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Accident Frequency Prediction Model for Flat Rural Roads in Serbia

Author

Listed:
  • Spasoje Mićić

    (Faculty of Transportaion, Pan-European University APEIRON, 78000 Banja Luka, Bosnia and Herzegovina)

  • Radoje Vujadinović

    (Faculty of Mechanical Engineering, University of Montenegro, 81000 Podgorica, Montenegro)

  • Goran Amidžić

    (Faculty of Security Science, University of Banja Luka, 78000 Banja Luka, Bosnia and Herzegovina)

  • Milanko Damjanović

    (Faculty of Mechanical Engineering, University of Montenegro, 81000 Podgorica, Montenegro)

  • Boško Matović

    (Faculty of Mechanical Engineering, University of Montenegro, 81000 Podgorica, Montenegro)

Abstract

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.

Suggested Citation

  • Spasoje Mićić & Radoje Vujadinović & Goran Amidžić & Milanko Damjanović & Boško Matović, 2022. "Accident Frequency Prediction Model for Flat Rural Roads in Serbia," Sustainability, MDPI, vol. 14(13), pages 1-14, June.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:13:p:7704-:d:846600
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    References listed on IDEAS

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    1. Lian Zhu & Linjun Lu & Wenying Zhang & Yurou Zhao & Meining Song, 2019. "Analysis of Accident Severity for Curved Roadways Based on Bayesian Networks," Sustainability, MDPI, vol. 11(8), pages 1-17, April.
    2. Vuong, Quang H, 1989. "Likelihood Ratio Tests for Model Selection and Non-nested Hypotheses," Econometrica, Econometric Society, vol. 57(2), pages 307-333, March.
    3. Longyu Shi & Nigar Huseynova & Bin Yang & Chunming Li & Lijie Gao, 2018. "A Cask Evaluation Model to Assess Safety in Chinese Rural Roads," Sustainability, MDPI, vol. 10(11), pages 1-16, October.
    4. Lord, Dominique & Mannering, Fred, 2010. "The statistical analysis of crash-frequency data: A review and assessment of methodological alternatives," Transportation Research Part A: Policy and Practice, Elsevier, vol. 44(5), pages 291-305, June.
    5. Thanapong Champahom & Sajjakaj Jomnonkwao & Chinnakrit Banyong & Watanya Nambulee & Ampol Karoonsoontawong & Vatanavongs Ratanavaraha, 2021. "Analysis of Crash Frequency and Crash Severity in Thailand: Hierarchical Structure Models Approach," Sustainability, MDPI, vol. 13(18), pages 1-19, September.
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