Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: May 13, 2026

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation
11:41

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation

Published on: February 1, 2020

A crash-prediction model for road tunnels.

Ciro Caliendo1, Maria Luisa De Guglielmo, Maurizio Guida

  • 1Department of Civil Engineering, University of Salerno, 84084 Fisciano (SA), Italy. ccaliendo@unisa.it

Accident; Analysis and Prevention
|March 26, 2013
PubMed
Summary

This study developed crash prediction models for Italian road tunnels, finding tunnel length, traffic volume, truck percentage, and lane number significantly impact accident severity. A reduction in severe crashes over time was also observed.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Correction: Cevenini et al. Lytic or Latent Phase in Human Cytomegalovirus Infection: An Epigenetic Trigger. <i>Int. J. Mol. Sci.</i> 2025, <i>26</i>, 11554.

International journal of molecular sciences·2026
Same author

Non-invasive cell-free DNA-based approach for the diagnosis of aneuploidies in spontaneous abortion.

Fetal diagnosis and therapy·2026
Same author

Human papillomavirus persistence and clearance in women living with HIV: A five-year retrospective analysis from an Italian university center.

Acta obstetricia et gynecologica Scandinavica·2026
Same author

Cytomegalovirus in pregnancy: 20-year experience of a southern italy centre on diagnosis, prenatal surveillance, and neonatal outcomes.

European journal of obstetrics, gynecology, and reproductive biology·2026
Same author

HIV infection and preeclampsia: reconciling divergent findings across ART eras and study designs.

American journal of obstetrics & gynecology MFM·2026
Same author

Prenatal ultrasound diagnosis, intrauterine monitoring and postnatal management of a giant fetal abdominopelvic lymphangioma: a case report and scoping review.

Frontiers in pediatrics·2026

Area of Science:

  • Transportation Engineering
  • Traffic Safety
  • Statistical Modeling

Background:

  • Limited research exists on crash prediction models specifically for road tunnels, particularly in the Italian context.
  • Existing studies show varying results regarding the influence of traffic and geometric factors on tunnel crashes.
  • Driving behavior and tunnel conditions in previous research often differ from those in Italy.

Purpose of the Study:

  • To develop novel crash prediction models for Italian road tunnels.
  • To identify key factors influencing both non-severe and severe crash occurrences in tunnels.
  • To analyze the temporal trends in severe crash rates within Italian road tunnels.

Main Methods:

  • Utilized a 4-year monitoring period (2006-2009) for single-tube, unidirectional traffic tunnels.

Related Experiment Videos

Last Updated: May 13, 2026

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation
11:41

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation

Published on: February 1, 2020

  • Employed the Bivariate Negative Binomial regression model for joint analysis of non-severe and severe crashes.
  • Applied Random Effects Binomial and Negative Multinomial regression models to assess the year effect on severe crashes.
  • Estimated regression parameters using the Maximum Likelihood Method and validated model adequacy with the Cumulative Residual Method.

    • Main Results:

    • Tunnel length (L), annual average daily traffic per lane (AADTL), percentage of trucks (%Tr), and number of lanes (NL) were significant predictors for both non-severe and severe crashes.
    • A statistically significant reduction in severe crashes over the study period was detected.
    • The models demonstrated the influence of specific tunnel characteristics and traffic conditions on accident frequency and severity.

    Conclusions:

    • The developed crash prediction models provide valuable insights into road tunnel safety in Italy.
    • Findings can inform tunnel safety improvements, traffic control modifications, and future tunnel design comparisons.
    • The study highlights the importance of considering tunnel-specific factors in safety analysis and risk assessment.