National Technical University of Athens, Department of Transportation Planning and Engineering, 5 Iroon Polytechniou St, GR-15773 Athens, Greece.
National Technical University of Athens, Department of Transportation Planning and Engineering, 5 Iroon Polytechniou St, GR-15773 Athens, Greece.
Accid Anal Prev. 2023 Mar;181:106936. doi: 10.1016/j.aap.2022.106936. Epub 2022 Dec 26.
While driver distraction remains an issue in modernized societies, technological advancements in data collection, storage and analysis provide the means for deeper insights of this complex phenomenon. In this research, factors influencing when driver distraction through mobile phone use occurs during naturalistic driving are investigated. Naturalistic data from a 6-stage, 230-driver experiment are exploited, in which drivers installed a non-intrusive driving recording application in their devices and conducted their trips normally across a 21-month timespan, coupled with corresponding questionnaire data. The various experiment stages involved providing progressively more behavioral feedback to drivers while continuing to record them. Subsequently, supervised Machine Learning XGBoost algorithms were employed to model the contributions of naturalistic driving and questionnaire features to the decision to engage mobile phone use. Mobile phone use percentages were heavily skewed towards zero, therefore imbalanced ML with a minority-oversampling approach in a binary format was employed. To increase the explainability offered by the algorithm, SHAP values were calculated for the informative features. Results indicate that the decision of drivers to use a mobile while driving is governed by a number of complex, non-linear relationships. Total trip distance is the most significant predictor variable by a wide margin, with mean SHAP values of 0.79 towards affecting the model decisions for the probability of mobile phone use of each driver. However, other variables influence the final predictions as well, such as the number of tickets in the last three years (m.SHAP = 0.30), declared mobile phone use (m.SHAP = 0.26), the amount and variety of provided feedback (m.SHAP = 0.17) (i.e. experiment phase number) and family member numbers (m.SHAP = 0.09) decrease the probability of using a mobile phone while driving. Conversely, increases in driver experience (m.SHAP = 0.22), driver age (m.SHAP = 0.11), engine capacity (m.SHAP = 0.11) and total kilometers driven annually (m.SHAP = 0.08) increase the probability of using a mobile phone in naturalistic driving conditions. SHAP dependency plots reveal non-linear effects present in almost all variables. Fuel consumption had a particularly strong non-linear effect, as higher values of this variable lead to both higher and lower probability of drivers using a mobile phone, deviating from the safer average. Legislation, campaigns and enforcement measures can be restructured to take advantage of gains margins in terms of understanding and predicting driver distraction behavior, as explored in the present study.
虽然驾驶员分心仍然是现代社会的一个问题，但数据收集、存储和分析方面的技术进步为深入了解这一复杂现象提供了手段。在这项研究中，调查了在自然驾驶过程中通过使用手机导致驾驶员分心的因素。利用了一项 6 阶段、230 名驾驶员实验的自然数据，驾驶员在他们的设备中安装了一个非侵入性的驾驶记录应用程序，并在 21 个月的时间跨度内正常进行他们的行程，同时还有相应的问卷调查数据。在不同的实验阶段，为驾驶员提供了越来越多的行为反馈，同时继续记录他们的驾驶情况。随后，采用了监督机器学习 XGBoost 算法来对自然驾驶和问卷调查特征对使用手机的决策的贡献进行建模。手机使用百分比严重偏向于零，因此采用了少数过采样方法进行不平衡机器学习，采用二进制格式。为了增加算法提供的可解释性，计算了 SHAP 值以用于有价值的特征。结果表明，驾驶员在驾驶时使用手机的决定受到许多复杂的非线性关系的影响。总行驶距离是最显著的预测变量，其平均 SHAP 值为 0.79，对每个驾驶员使用手机的概率模型决策产生影响。然而，其他变量也会影响最终预测，例如过去三年的罚单数量（m.SHAP=0.30）、声明的手机使用（m.SHAP=0.26）、提供的反馈数量和种类（m.SHAP=0.17）（即实验阶段数）以及家庭成员数量（m.SHAP=0.09）会降低驾驶时使用手机的概率。相反，驾驶员经验（m.SHAP=0.22）、年龄（m.SHAP=0.11）、发动机容量（m.SHAP=0.11）和每年行驶的总公里数（m.SHAP=0.08）的增加会增加在自然驾驶条件下使用手机的概率。SHAP 依赖关系图揭示了几乎所有变量中存在的非线性效应。燃料消耗具有特别强的非线性效应，因为该变量的较高值会导致驾驶员使用手机的概率增加和降低，偏离了更安全的平均值。可以利用本研究中探索到的理解和预测驾驶员分心行为方面的收益优势，来重新构建立法、运动和执法措施。
