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Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street
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Predicting expressway crash frequency using a random effect negative binomial model: A case study in China.

Zhuanglin Ma1, Honglu Zhang2, Steven I-Jy Chien3

  • 1School of Automobile, Chang'an University, Xi'an, Shaanxi, China.

Accident; Analysis and Prevention
|October 21, 2016
PubMed
Summary
This summary is machine-generated.

This study analyzed expressway crash data to identify key factors influencing accident frequency. Results show that longitudinal grade, road width, and grade-to-radius ratio significantly impact crashes, with a random effect model proving most effective.

Keywords:
Crash frequencyGoodness-of-fitNegative binomial modelPredictionRandom effects negative binomial model

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Area of Science:

  • Traffic Safety
  • Transportation Engineering
  • Road Safety Analysis

Background:

  • Road safety is a critical concern in transportation infrastructure.
  • Understanding factors influencing crash frequency is essential for effective mitigation strategies.

Purpose of the Study:

  • To investigate the relationship between crash frequency and potential influence factors on a Chinese expressway.
  • To compare the performance of different statistical models and segmentation methods for crash prediction.

Main Methods:

  • Analysis of 567 crash records from a 50km expressway (2006-2008).
  • Application of fixed-length and homogeneous longitudinal grade segmentation methods.
  • Development and comparison of negative binomial (NB) and random effect negative binomial (RENB) models using maximum likelihood estimation.

Main Results:

  • Longitudinal grade, road width, and the ratio of longitudinal grade to curve radius (RGR) were identified as significant factors affecting crash frequency.
  • The random effect negative binomial (RENB) model demonstrated superior performance compared to the standard negative binomial (NB) model.
  • The fixed-length segment method yielded better model performance than the homogeneous longitudinal grade segment method.

Conclusions:

  • Road geometry, specifically longitudinal grade and its interaction with curve radius, alongside road width, are crucial predictors of expressway crash frequency.
  • The RENB model offers enhanced accuracy for crash frequency prediction in road safety studies.
  • Employing a fixed-length segmentation approach improves the reliability of crash prediction models.