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Road Safety Risk Evaluation Using GIS-Based Data Envelopment Analysis—Artificial Neural Networks Approach

机译：基于GIS的数据包络分析的道路安全风险评估—人工神经网络方法

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Identification of the most significant factors for evaluating road risk level is an important question in road safety research, predominantly for decision-making processes. However, model selection for this specific purpose is the most relevant focus in current research. In this paper, we proposed a new methodological approach for road safety risk evaluation, which is a two-stage framework consisting of data envelopment analysis (DEA) in combination with artificial neural networks (ANNs). In the first phase, the risk level of the road segments under study was calculated by applying DEA, and high-risk segments were identified. Then, the ANNs technique was adopted in the second phase, which appears to be a valuable analytical tool for risk prediction. The practical application of DEA-ANN approach within the Geographical Information System (GIS) environment will be an efficient approach for road safety risk analysis.
机译：在道路安全研究中，识别最重要的因素是评估道路风险水平的一个重要问题，主要是决策过程。但是，为此目的选择模型是当前研究中最相关的焦点。在本文中，我们提出了一种道路安全风险评估的新方法，该方法是一个由数据包络分析（DEA）和人工神经网络（ANN）组成的两阶段框架。在第一阶段，通过应用DEA来计算所研究路段的风险水平，并确定高风险路段。然后，在第二阶段采用了人工神经网络技术，这似乎是进行风险预测的有价值的分析工具。 DEA-ANN方法在地理信息系统（GIS）环境中的实际应用将是道路安全风险分析的有效方法。

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