Mobile phone distracted drivers have been reported to initiate risk-compensating behaviour depending on a multitude of factors such as roadway environment and traffic characteristics, personal demographics and psychological attributes, and mobile phone task characteristics. However, the complexities of drivers’ decisions in engaging in such behaviour are not well known. This study aims to fill this gap by developing a comprehensive multivariate ordered model in Bayesian framework for risk-compensating behaviour of distracted drivers. The multivariate setting captures the common unobserved factors between multiple types of risk-compensating behaviour. In addition, an instrumental variable is employed to account for the endogeneity between crash risk and driving behaviour. To capture the varying effects of exogenous factors as well as varying propensity of initiating risk-compensating behaviour, the model is specified with grouped random parameters and random thresholds. This model is then empirically tested by data from a survey, which was specifically designed to understand the risk-compensating behaviour of mobile phone distracted drivers in Queensland, Australia. Results indicate that the grouped random parameters random thresholds ordered model has a substantially improved fit compared to its fixed parameters/fixed thresholds counterparts, indicating that the unobserved heterogeneity is significant, both in the effects of exogenous factors and in the propensity of initiating risk-compensating behaviour. It is found that drivers’ decisions to engage in different types of risk-compensating behaviour are correlated, indicating that they generally initiate different types of risk-compensating strategies simultaneously. Overall, the perceived crash risk has been found to increase the likelihood of risk-compensating behaviour among distracted drivers. Demanding secondary tasks and complex road traffic environment are also found to initiate risk-compensating behaviours such as increasing headway, reducing driving speed and visual scanning of the surrounding environment.

据报告，移动电话分心的驾驶员会根据多种因素（例如道路环境和交通特征，个人人口统计和心理属性以及移动电话任务特征）启动风险补偿行为。然而，驾驶员参与这种行为的决定的复杂性尚不为人所知。这项研究旨在通过在贝叶斯框架中开发一种综合的多元有序模型来分散注意力的驾驶员的风险补偿行为，从而填补这一空白。多元设置捕获了多种类型的风险补偿行为之间的共同未观察因素。另外，使用工具变量来解释碰撞风险和驾驶行为之间的内生性。为了捕获外源因素的不同影响以及发起风险补偿行为的不同倾向，使用分组的随机参数和随机阈值指定了模型。然后，通过调查数据对模型进行实证检验，该调查数据专门用于了解澳大利亚昆士兰州移动电话分心驾驶员的风险补偿行为。结果表明，与固定参数/固定阈值对应物相比，分组的随机参数随机阈值有序模型具有显着提高的拟合度，这表明，在外源因素的影响和发起风险补偿的倾向方面，未观察到的异质性均很显着。行为。发现驾驶员参与不同类型的风险补偿行为的决策是相关的，这表明他们通常同时启动不同类型的风险补偿策略。总体而言，已发现感知的撞车风险会增加分心的驾驶员进行风险补偿行为的可能性。还发现苛刻的次要任务和复杂的道路交通环境会引发风险补偿行为，例如增加行驶距离，降低驾驶速度和对周围环境进行可视扫描。