations : an exploration.

Details

• Creators:
  Burt, Matthew ; Cuddy, Matthew ; Razo, Michael
• Corporate Creators:
  John A. Volpe National Transportation Systems Center (U.S.)
• Subject/TRT Terms:
  • Intelligent transportation systems
  • Data analysis
  • Database management systems
  • Data collection
  • Transportation operations Big data
  • Connected vehicle
  • Crowdsourcing
  • Data capture
  • Cloud computing
• Publication/ Report Number:
  FHWA-JPO-14-157;DOT-VNTSC-FHWA-14-13
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Edition:
  White paper
• Corporate Publisher:
  United States. Department of Transportation. Intelligent Transportation Systems Joint Program Office
• Abstract:
  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: ; • 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. ; • Data management that features storage in decentralized and virtual locations (i.e., the cloud) and handles both structured and unstructured data. ; • 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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• Format:
  PDF
• Funding:
  HW4YA1
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/3542/dot_3542_DS1.pdf
• File Type:
  [PDF - 625.53 KB]
• Collection(s):
  Federal Highway Administration ; ITSJPO Digital Library
• Main Document Checksum:
  urn:sha-512:6f75cf3114d23053baae2fbf543b3767d00ce3ef1507421d7fb855abd046b03db5a2f95390d18bbee1d94bffb3927f205c427f5372e20ea4329f16fe50e67403