Almutairi Omar
Civil Engineering Department, College of Engineering, Al Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia.
Heliyon. 2024 Mar 28;10(7):e28900. doi: 10.1016/j.heliyon.2024.e28900. eCollection 2024 Apr 15.
In this study, a nested grouped random parameter negative binomial framework is proposed to model crash counts at the segment level, a three-level longitudinal framework. The proposed model accounts for correlations along county routes and over time and thus includes a time variable, the year index, to analyze crash counts. The model is applied to crashes on undivided two-lane arterial roads in Ohio from 2012 to 2017. The results present two variants of the model: one with varying intercepts and fixed slopes and the other with varying intercepts and slopes. Both variants have comparable interpretations concerning the fixed parameters, but the latter variant exhibits a significantly improved fit and provides additional information on the interpretations. The results show a significant quadratic relationship between the time variable and the crash count, indicating that, on average, the crash count of segments increases with a decreasing rate as time variable increases. Regarding random parameters, the findings show that 17% of segments within routes and 2% of routes exhibit crash counts that decrease at accelerating downward trend as time variable increases. The effect of the natural logarithm of the segment length varies significantly across different routes, with an increase in this value primarily leading to an increase in crashes. On the other hand, the effect of the total shoulder width also varies across routes, but unlike the former, an increase in this value generally results in a decrease in crashes. The proposed model shows high forecast accuracy for crash count prediction, making it a valuable tool for informed decision-making in safety improvement.
在本研究中，提出了一种嵌套分组随机参数负二项式框架，用于对路段层面的碰撞次数进行建模，这是一个三级纵向框架。所提出的模型考虑了沿县级道路以及随时间的相关性，因此包含一个时间变量，即年份索引，以分析碰撞次数。该模型应用于2012年至2017年俄亥俄州未分隔的双车道主干道上的碰撞事故。结果展示了该模型的两种变体：一种是截距可变而斜率固定，另一种是截距和斜率均可变。两种变体对于固定参数具有可比的解释，但后一种变体显示出显著改善的拟合效果，并在解释方面提供了更多信息。结果表明时间变量与碰撞次数之间存在显著的二次关系，这表明，平均而言，随着时间变量的增加，路段的碰撞次数以递减的速率增加。关于随机参数，研究结果表明，路线内17%的路段和2%的路线呈现出随着时间变量增加碰撞次数以加速下降趋势减少的情况。路段长度的自然对数的影响在不同路线间差异显著，该值的增加主要导致碰撞次数增加。另一方面，总路肩宽度的影响在不同路线间也有所不同，但与前者不同的是，该值的增加通常会导致碰撞次数减少。所提出的模型在碰撞次数预测方面显示出较高的预测准确性，使其成为安全改进中明智决策的宝贵工具。
