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首页> 外文期刊>Asian Transport Studies >Development of Robust Real-Time Crash Prediction Models Using Bayesian Networks
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Development of Robust Real-Time Crash Prediction Models Using Bayesian Networks

机译：利用贝叶斯网络开发鲁棒的实时碰撞预测模型

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Several real-time crash prediction (RTCP) models have been proposed using Bayesian networks (BNs), which are probabilistic graphical modeling methods offering a great degree of robustness. These models offer real-time applicability, high prediction success, a capacity to handle missing data, and the possibility of a flexible variable space. However, to develop an advanced RTCP model using BN, it is imperative to identify the most influential traffic variables and their combinations. This study proposes BN-based RTCP models with 24 combinations of 12 traffic variables. After modeling, their performances were validated and compared to identify the preferable combinations of input variables. The models constructed with differences between the upstream and downstream congestion index, flow, speed, and the upstream congestion index as variables proved to be the most effective combination of input variables.
机译：已经使用贝叶斯网络（BN）提出了几种实时崩溃预测（RTCP）模型，这些模型是提供高度鲁棒性的概率图形建模方法。这些模型提供了实时适用性，很高的预测成功率，处理丢失数据的能力以及灵活可变空间的可能性。但是，要使用BN开发高级RTCP模型，必须确定最有影响力的流量变量及其组合。这项研究提出了基于BN的RTCP模型，其中包含12种流量变量的24种组合。建模后，对它们的性能进行验证并进行比较，以确定输入变量的最佳组合。以上游和下游拥塞指数，流量，速度和上游拥塞指数之间的差异作为变量构建的模型被证明是输入变量的最有效组合。

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