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首页> 外文期刊>The Open Statistics & Probability Journal >A Stochastic Model for Highway Accident Predictions with Winter Data
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A Stochastic Model for Highway Accident Predictions with Winter Data

机译：冬季数据的公路事故预测的随机模型

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In this paper, we consider the problem of modeling and predicting highway accidents in the presence ofrandomly changing winter driving conditions. Unlike most accident prediction models in the literature, which are typicallyformulated in a static (e.g. regression models) or discrete time (e.g. time-series models) setting, we propose a continuoustimestochastic model to describe the relation between highway accidents and winter weather dynamics. We believe this tobe a more natural way to describe discrete-event highway accidents that occur in continuous-time. In particular, theaccident counting process is viewed as a non-homogeneous Poisson process (NHPP) with an intensity function thatdepends on a (Markovian) weather process. Such a model is known in the stochastic process literature as a MarkovmodulatedPoisson process (MMPP) and has been successfully applied to queuing and telecommunications problems. Onemain advantage of such an approach, is its ability to provide explicit closed-form prediction formulae for both weatherand accidents over any future time horizon (i.e. short or long-term predictions). To illustrate the effectiveness of theproposed stochastic model, we study a large winter data set provided by Ministry of Transportation of Ontario (MTO) thatincludes motor vehicle accidents on Highway 401, the busiest highway in North America.
机译：在本文中，我们考虑了在冬季驾驶条件随机变化的情况下对高速公路事故进行建模和预测的问题。与文献中大多数大多数事故预测模型不同，后者通常是在静态（例如回归模型）或离散时间（例如时间序列模型）设置中形成的，我们提出了一种连续时间随机模型来描述公路事故与冬季天气动态之间的关系。我们认为，这是描述连续时间发生的离散事件公路事故的更自然的方法。特别地，事故计数过程被视为具有取决于（马尔可夫）天气过程的强度函数的非均匀泊松过程（NHPP）。这样的模型在随机过程文献中称为MarkovmodulatedPoisson过程（MMPP），并且已成功应用于排队和电信问题。这种方法的主要优势在于，它能够为未来任何时间范围内的天气和事故提供明确的封闭式预测公式（即短期或长期预测）。为了说明所建议的随机模型的有效性，我们研究了安大略省交通部（MTO）提供的大型冬季数据集，其中包括北美最繁忙的高速公路401上的机动车事故。

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