Open Access
Confidence and prediction intervals for generalised linear accident models
Graham Wood
- pp 1-15
TLDR
In this article, the authors describe how confidence intervals (for example, for the true accident rate at given flows) and prediction intervals can be produced using spreadsheet technology, which can be used for estimating the number of accidents at a new site with given flows.Abstract:
Generalised linear models, with "log" link and either Poisson or negative binomial errors, are commonly used for relating accident rates to explanatory variables. This paper adds to the toolkit for such models. It describes how confidence intervals (for example, for the true accident rate at given flows) and prediction intervals (for example, for the number of accidents at a new site with given flows) can be produced using spreadsheet technology.read more
Citations
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Methodology for fitting and updating predictive accident models with trend [forthcoming]
TL;DR: This paper addresses a number of methodological issues that arise in seeking practical and efficient ways to update PAMs, whether by re-calibration or byRe-fitting, including the choice of distributional assumption for overdispersion, and considerations about the most efficient and convenient ways to fit the required models.
Dissertation
Development Of Relationships Among Vehicular And Driver’s Characteristics With Traffic Accidents
TL;DR: It is concluded that by properly addressing the vehicular and driver’s characteristics, incidents of traffic accidents can be reduced and developed Accident Prediction Models (APMs) relating number of accidents occurred per year are developed.
81.The Bootstrap Maximum Likelihood Method for Estimation ofDispersion Parameter in the Negative Binomial Regression Model
Abstract: Estimation of dispersion parameter in the negative binomial regression model plays an important role in various types of count data analysis. For this purpose, the maximum likelihood method is often used. However, it has been reported in the literature that the dispersion parameter can be misestimated when using the maximum likelihood method for small sample size. In addition, several researchers recommended that disregarding important covariates maybe significantly affected on dispersion estimator. This study extends the bootstrap maximum likelihood method for estimation of dispersion parameter in the negative binomial regression model with covariates for small sample size. In order to evaluate performances of the estimators in the sense of root mean square error, a Monte Carlo simulation study is given. Furthermore, a real dataset is provided to illustrate some of the simulation results.
Journal ArticleDOI
Analysis of Railroad Accident Prediction using Zero-truncated Negative Binomial Regression and Artificial Neural Network Model: A Case Study of National Railroad in South Korea
Journal ArticleDOI
Quantitative analysis of freight train derailment severity with structured and unstructured data
TL;DR: In this article , a statistical model that integrates both structured and unstructured data was established to analyze U.S. freight train derailments from 1996 to 2019, and the comparative results of predictions revealed that the model with combined text information outperformed the one without the data.
References
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Journal ArticleDOI
An Introduction to Generalized Linear Models.
M. J. Campbell,Annette J. Dobson +1 more
Modeling Motor Vehicle Crashes Using Poisson-Gamma Models: Examining Effects of Low Sample Mean Values and Small Sample Size on Estimation of Fixed Dispersion Parameter
TL;DR: The study shows that a low sample mean combined with a small sample size can seriously affect the estimation of the dispersion parameter, no matter which estimator is used within the estimation process.
Journal ArticleDOI
A comprehensive methodology for the fitting of predictive accident models
Michael J. Maher,Ian Summersgill +1 more
TL;DR: This paper describes the form of the TRL studies and the model-fitting procedures used, and gives examples of the models which have been developed, and constitutes a comprehensive methodology for the development of predictive accident models.
Journal Article