A systematic unified approach for addressing temporal instability in road safety analysis

IF 12.5 1区 工程技术 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH
Kazi Redwan Shabab , Tanmoy Bhowmik , Mohamed H. Zaki , Naveen Eluru
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引用次数: 0

Abstract

Multivariate models are widely employed for crash frequency analysis in traffic safety literature. In the context of analyzing data for multiple instances (such as years), it becomes essential to evaluate the stability of parameters over time. The current research proposes a novel approach, labelled the mixed spline indicator pooled model, that offers significant enhancement relative to current approaches employed for capturing temporal instability. The proposed approach entails carefully creating independent variables that allow us to measure parameter slope changes over time and can be easily integrated into existing methodological frameworks. The current research effort compares four multivariate model systems: year specific negative binomial model, year indicator pooled model, spline indicator pooled model, and mixed spline indicator pooled model. The model performance is compared using log-likelihood and Bayesian Information Criterion. The empirical analysis is conducted using the Traffic Analysis Zone (TAZ) level crash severity records from Central Florida for the years from 2011 to 2019. The comparison results indicate that the proposed mixed spline indicator pooled model outperforms the other models providing superior data fit while optimizing the number of parameters. The proposed mixed spline model can allow a piece-wise linear functional form for the parameter and is suitable to forecast crashes for future years as illustrated in our predictive performance analysis.

解决道路安全分析中时间不稳定性的系统统一方法
在交通安全文献中,碰撞频率分析广泛采用多变量模型。在分析多个实例(如年份)的数据时,评估参数随时间变化的稳定性变得至关重要。目前的研究提出了一种名为混合样条指标集合模型的新方法,与目前用于捕捉时间不稳定性的方法相比,该方法具有显著的优势。建议的方法需要精心创建独立变量,使我们能够测量参数斜率随时间的变化,并可轻松集成到现有的方法框架中。目前的研究工作比较了四种多元模型系统:特定年份负二项模型、年份指标集合模型、样条指标集合模型和混合样条指标集合模型。使用对数似然法和贝叶斯信息准则对模型性能进行比较。实证分析使用了佛罗里达州中部 2011 年至 2019 年的交通分析区(TAZ)级碰撞严重程度记录。比较结果表明,所提出的混合样条指标集合模型优于其他模型,在优化参数数量的同时,还提供了更优越的数据拟合。正如我们的预测性能分析所示,建议的混合样条线模型可允许参数采用片断线性函数形式,适合预测未来几年的碰撞事故。
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来源期刊
CiteScore
22.10
自引率
34.10%
发文量
35
审稿时长
24 days
期刊介绍: Analytic Methods in Accident Research is a journal that publishes articles related to the development and application of advanced statistical and econometric methods in studying vehicle crashes and other accidents. The journal aims to demonstrate how these innovative approaches can provide new insights into the factors influencing the occurrence and severity of accidents, thereby offering guidance for implementing appropriate preventive measures. While the journal primarily focuses on the analytic approach, it also accepts articles covering various aspects of transportation safety (such as road, pedestrian, air, rail, and water safety), construction safety, and other areas where human behavior, machine failures, or system failures lead to property damage or bodily harm.
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