Analyzing Correlated Road Accident Count Data Using Zero Truncated Bivariate Poisson Regression Model

Dhaka University Journal of Science Pub Date : 2024-03-25 DOI:10.3329/dujs.v72i1.71181

Trishna Saha, A. Sajib

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Abstract

This paper aims to determine the significant factors which influence two correlated count responses, namely the total number of cars involved in an accident and the total number of fatalities due to that accident, of United Kingdom (UK) road accident count data. The bivariate Poisson (BVP) of two different forms and zero truncated bivariate Poisson regression (ZTBVP) models are considered to analyze UK road accident count data and the best model is selected based on the AIC and BIC values. From the data analysis, it is observed that the ZTBVP model provides the best fit (AIC value: 20563.26) for the UK road accident count data compared to all two variants of the BVP model (AIC value: >20563.26). From the results obtained from ZTBVP model, it is also observed that sex of driver, area, serious severity, and light condition are the significant covariates for the total number of cars involved in an accident while area, fatal severity, serious severity, light condition and year 2021 are the significant covariates for the total number of fatalities due to that accident. Dhaka Univ. J. Sci. 72(1): 24-29, 2024 (January)

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使用零截断双变量泊松回归模型分析相关道路事故计数数据

本文旨在确定影响英国（UK）道路事故计数数据中两个相关计数响应（即事故车辆总数和事故死亡总人数）的重要因素。在分析英国道路事故计数数据时，考虑了两种不同形式的双变量泊松（BVP）模型和零截断双变量泊松回归（ZTBVP）模型，并根据 AIC 值和 BIC 值选出了最佳模型。从数据分析中可以看出，与 BVP 模型的所有两个变体（AIC 值：>20563.26）相比，ZTBVP 模型对英国道路事故数量数据的拟合效果最好（AIC 值：20563.26）。从 ZTBVP 模型得出的结果还可以看出，驾驶员性别、地区、严重程度和灯光条件是事故车辆总数的重要协变量，而地区、致命严重程度、严重程度、灯光条件和 2021 年是事故死亡总人数的重要协变量：24-29, 2024 (January)

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Dhaka University Journal of Science

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