旅游车驾驶行为评价分析数据
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通过对接入协议企业重点车辆智管平台的旅游车辆驾驶员的驾驶行为进行分析，统计车辆违规次数。尤其是针对安全影响最大的超速行为进行重点分析，得出驾驶员是否有超速偏好，是否喜欢开快车，是否会影响旅游乘客安全。根据“月内违规次数”和“系统内该月平均违规次数”计算出旅游车司机的“驾驶行为评价分值”，根据不同的分值判断是否存在不同级别的安全隐患。应用场景适用于旅游车辆的安全管理应用，针对司机不同驾驶行为评价分值进行针对性的培训和纠正，提高旅游车运营管理领域的安全管理能力。1、数据采集：通过协议企业自建平台，对接入平台管理的车辆以及驾驶行为轨迹进行采集和分析。 2、数据处理：以旅游车驾驶员姓名为唯一字段，对近2个月的驾驶行为进行统计和分析，比较从而得出不同评价分值，并比较每个月的变化情况。 3、算法规则： （1）是否存在超速偏好=IF(其中超速次数/月内违规次数>50%,是，否)；即如果（其中超速次数/月内违规次数）>50%，则说明该驾驶员有超速偏好，该字段填“是”，否则填“否”。 （2）驾驶行为评价分值=[1-月内违规次数/（2*系统内该月平均违规次数）]*100，如果该值小于0，则置为0；即月内违规次数占比该月份系统内的月内违规平均次数越小，说明驾驶员越规范，则驾驶行为评价分值越大；如果超出平均值的两倍以上，则驾驶行为评价分值为0。 （3）系统内该月平均违规次数：为该月份所有司机的月内违规次数平均值。 （4）评价分值较上月变化比值=（驾驶行为评价分值-上月驾驶行为评价分值）/上月驾驶行为评价分值。 其中，开始的第一个月，上月驾驶行为评价分值设置为0。如果上月驾驶行为评价分值为0，则评价分值较上月变化比值直接设置为1，即100%。 （5）其他说明： 数据采集了2个月，2025年1月，2月。1月份的上月驾驶行为评价分值设置为0。 4、数据应用：旅游车驾驶员驾驶行为分析数据可通过可视化界面直观展现每个旅游车驾驶员的驾驶行为评价分值情况，根据评价分值较上月变化比值，对驾驶员进行针对性的培训和安全教育，对规范行为较好的旅游车驾驶员进行适当激励，该分析数据可应用于旅游车驾驶员安全管理领域，通过正向激励和管理机制来促进旅游车运营领域的安全管理能力提升。
This dataset conducts analysis on the driving behaviors of tourist bus drivers connected to the Key Vehicle Intelligent Management Platform of partnering protocol-based enterprises, and counts the total number of vehicle violations. It places special emphasis on speeding, which poses the greatest threat to traffic safety, to determine whether a driver has a speeding preference, tends to drive at excessive speeds, and whether such behaviors jeopardize the safety of tourist passengers. Based on the "monthly violation times" and "the system's monthly average violation times", the "Driving Behavior Evaluation Score" of tourist bus drivers is calculated, and different levels of potential safety hazards are judged according to different score ranges. The applicable application scenario is the safety management of tourist vehicles: targeted training and correction can be carried out for drivers based on their respective Driving Behavior Evaluation Scores, so as to improve the safety management capabilities in the tourist bus operation management field. 1. Data Collection: Collect and analyze the managed vehicles and their drivers' driving trajectories through the self-built platform of the partnering protocol-based enterprises. 2. Data Processing: Take the tourist bus driver's name as the unique identifier, conduct statistics and analysis on the driving behaviors of the past two months, derive different evaluation scores, and compare the monthly changes. 3. Algorithm Rules: (1) Speeding Preference Judgment: IF (speeding times / total monthly violation times > 50%, "Yes", "No"). That is, if the proportion of speeding times in the total monthly violation times exceeds 50%, the driver is considered to have a speeding preference, and this field is filled with "Yes"; otherwise, it is filled with "No". (2) Driving Behavior Evaluation Score: [1 - (monthly violation times / (2 * monthly average violation times of the system in that month))] * 100. If the calculated value is less than 0, set it to 0. That is, the smaller the proportion of the driver's monthly violation times to the monthly average violation times of the system that month, the more standardized the driver's behavior, and the higher the Driving Behavior Evaluation Score. If the violation times exceed twice the average value, the Driving Behavior Evaluation Score will be set to 0. (3) Monthly Average Violation Times of the System: The average number of monthly violation times of all drivers in the system that month. (4) Month-over-Month Change Ratio of Evaluation Scores: (Current month's Driving Behavior Evaluation Score - Previous month's Driving Behavior Evaluation Score) / Previous month's Driving Behavior Evaluation Score. For the first month (January 2025), the previous month's Driving Behavior Evaluation Score is set to 0. If the previous month's Driving Behavior Evaluation Score is 0, the month-over-month change ratio of the evaluation scores is directly set to 1 (i.e., 100%). (5) Other Notes: The dataset collects data for two months: January and February 2025. The previous month's Driving Behavior Evaluation Score for January 2025 is set to 0. 4. Data Application: The analysis data of tourist bus drivers' driving behaviors can visually display the Driving Behavior Evaluation Score of each tourist bus driver through a visual interface. According to the month-over-month change ratio of the evaluation scores, targeted training and safety education can be carried out for drivers, and appropriate incentives can be given to tourist bus drivers with relatively standardized behaviors. This analysis data can be applied to the safety management field of tourist bus drivers, and promote the improvement of safety management capabilities in the tourist bus operation sector through positive incentive and management mechanisms.
