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首页> 外文期刊>Transport policy >Explanatory and prediction power of two macro models. An application to van-involved accidents in Spain
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Explanatory and prediction power of two macro models. An application to van-involved accidents in Spain

机译：两种宏观模型的解释力和预测力。在西班牙发生的面包车事故中的应用

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The figures representing road safety in Spain have substantially improved during the last decade. However, the severity indicators concerning vans have not improved as favorably as those of other types of vehicles, such as passenger cars and heavy freight transport vehicles. This study is intended to analyze the main factors explaining van accident behavior and to get a further insight into dynamic macro models for road accidents. For this purpose we are using four time series related to the frequency and severity of van accidents on Spanish roads and two types of methodologies applied in the study of traffic accidents: linear regression with Box-Cox transformed variables and autoregressive errors (DRAG), and an unobserved components model (UCM). The four response time series modeled are the number of fatal accidents, the number of accidents with seriously injured victims, the number of fatalities and the number of seriously injured victims. Since the choice of the appropriate macro model for the analysis of road traffic accidents is not a trivial matter, we are considering multiple factors such as goodness of fit and interpretation, as well as the prediction accuracy in order to choose the best model. Overall, the final results make sense and agree with the literature as far as the elasticities and coefficient signs are concerned. It was found that the DRAG-type model yields slightly better predictions for all four models compared to UCM. With these macroeconomic models, the effect of some influential factors (fleet, drivers, exposure variables, economic factors, as well as legislative actions) can be addressed. Estimating the effect of the vigilance and surveillance actions can help safety authorities in their policy evaluation and in the allocation of resources.
机译：在过去十年中，代表西班牙道路安全的数字已大大改善。但是，关于货车的严重性指标并未像其他类型的车辆（如乘用车和重型货运车辆）那样明显改善。本研究旨在分析解释货车事故行为的主要因素，并进一步了解道路交通事故的动态宏模型。为此，我们使用与西班牙道路上的货车事故发生频率和严重程度相关的四个时间序列，以及在交通事故研究中应用的两种方法：Box-Cox变换变量和自回归误差（DRAG）的线性回归；以及未观察到的组件模型（UCM）。建模的四个响应时间序列分别是致命事故的数量，受重伤的事故的数量，死亡人数和受重伤的人数。由于选择合适的宏观模型来分析道路交通事故不是一件容易的事，因此，为了选择最佳模型，我们正在考虑多种因素，例如拟合优度和解释性以及预测准确性。总体而言，就弹性和系数符号而言，最终结果是有意义的并与文献一致。已发现，与UCM相比，DRAG类型的模型对所有四个模型的预测都稍好。使用这些宏观经济模型，可以解决某些影响因素（车队，驱动因素，风险敞口变量，经济因素以及立法措施）的影响。评估警戒和监视措施的效果可以帮助安全机构进行政策评估和资源分配。

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