A Markov switching logit model with spatial dependencies for real-time crash risk assessment is proposed, with the purpose of identifying hazardous traffic-flow conditions with high crash potential. The Markov switching process assumes that freeway segments can switch between two unobserved safety states over time, and that parameter estimates may vary between these two states. The spatial simultaneous autoregressive process was used to account for possible dependencies in crash likelihood between neighbouring freeway segments. The proposed model was used to link crash likelihood with real-time traffic, weather and roadway geometry data. The Bayesian inference method based on Markov chain Monte Carlo simulations was used for model estimation. Bayes factor analysis suggested that the proposed model produces a better fit than other alternatives that ignore temporal or/and spatial effects. The estimation results revealed that two states with regard to crash likelihood exist and that freeway segments can switch between these states over time. The spatial autocorrelation coefficient indicated that crash likelihood on a freeway segment is interlinked with those on neighbouring segments over space. The key traffic variables contributing to crash likelihood are detector occupancy, occupancy difference between adjacent lanes, speed variance and occupancy difference between upstream and downstream detector stations. Moreover, three geometric variables and weather conditions are significantly related to crash likelihood in the model. The receiver operating characteristic curve showed that the predictive performance of the proposed model is satisfactory.

中文翻译：

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考虑空间效应的高速公路碰撞风险的马尔可夫切换回归分析

提出了一种具有空间依赖性的马尔可夫切换对数模型，用于实时碰撞风险评估，目的是识别具有高碰撞可能性的危险交通流状况。马尔可夫切换过程假定高速公路路段可以随时间在两个未观察到的安全状态之间切换，并且参数估计值在这两个状态之间可能会有所不同。使用空间同时自回归过程来解释相邻高速公路路段之间的碰撞可能性的可能依赖性。提议的模型用于将碰撞可能性与实时交通，天气和道路几何数据关联起来。基于马尔可夫链蒙特卡洛模拟的贝叶斯推断方法用于模型估计。贝叶斯因子分析表明，与忽略时间或/和空间影响的其他替代方法相比，所提出的模型具有更好的拟合度。估计结果表明，存在关于撞车可能性的两种状态，并且高速公路路段可以随时间在这些状态之间切换。空间自相关系数表明，高速公路路段的碰撞可能性与空间上相邻路段的碰撞可能性相互关联。导致碰撞可能性的关键交通变量是探测器占用率，相邻车道之间的占用率差异，速度差异和上游和下游探测器站之间的占用率差异。此外，三个几何变量和天气状况与模型中的崩溃可能性显着相关。