公交车驾驶员危险驾驶行为安全报警分析数据

收集各个线路驾驶员在驾驶过程中的危险驾驶行为安全报警数据，并对这些危险驾驶行为进行了分类分级，统计每个驾驶员的危险驾驶报警指数，根据这一指数，将驾驶员标记为一般提醒、中度警告、严重警告三个层次，有效遏制驾驶员的危险驾驶行为，确保线路的安全稳定运行，并提升整体运营效率。1.数据采集：通过红外摄像头实时的对驾驶员的头部、身体、动作进行采集，将视频送给主动安全设备。为了提高图像的处理速度，我们采用的是红外黑白视频。2.数据处理：主动安全设备接受到视频后，采用AI算法，对司机人脸、嘴巴、耳朵、眼睛、动作进行全方位的判断，对危险驾驶报警行为进行捕捉，生成数据集合，如：司机id、线路id、时间、主动安全报警类别、风险等数据。3.算法加工：使用COUNTIFS函数分类汇总出该司机的低风险累计报警次数、中风险累计报警次数、高风险累计报警次数，将处理后的数据通过加权平均法：Y=a1X1+a2X2+a3X3，权重系数a1、a2、a3分别是0.1、0.3、0.6，X1、X2、X3分别是低风险累计报警次数、中风险累计报警次数、高风险累计报警次数，计算出得分Y。通过分数，对用户进行分类分级，得分≤3，记为“一般提醒”，15≥得分＞3，记为“中度警告”，得分＞15，记为“严重警告”。通过标签，有效遏制驾驶员的危险驾驶行为，确保线路的安全稳定运行，并提升整体运营效率。

This dataset collects safety alert data regarding dangerous driving behaviors of drivers operating each route during their driving sessions, classifies and grades these dangerous driving behaviors, and calculates the dangerous driving alert index for each driver. Based on this index, drivers are categorized into three levels: General Reminder, Moderate Warning, and Severe Warning, aiming to effectively curb drivers' dangerous driving behaviors, ensure safe and stable operation of routes, and enhance overall operational efficiency.
1. Data Collection: Infrared cameras are deployed to collect the head, body and movements of drivers in real time, and the captured videos are transmitted to active safety equipment. To accelerate image processing speed, infrared monochrome videos are utilized.
2. Data Processing: Upon receiving the videos, the active safety equipment employs AI algorithms to comprehensively evaluate the driver's face, mouth, ears, eyes and movements, detect dangerous driving alert behaviors, and generate a dataset that includes driver ID, route ID, timestamp, active safety alert category, risk level and other relevant data.
3. Algorithm Processing: The COUNTIFS function is utilized to classify and aggregate the cumulative counts of low-risk, medium-risk and high-risk alerts for each driver. Subsequently, the weighted average formula Y = a1X1 + a2X2 + a3X3 is applied to calculate the score Y, where the weight coefficients a1, a2, a3 are set to 0.1, 0.3 and 0.6 respectively, and X1, X2, X3 represent the cumulative times of low-risk, medium-risk and high-risk alerts respectively. Drivers are then classified and graded based on their calculated scores: those with a score ≤ 3 are labeled as "General Reminder", those with 3 < score ≤ 15 as "Moderate Warning", and those with a score > 15 as "Severe Warning". Such a classification and labeling mechanism can effectively curb drivers' dangerous driving behaviors, ensure safe and stable operation of routes, and improve overall operational efficiency.

搜集汇总

数据集介绍

特点

该数据集包含公交车驾驶员危险驾驶行为的安全报警数据，共517条记录，每日更新。数据通过红外摄像头和AI算法采集处理，用于分析驾驶员的危险驾驶行为，并根据风险等级进行分类分级，以提升运营效率和安全性。