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Design Example: Alignment of a Road Line Using GIS01:17

Design Example: Alignment of a Road Line Using GIS

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The alignment of a road line using Geographic Information Systems (GIS) is a critical process in civil engineering, combining advanced technology with practical decision-making. This methodology begins with the collection of geospatial data, including information on land cover, geomorphology, drainage patterns, slope, and contour details. Such data is typically acquired through satellite imagery and GIS tools, offering a comprehensive understanding of the terrain.Once the data is gathered, it...
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Network-wide road crash risk screening: A new framework.

Michela Bonera1, Benedetto Barabino2, George Yannis3

  • 1Ufficio Studi, Ricerca e Sviluppo - Brescia Mobilità S.p.A., Brescia, Italy.

Accident; Analysis and Prevention
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Summary
This summary is machine-generated.

This study introduces a new framework for proactive road safety analysis. It quantifies crash risk by integrating probability, vulnerability, and exposure, enabling authorities to identify and prioritize critical road segments.

Keywords:
Crash exposureCrash probabilityCrash severityNetwork wide screeningRoad crash riskRoad infrastructure safety management

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Area of Science:

  • Transportation Engineering
  • Road Safety Management
  • Risk Assessment

Background:

  • Network-wide road crash risk screening is vital for road safety authorities globally.
  • Proactive screening methods to identify critical road segments before crashes occur are highly beneficial.
  • Existing research lacks a comprehensive quantification of road network crash risk considering probability, vulnerability, and exposure simultaneously.

Purpose of the Study:

  • To develop and validate a novel framework for quantifying and screening road network crash risk.
  • To integrate road safety factors, prediction models, and a risk-based method for comprehensive risk assessment.
  • To provide a tool for road safety authorities to proactively identify high-risk road segments.

Main Methods:

  • Developed a framework integrating road safety factors, prediction models, and a risk-based method.
  • Calculated risk value for each road segment based on crash occurrence probability, severity, and exposure.
  • Ranked and classified road segments into a five-level risk scale using real-world data.

Main Results:

  • Successfully integrated base map data, context information, traffic data, and five years of crash records.
  • Demonstrated the framework's capability to identify critical road segments on a non-urban network.
  • Generated a ranked list of road segments based on their quantified crash risk.

Conclusions:

  • The proposed framework offers a valid support for road safety authorities in identifying critical road segments.
  • It enables prioritization of interventions and potential improvement of road safety performance.
  • The framework is designed for incorporation into existing safety management systems.