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Utilizing Connected Vehicle Data to Identify Impacts of Congestion on Adjacent Agencies

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English


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  • Creators:
  • Corporate Creators:
    Purdue University. Lyles Department of Civil Engineering
  • Subject/TRT Terms:
  • DOI:
  • Resource Type:
    Journal Article
  • Right Statement:
    Open Access; https://creativecommons.org/licenses/by/4.0/
  • Geographical Coverage:
    United States
  • Corporate Publisher:
    Scientific Research Publishing
  • Abstract:
    Most state agencies have good practices for monitoring congestion on roadways within their jurisdiction. Many urbanized areas span state boundaries, where incidents, work zones, and congestion often impact neighboring states or municipalities. States with urbanized borders that experience congestion often develop informal communication procedures and occasionally share a few traffic management cameras. However, system integration to share between adjacent states can be challenging, particularly for work zones and incidents. Commercial connected vehicle data (CV) provides real-time probes for corridors and can be used to monitor traffic data either within a state or across the border into an adjacent state with no requirement for institutional interfaces. For example, Indiana has 15 interstate crossings into four different states. As a matter of practice, Indiana purchases connected vehicle data that extends 10 miles into all neighboring states. This provides an ability to monitor work zones, incidents, and re-occurring congestion in adjacent states that may impact Indiana, as well as understanding when Indiana freeway congestion impacts neighboring states. This paper presents case studies that explain how cross border queueing can be monitored and summarized over a year for the I-94, I-90, I-70, I-69, I-65, I-64, I-275 and I-265 Indiana Interstates. The inbound IL to IN segment of I-94 had the largest median hours of queuing with 69.1 hours per week. The outbound IN to KY segment of I-275 departing Indiana had the largest median hours of queuing with 19.5 hours per week.
  • Content Notes:
    This is an open access article under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) license https://creativecommons.org/licenses/by/4.0/. Please cite this article as: Driscoll, T.M., Sakhare, R.S., Desai, J. and Bullock, D.M. (2025) Utilizing Connected Vehicle Data to Identify Impacts of Congestion on Adjacent Agencies. Journal of Transportation Technologies , 15, 522-541. https://doi.org/10.4236/jtts.2025.154024
  • Format:
    PDF
  • Funding:
    69A3552348305
  • Collection(s):
  • Main Document Checksum:
    urn:sha-512:9e2a03265cdcda3ed048df1b031da8e4ed5e6bbaa65502289c72d2c6590254f49c1a147a4aea0c87ccb0fb583f5d4885131c9dd9480b61f25c9b96df5699f359
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  • File Type:
    Filetype[PDF - 8.53 MB ]
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