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The hazard rate, also known as the hazard function or failure rate, is a statistical measure used to describe the instantaneous rate at which an event occurs, given that the event has not yet happened. From a probabilistic perspective, it represents the likelihood that a subject will experience the event in a very small time interval, conditional on surviving up to the beginning of that interval. In terms of frequency, the hazard rate can be viewed as the ratio of the number of events to the...
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Crash Frequency Analysis Using Hurdle Models with Random Effects Considering Short-Term Panel Data.

Feng Chen1, Xiaoxiang Ma2, Suren Chen3

  • 1Department of Traffic Engineering and Key Laboratory of Road & Traffic Engineering of the Ministry of Education, Tongji University, 4800 Cao'an Road, Shanghai 201804, China. fengchen@tongji.edu.cn.

International Journal of Environmental Research and Public Health
|October 30, 2016
PubMed
Summary
This summary is machine-generated.

This study developed a new model to predict daily traffic crashes on mountainous highways using real-time weather and road data. The random effect hurdle negative binomial model accurately captures crash frequency, improving road safety analysis.

Keywords:
daily crash frequencyhurdle negative binomialpanel datarandom effectshort-term driving environment

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Area of Science:

  • Transportation Engineering
  • Statistical Modeling
  • Road Safety

Background:

  • Analyzing daily crash frequency on mountainous highways presents challenges due to complex factors like weather, traffic, and road conditions.
  • Existing models often struggle with the characteristics of crash data, such as excess zeros and unobserved heterogeneity.

Purpose of the Study:

  • To develop and validate a robust statistical model for predicting daily crash frequency on a specific mountainous highway section.
  • To incorporate real-time traffic, weather, and road surface data into crash frequency analysis.
  • To identify key factors influencing crash occurrence in challenging road environments.

Main Methods:

  • Utilized random effect panel data hurdle models, specifically the random effect hurdle negative binomial (REHNB) model.
  • Integrated data from the Road Weather Information System (RWIS) including traffic, weather, and road surface conditions.
  • Compared the REHNB model against three other competing models using statistical tests.

Main Results:

  • The REHNB model was identified as the most appropriate for analyzing daily crash frequency on mountainous highways.
  • Confirmed that over-dispersion in crash data is caused by both excess zeros and unobserved heterogeneity.
  • Demonstrated the significant impact of time-varying factors like weather, road surface, and traffic conditions on crash frequency.

Conclusions:

  • The REHNB model effectively addresses the complexities of crash data, including serial correlation and unbalanced panels.
  • The findings highlight the critical role of real-time environmental and traffic data in short-term crash prediction.
  • The developed methodology offers significant potential for practical engineering applications in enhancing road safety through accessible field monitoring data.