Accident Frequency Prediction Model for Flat Rural Roads in Serbia

Traffic accidents, by their nature, are random events; therefore, it is difficult to estimate the exact places and times of their occurrences and the true nature of their impacts. Although they are hard to precisely predict, preventative actions can be taken and their numbers (in a certain period) c...

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Bibliographic Details
Published in:Sustainability 2022-07, Vol.14 (13), p.7704
Main Authors: Mićić, Spasoje, Vujadinović, Radoje, Amidžić, Goran, Damjanović, Milanko, Matović, Boško
Format: Article
Language:English
Subjects:
Access roads
Accident data
Accident prediction
Data models
Fatalities
Goodness of fit
Mathematical models
Prediction models
Regression analysis
Roads
Roads & highways
Rural areas
Statistical analysis
Statistical models
Sustainability
Traffic accidents
Traffic accidents & safety
Traffic congestion
Traffic flow
Variables
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Summary:Traffic accidents, by their nature, are random events; therefore, it is difficult to estimate the exact places and times of their occurrences and the true nature of their impacts. Although they are hard to precisely predict, preventative actions can be taken and their numbers (in a certain period) can be approximately predicted. In this study, we investigated the relationship between accident frequency and factors that affect accident frequency; we used accident data for events that occurred on a flat rural state road in Serbia. The analysis was conducted using five statistical models, i.e., Poisson, negative binomial, random effect negative binomial, zero-inflated Poisson, and zero-inflated negative binomial models. The results indicated that the random effect negative binomial model outperformed the other models in terms of goodness-of-fit measures; it was chosen as the accident prediction model for flat rural roads. Four explanatory variables—annual average daily traffic, segment length, number of horizontal curves, and access road density—were found to significantly affect accident frequency. The results of this research can help road authorities make decisions about interventions and investments in road networks, designing new roads, and reconstructing existing roads.
ISSN:2071-1050
2071-1050
DOI:10.3390/su14137704