Antoniou Constantinos, Yannis George, Papadimitriou Eleonora, Lassarre Sylvain
National Technical University of Athens, Greece.
National Technical University of Athens, Greece.
Accid Anal Prev. 2016 Jul;92:89-96. doi: 10.1016/j.aap.2016.03.025. Epub 2016 Apr 4.
Modeling road safety development can provide important insight into policies for the reduction of traffic fatalities. In order to achieve this goal, both the quantifiable impact of specific parameters, as well as the underlying trends that cannot always be measured or observed, need to be considered. One of the key relationships in road safety links fatalities with risk and exposure, where exposure reflects the amount of travel, which in turn translates to how much travelers are exposed to risk. In general two economic variables: GDP and unemployment rate are selected to analyse the statistical relationships with some indicators of road accident fatality risk. The objective of this research is to provide an overview of relevant literature on the topic and outline some recent developments in macro-panel data analysis that have resulted in ongoing research that has the potential to improve our ability to forecast traffic fatality trends, especially under turbulent financial situations. For this analysis, time series of the number of fatalities and GDP in 30 European countries for a period of 38 years (1975-2012) are used. This process relies on estimating long-term models (as captured by long term time-series models, which model each country separately). Based on these developments, utilizing state-of-the-art modelling and analysis techniques such as the Common Correlated Effects Mean Group estimator (Pesaran), the long-term elasticity mean value equals 0.63, and is significantly different from zero for 10 countries only. When we take away the countries, where the number of fatalities is stationary, the average elasticity takes a higher value of nearly 1. This shows the strong sensitivity of the estimate of the average elasticity over a panel of European countries and underlines the necessity to be aware of the underlying nature of the time series, to get a suitable regression model.
对道路安全发展进行建模可以为减少交通死亡人数的政策提供重要见解。为了实现这一目标，需要考虑特定参数的可量化影响以及那些并非总能测量或观察到的潜在趋势。道路安全中的关键关系之一是将死亡人数与风险和暴露联系起来，其中暴露反映了出行量，进而转化为旅行者面临风险的程度。一般选择两个经济变量：国内生产总值（GDP）和失业率，来分析它们与道路交通事故死亡风险的一些指标之间的统计关系。本研究的目的是概述该主题的相关文献，并概述宏观面板数据分析中的一些最新进展，这些进展促成了正在进行的研究，这些研究有可能提高我们预测交通死亡趋势的能力，尤其是在动荡的金融形势下。对于此分析，使用了30个欧洲国家在38年期间（1975 - 2012年）的死亡人数和GDP的时间序列。这个过程依赖于估计长期模型（如长期时间序列模型所描述的，该模型对每个国家分别进行建模）。基于这些进展，利用诸如共同相关效应均值组估计器（佩萨兰）等最先进的建模和分析技术，长期弹性均值等于0.63，并且仅对10个国家而言显著不为零。当我们去除死亡人数平稳的国家时，平均弹性会取一个更高的值，接近1。这表明在一组欧洲国家中平均弹性估计具有很强的敏感性，并强调了了解时间序列基本性质以获得合适回归模型的必要性。
