Investigation of factors affecting the duration of full-lane-closure accidents on the freeway

Research output: Contribution to journalArticlepeer-review

Abstract

A full-lane-closure accident refers to a traffic accident in which one or both directions of a freeway are completely obstructed, thus resulting in severe traffic congestion. Traffic operators must promptly manage the accident and predict its duration. This study aims to analyze the accident duration of a full-lane-closure accident and identify the factors affecting the accident duration based on crash reports from 2013 to 2016. The accident duration must only be predicted using information acquired at the site immediately after the accident. Therefore, the variables used in the prediction model are selected only for information obtained from the time of the accident. An ordered probit model is used to identify the magnitudes of the influencing variables. Seven variables, including accident location, time of day, post-accident vehicle condition, and number of vehicles involved in the accident, are selected to predict the accident duration. In addition, the marginal effects of each independent variable are analyzed. The findings of this study can aid freeway management agencies in establishing prompt accident-treatment measures for each stage of accident clearance and recovery to ease traffic congestion and prevent secondary accidents, and the results are expected to facilitate improvements to the safety of road users by alleviating the risk of secondary accidents.

Original languageEnglish
Article number100343
JournalKSCE Journal of Civil Engineering
Volume30
Issue number2
DOIs
StatePublished - Feb 2026

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being
  2. SDG 9 - Industry, Innovation, and Infrastructure
    SDG 9 Industry, Innovation, and Infrastructure

Keywords

  • Accident duration
  • Full-lane-closure accident
  • Influential factor
  • Marginal effects
  • Ordered probit model

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