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Table 1 Summary of some previous studies to analyzing accident data

From: Analysis of injuries and deaths from road traffic accidents in Iran: bivariate regression approach

Model type

Year

Conclusion

A joint model with Weighted risk score to combine crash count and crash severity [29]

2020

using of crash severity and crash count amended the accuracy of prediction model

A bivariate Bayesian hierarchical extreme value model for traffic conflict-based crash estimation [30]

2020

The bivariate model estimate regression coefficients more precisely than univariate models

Bayesian multivariate hierarchical spatial joint model [31]

2018

This model has a better fit for the crash data compared to the univariate alternative model

Copula-Based Joint Model of Injury Severity and Vehicle Damage in Two-Vehicle Crashes [32]

2015

On the basis of goodness-of-fit statistics, the Gaussian copula model that was calculated interrelationships between injury severity and vehicle damage was suitable

using the random-parameters tobit model for factors affecting highway accident rates [33]

2012

The empirical results show that this model was proper fit to the data

A joint-probability approach to crash prediction models [34]

2011

Joint probability model that modeled Crash occurrence and severity simultaneously, shown the good fit for data

Multivariate Poisson-Lognormal Models for Jointly Modeling Crash Frequency by Severity [35]

2007

The results show multivariate model that accounted correlation of variables, was achieved more accurate estimates