Post-event Connected Vehicle Data Exploration - Lessons Learned
Advanced Search
Select up to three search categories and corresponding keywords using the fields to the right. Refer to the Help section for more detailed instructions.

Search our Collections & Repository

For very narrow results

When looking for a specific result

Best used for discovery & interchangable words

Recommended to be used in conjunction with other fields

Dates

to

Document Data
Library
People
Clear All
Clear All

For additional assistance using the Custom Query please check out our Help Page

i

Post-event Connected Vehicle Data Exploration - Lessons Learned

Filetype[PDF-1018.49 KB]


English
Details You May Also Like

Details:

  • Creators:
  • Corporate Creators:
    United States. Department of Transportation. Federal Highway Administration. Office of Highway Policy Information
  • Corporate Contributors:
    United States. Department of Transportation. Federal Highway Administration. Office of Policy and Governmental Affairs
  • Subject/TRT Terms:
  • Publication/ Report Number:
    FHWA-HPL-24-012
  • Resource Type:
    Tech Report
  • Geographical Coverage:
    United States
  • Edition:
    Technical Illustration
  • Corporate Publisher:
    United States. Federal Highway Administration. Office of Policy and Governmental Affairs
  • Abstract:
    Traditional traffic monitoring sensors on roadways provide valuable information about travel demands and patterns. However, they do not capture detailed data on how vehicles are driven and interact with the road and the environment. To address this gap, the Office of Highway Policy Information in the FHWA has explored connected vehicle (CV) data from Wejo and the US DOT JPO Connected Vehicle pilot project. The CV data analysis is termed post-CV data analysis as the analysis is not done in site and in real time vehicles are traveling. Through this exploration, a wealth of highly desired information has been extracted from the post-CV data. This includes data on hard vehicle acceleration and deceleration accompanied by specific geospatial locations on the roadway, vehicle speed, seat belt usage, and windshield wiper status. The geolocation data is particularly significant as it helps identify areas where potential geometric and pavement inadequacies may exist. The availability of seat belt information, including when and under what conditions they are buckled, is unprecedented. The pos-CV data offers not only the distance vehicle travelers buckled but also the length of time travelers buckled along with microlevel information on when and where buckle/unbuckling occurred during their journeys. To effectively utilize post-CV data, it is crucial to have a suitable platform for data storage, access, and analytics. The choice of a data platform should be based on the programming language it supports and the expertise of an organization's analysts. Given the size of the data and the potential presence of Personal Identifiable Information (PII), the focus should be on accessing and utilizing the data rather than owning it. From a cost perspective, accessing the data is more economical than owning it. It is important to acknowledge that CV data may have quality issues. While they are primarily machine-generated, they are still prone to errors. Analysts are advised to perform data quality checks before utilizing the data to ensure its reliability and accuracy. The present paper provides an overview on post-CV data analysis related issues including its significant and unprecedent value, tools needed, expertise desired, and the awareness of potential data quality present. The goal of this paper is to encourage team and cooperative effort in acquiring and utilizing CV data to facilitate strategies for a safe and efficient highway travel.
  • Format:
    PDF
  • Collection(s):
  • Main Document Checksum:
  • Download URL:
  • File Type:

Supporting Files

  • No Additional Files
More +

You May Also Like

Checkout today's featured content at rosap.ntl.bts.gov