修正Heston随机波动率模型预测地方机动车碰撞率：华盛顿特区案例研究
Amending the Heston Stochastic Volatility Model to Forecast Local Motor Vehicle Crash Rates: A Case Study of Washington, D.C
摘要：
对城市地区的碰撞率进行建模需要大量关于历史和主要交通量以及碰撞事件和特征的数据。假设城市交通网络的交通量主要由典型的工作和学校通勤模式定义，则可以以合理的准确度确定碰撞率。然而，这一过程变得更加复杂的一个地区，经常受到高峰和低谷的交通量和碰撞事件，由于外源性事件（例如，极端天气），而不是典型的通勤模式。华盛顿特区是特别容易受到外部事件影响的地区之一，2009年至2020年间，该市的撞车事件大幅增加。在这项研究中，我们采用了一种预测模型，嵌入异质性和时间不稳定性的估计，以改善目前用于交通和道路安全研究的预测模型。具体来说，我们引入了一个随机波动模型，旨在捕捉与华盛顿特区的崩溃率的细微差别。我们确定该模型的表现优于传统预测模型，但鉴于整个COVID-19大流行期间表现出的独特旅行模式，该模型的表现并不理想。尽管如此，它的适应性华盛顿特区的特质，车祸率表明其能够准确地模拟本地化的碰撞率过程，这可以进一步适应公共政策的背景下，形成道路安全目标。
英文摘要：
Modelling crash rates in an urban area requires a swathe of data regarding historical and prevailing traffic volumes and crash events and characteristics. Provided that the traffic volume of urban networks is largely defined by typical work and school commute patterns, crash rates can be determined with a reasonable degree of accuracy. However, this process becomes more complicated for an area that is frequently subject to peaks and troughs in traffic volume and crash events owing to exogenous events (for example, extreme weather) rather than typical commute patterns. One such area that is particularly exposed to exogenous events is Washington, DC, which has seen a large rise in crash events between 2009 and 2020. In this study, we adopt a forecasting model that embeds heterogeneity and temporal instability in its estimates in order to improve upon forecasting models currently used in transportation and road safety research. Specifically, we introduce a stochastic volatility model that aims to capture the nuances associated with crash rates in Washington, DC. We determine that this model can outperform conventional forecasting models, but it does not perform well in light of the unique travel patterns exhibited throughout the COVID-19 pandemic. Nevertheless, its adaptability to the idiosyncrasies of Washington, DC crash rates demonstrates its ability to accurately simulate localised crash rates processes, which can be further adapted in public policy contexts to form road safety targets.
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