公交车驾驶员驾驶习惯安全报警分析数据

收集各个线路驾驶员在驾驶过程中的驾驶习惯安全报警数据，并对这些驾驶习惯进行了分类分级，统计每个驾驶员的驾驶习惯报警指数，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，规范驾驶员的驾驶习惯，保障线路安全运行，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，对驾驶习惯安全报警行为进行捕捉，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，规范驾驶员的驾驶习惯，保障线路安全运行，并提升整体运营效率。

This dataset collects driving habit-related safety alarm data of drivers on various transit routes during driving, classifies and grades these driving habits, counts the driving habit alarm index for each driver, and marks drivers into three levels: "General Reminder", "Moderate Warning" and "Severe Warning" based on the index, so as to standardize drivers' driving behaviors, ensure safe operation of transit routes and improve overall operational efficiency.
1. Data Collection: Real-time collection of drivers' heads, bodies and driving movements via infrared cameras, with the captured video being transmitted to active safety equipment. To accelerate image processing speed, infrared monochrome video is adopted for acquisition.
2. Data Processing: After receiving the video, the active safety equipment applies AI algorithms to conduct comprehensive detection on the driver's face, mouth, ears, eyes and movements, capture safety alarm behaviors related to driving habits, and generate a dataset containing fields such as driver ID, route ID, timestamp, active safety alarm category, risk level and other relevant data.
3. Algorithm Processing: The COUNTIFS function is used to classify and summarize the cumulative number of low-risk, medium-risk and high-risk alarms for each driver. The weighted average formula Y = a1X1 + a2X2 + a3X3 is then applied to calculate the comprehensive score Y, where the weight coefficients a1, a2 and a3 are 0.1, 0.3 and 0.6 respectively, and X1, X2 and X3 represent the cumulative times of low-risk, medium-risk and high-risk alarms respectively. Drivers are classified and graded according to the calculated score Y: drivers with a score ≤ 3 are marked as "General Reminder", those with 3 < score ≤ 15 as "Moderate Warning", and those with a score > 15 as "Severe Warning". The above classification labels are used to standardize drivers' driving behaviors, ensure safe operation of transit routes and improve overall operational efficiency.

搜集汇总

数据集介绍

特点

该数据集记录了公交车驾驶员的驾驶习惯安全报警数据，通过AI算法分析驾驶行为并计算风险得分，用于规范驾驶习惯和提升线路安全运行效率。