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:
  Cloud Computing Connected Vehicle Crowdsourcing Data Analysis Database Management Systems Data Capture Data Collection Intelligent Transportation Systems Transportation Operations Big Data
• 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.
  
  
• Format:
  PDF
• Funding:
  HW4YA1
• Collection(s):
  ITSJPO Digital Library Federal Highway Administration
• Main Document Checksum:
  urn:sha-512:6f75cf3114d23053baae2fbf543b3767d00ce3ef1507421d7fb855abd046b03db5a2f95390d18bbee1d94bffb3927f205c427f5372e20ea4329f16fe50e67403
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/3542/dot_3542_DS1.pdf
• File Type:
  [PDF - 625.53 KB ]