Department of Epidemiology and Health Statistics, Hunan Provincial Key Laboratory of Clinical Epidemiology, Xiangya School of Public Health, Central South University, Changsha, 410013, China.
Changsha Center for Disease Control and Prevention, Changsha, 410004, China.
BMC Public Health. 2024 Jan 20;24(1):241. doi: 10.1186/s12889-024-17756-y.
Multiple distraction indicators have been applied to measure street-crossing distraction but their validities in predicting pedestrian safety are poorly understood.
Based on a video-based observational study, we compared the validity of four commonly used distraction indicators (total duration of distraction while crossing a street, proportion of distracted time over total street-crossing time, duration of the longest distraction time, and total number of distractions) in predicting three pedestrian safety outcomes (near-crash incidence, frequency of looking left and right, and speed crossing the street) across three types of distraction (mobile phone use, talking to other pedestrians, eating/drinking/smoking). Change in Harrell's C statistic was calculated to assess the validity of each distraction indicator based on multivariable regression models including only covariates and including both covariates and the distraction indicator.
Heterogeneous capacities in predicting the three safety outcomes across the four distraction indicators were observed: 1) duration of the longest distraction time was most predictive for the occurrence of near-crashes and looks left and right among pedestrians with all three types of distraction combined and talking with other pedestrians (Harrell's C statistic changes ranged from 0.0310 to 0.0335, P < 0.05), and for the occurrence of near-crashes for pedestrians involving mobile phone use (Harrell's C statistic change: 0.0053); 2) total duration of distraction was most predictive for speed crossing the street among pedestrians with the combination and each of the three types of distraction (Harrell's C statistic changes ranged from 0.0037 to 0.0111, P < 0.05), frequency of looking left and right among pedestrians distracted by mobile phone use (Harrell's C statistic change: 0.0115), and the occurrence of near-crash among pedestrians eating, drinking, or smoking (Harrell's C statistic change: 0.0119); and 3) the total number of distractions was the most predictive indicator of frequency of looking left and right among pedestrians eating, drinking, or smoking (Harrell's C statistic change: 0.0013). Sensitivity analyses showed the results were robust to change in grouping criteria of the four distraction indicators.
Future research should consider the pedestrian safety outcomes and type of distractions to select the best distraction indicator.
已经有多种分散注意力的指标被应用于测量过街时的分散注意力程度,但这些指标在预测行人安全方面的有效性还知之甚少。
基于基于视频的观察性研究,我们比较了四种常用的分散注意力指标(过街时分散注意力的总时间、分散注意力时间占过街总时间的比例、最长分散注意力时间的持续时间和分散注意力的总次数)在预测三种行人安全结果(近撞事故发生率、左右看的频率和过街速度)方面的有效性,这三种行人安全结果涉及三种类型的分散注意力(使用手机、与其他行人交谈、吃喝或吸烟)。基于仅包含协变量和同时包含协变量和分散注意力指标的多变量回归模型,计算哈雷尔 C 统计量的变化,以评估每个分散注意力指标的有效性。
在预测四种分散注意力指标的三种安全结果方面,观察到能力存在差异:1)最长分散注意力时间最能预测所有三种类型的分散注意力和与其他行人交谈时的近撞事故发生率和左右看(哈雷尔 C 统计量变化范围为 0.0310 至 0.0335,P<0.05),以及使用手机的行人的近撞事故发生率(哈雷尔 C 统计量变化:0.0053);2)总分散注意力时间最能预测组合和三种类型的分散注意力中行人的过街速度(哈雷尔 C 统计量变化范围为 0.0037 至 0.0111,P<0.05)、使用手机的行人左右看的频率(哈雷尔 C 统计量变化:0.0115)和进食、饮水或吸烟的行人近撞事故发生率(哈雷尔 C 统计量变化:0.0119);3)总分散注意力次数是预测进食、饮水或吸烟的行人左右看频率的最具预测性指标(哈雷尔 C 统计量变化:0.0013)。敏感性分析表明,结果对四种分散注意力指标分组标准的变化具有稳健性。
未来的研究应考虑行人安全结果和分散注意力的类型,以选择最佳的分散注意力指标。