Count Data Modeling for Predicting Crash Severity on Indian Highways


  • Krantikumar V. Mhetre Department of Civil Engineering, COEP TECH University, Pune, Maharashtra, India
  • Aruna D. Thube Department of Civil Engineering, COEP TECH University, Pune, Maharashtra, India
Volume: 13 | Issue: 5 | Pages: 11816-11820 | October 2023 |


This study collected data on road accidents for the years 2016-2020 for the NH-48 highway in Maharashtra, India to model their conditions. Road crash data models were developed using 70% of actual data for training and 30% for testing purposes. Negative binomial regression modeling was used to predict crash fatalities. The results showed that the factors that affected the fatality of road crashes were head-on-collision, friction, time zone, and weather conditions of the crash. The developed models were validated and tested using log-likelihood, AIC, BIC, MAD, MSE, RMSE, and MAPE values. Head-on-collision, AM, PM, light rain, mist/fog, heavy rain, fine, and cloudy were positively associated with the fatality of road crashes, while friction was negatively associated. The developed models can be used to predict the fatality/non-fatality of road crashes and implement road safety strategies on highways to reduce them.


data, modeling, NH-48, road safety, India, crach


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How to Cite

K. V. Mhetre and A. D. Thube, “Count Data Modeling for Predicting Crash Severity on Indian Highways”, Eng. Technol. Appl. Sci. Res., vol. 13, no. 5, pp. 11816–11820, Oct. 2023.


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