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Related Experiment Video

Updated: Nov 20, 2025

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Connected vehicle-based road safety information system (CROSS): Framework and evaluation.

Young Jo1, Jiyong Jang1, Subin Park1

  • 1Hanyang University Erica Campus, Department of Transportation and Logistics Engineering, 55 Hanyangdaehak-ro, Sangnok-gu, Ansan, 15588, Republic of Korea.

Accident; Analysis and Prevention
|January 19, 2021
PubMed
Summary
This summary is machine-generated.

Cooperative intelligent transportation systems (C-ITS) enable new road safety insights. Analyzing connected vehicle data with a crash potential index (CPI) effectively predicts crash risks, even with moderate market penetration.

Keywords:
Cooperative-intelligent transportation systems (C-ITS)Crash potential indexMicro traffic simulationRoad risk monitoringTraffic safety

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Area of Science:

  • Transportation Engineering
  • Traffic Safety
  • Intelligent Transportation Systems

Background:

  • Connected vehicle systems, or cooperative intelligent transportation systems (C-ITS), generate high-resolution data on vehicle maneuvers.
  • C-ITS facilitate vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications, sharing crucial traffic information.
  • A Korean freeway C-ITS pre-deployment project equipped vehicles with forward collision warning systems, enabling objective crash risk evaluation.

Purpose of the Study:

  • To develop a road safety information system using connected vehicle (CV) data.
  • To estimate individual vehicle crash risks via a crash potential index (CPI).
  • To establish a methodology for assessing road safety risks on freeways based on CV data analysis.

Main Methods:

  • Analysis of high-resolution maneuvering data from connected vehicles.
  • Development and application of a crash potential index (CPI) to estimate individual vehicle crash risks.
  • Microscopic traffic simulation (VISSIM) to assess the impact of market penetration rate (MPR) on the proposed system's feasibility.

Main Results:

  • High CPIs were identified in critical areas: toll plazas, recurrent congestion zones, and on/off-ramps.
  • A statistically significant correlation was found between the estimated CPI and actual crash frequencies.
  • Traffic simulations indicated that 30% CV market penetration could achieve safety evaluation comparable to 100% penetration.

Conclusions:

  • The developed road safety information system effectively estimates crash risks using CV data.
  • The CPI methodology provides a scientifically valid approach to assessing freeway safety.
  • The study demonstrates the feasibility and efficiency of CV data for proactive road risk monitoring, even at lower market penetration rates.