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<p>Many state and local agencies’ mobility metrics are based upon selected spot speed studies, modeled segment speeds and modeled intersection delays. Field visits to collect speed and mobility data are expensive, time consuming and do not scale. Connected vehicle (CV) data is an emerging data source that provides richer, higher fidelity, scalable and timelier information. However, the sheer volume of CV data requires additional skills in processing “big data”. As a point of reference, data from vehicles travelling in the United States, from one original equipment manufacturer, is on the order of 500&#xa0;billion records per month. In its raw form, this data can be challenging for many state and local agencies to efficiently process and extract insightful information. This paper proposes a novel framework to derive mobility related attributes at 0.1-mile segment resolution. These attributes characterize speeds, travel time indexes and reliability with temporal variations. These techniques are applied to over 24,831 directional miles of Interstates, U.S. routes, and state routes in Indiana. Approximately 108&#xa0;billion CV records over a 7-month period from June 1 to December 31, 2024, were used to distill down to a system assessment database comprised of 248,314 rows and 317 parameters. The derived mobility metric dataset is also available through an open access data repository that can be easily ingested into any GIS platform. A discussion on agency use cases as well as how this data aided state agency staff in developing capital program recommendations is provided.</p>

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Deriving systemwide granular segment level traffic mobility performance metrics using connected vehicle data to identify improvement opportunities

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月26日

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  • Rahul Suryakant Sakhare,
  • Jairaj Desai,
  • Christopher Gartner,
  • Nathan Shellhamer,
  • Taylor Ruble,
  • Daniel McCoy,
  • Darcy M. Bullock

摘要

Many state and local agencies’ mobility metrics are based upon selected spot speed studies, modeled segment speeds and modeled intersection delays. Field visits to collect speed and mobility data are expensive, time consuming and do not scale. Connected vehicle (CV) data is an emerging data source that provides richer, higher fidelity, scalable and timelier information. However, the sheer volume of CV data requires additional skills in processing “big data”. As a point of reference, data from vehicles travelling in the United States, from one original equipment manufacturer, is on the order of 500 billion records per month. In its raw form, this data can be challenging for many state and local agencies to efficiently process and extract insightful information. This paper proposes a novel framework to derive mobility related attributes at 0.1-mile segment resolution. These attributes characterize speeds, travel time indexes and reliability with temporal variations. These techniques are applied to over 24,831 directional miles of Interstates, U.S. routes, and state routes in Indiana. Approximately 108 billion CV records over a 7-month period from June 1 to December 31, 2024, were used to distill down to a system assessment database comprised of 248,314 rows and 317 parameters. The derived mobility metric dataset is also available through an open access data repository that can be easily ingested into any GIS platform. A discussion on agency use cases as well as how this data aided state agency staff in developing capital program recommendations is provided.