The link between attitudes and behavior shows that driving behavior can be predicted by personal characteristics and individual attitudes, as has been shown in previous research. This study aimed to predict the level of compliance with speed limits by individual drivers by using attitudes data including speed limit credibility perception and risk perception on eight rural single carriageway layouts. This study investigated how the road layout and roadside environment affect speed limit credibility perception and risk perception, and investigated which machine learning algorithm can be used to predict driving behavior based on experimental evidence. This study was carried out in a well-controlled experimental design by using a questionnaire and a driving simulator. The simulated road environment only considered rural single carriageway which has higher risk factors than other road types. The results show that a boosted decision tree algorithm can establish a driving behavior model based on drivers’ credibility perception and risk perception. This result can be used to predict driving behavior in advance for in-vehicle warning system design.

态度和行为之间的联系表明，驾驶行为可以通过个人特征和态度来预测，如先前的研究所示。这项研究旨在通过使用态度数据来预测单个驾驶员对速度限制的遵守程度，这些态度数据包括对八个农村单车道布局的速度限制可信度感知和风险感知。这项研究调查了道路布局和路边环境如何影响限速信誉度感知和风险感知，并基于实验证据研究了哪种机器学习算法可用于预测驾驶行为。这项研究是通过使用问卷和驾驶模拟器在控制良好的实验设计中进行的。模拟道路环境仅考虑农村单车道，其危险因素高于其他道路类型。结果表明，改进的决策树算法可以基于驾驶员的信誉感知和风险感知建立驾驶行为模型。该结果可用于预先预测驾驶行为，以进行车载警报系统设计。