工程运输车辆安全行为分析数据
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适用工程运输领域安全监管应用场景，通过对工程运输车辆自身状况（车辆年限）和行驶数据信息（年平均里程、平均时速、安全监管违规次数）进行统计分析，从而得出不同车辆损耗加上驾驶员自身驾驶行为叠加的情况下，对工程车运输过程中安全的影响。建立安全指数模型，主要解决工程运输领域应用场景下，对车辆和驾驶员在路上行驶过程中的整体进行安全评估的问题。 通过可视化界面直观展现每辆工程车和驾驶员整体行为结合后的安全指数评价情况，以月为维度动态展示安全指数变化，协助车辆和驾驶员所属公司及时了解工程车运输安全问题，针对性调整安全管理机制，对车辆进行及时巡检，对驾驶员进行安全教育和培训等，提升企业安全管理能力。1、数据采集：通过企业自建平台，对接入平台管理的车辆和对应驾驶员安全监管要求下的违规行为进行采集和分析统计，并经数据脱敏处理。 2、数据处理：以车牌号为唯一字段，对近一年内的车辆和驾驶员行驶过程信息建立过程评价模型，计算安全指数。 3、算法规则： 安全指数=(1-车辆年限/20)*0.2+(1-年平均里程/最大年平均里程)*0.2+(1-平均时速/80)*0.1+(1-安全监管年违规次数/最大年安全监管违规次数)*0.5。 4、安全指数使用说明：解决工程运输领域应用场景下，对车辆和驾驶员在路上行驶过程中的整体进行安全评估。该部分评估主要根据安全指数的最终计算数值来看，安全指数越接近于1，说明整体评估效果越好（包括车辆的安全状况和驾驶员的行为习惯），值越低，说明至少车辆或者驾驶行为等其中的某一项出现明显问题，可以通过车辆定期巡检，驾驶员安全培训等行为手段及时介入并改善安全状况，提前预防安全事故发生。另外，除根据安全指数进行综合评价以外，还可对单项分值进行可视化展示，并对比一定时间周期内的变化，采取车辆维护、教育培训、减少工程载重和行驶里程等针对性措施，纠正单项评价存在的安全隐患。
This dataset is developed for safety supervision application scenarios in the engineering transport industry. It conducts statistical analysis on the self-status of engineering transport vehicles (vehicle age) and driving data (annual average mileage, average driving speed, number of safety supervision violations), to quantify the combined impact of vehicle wear and driver's driving behaviors on the safety of engineering transport operations. A safety index model is established to address the need for comprehensive safety assessment of vehicles and drivers during on-road driving in engineering transport scenarios. A visual interface is provided to intuitively display the safety index evaluation results of each engineering vehicle combined with its driver's overall driving behavior, and dynamically visualize the changes in safety index on a monthly basis. This enables the enterprises managing the vehicles and their drivers to timely identify safety issues in engineering transport, adjust safety management mechanisms in a targeted manner, perform routine vehicle inspections, and carry out safety education and training for drivers, thereby enhancing the enterprises' safety management capabilities. 1. Data Collection: Through the self-built enterprise platform, collect, analyze and count the violations of the managed vehicles and their corresponding drivers in accordance with safety supervision requirements, and perform data desensitization processing. 2. Data Processing: Take the license plate number as the unique field, establish a process evaluation model based on the driving information of vehicles and drivers in the past year, and calculate the safety index. 3. Algorithm Rules: Safety Index = (1 - Vehicle Age / 20) * 0.2 + (1 - Annual Average Mileage / Maximum Annual Average Mileage) * 0.2 + (1 - Average Driving Speed / 80) * 0.1 + (1 - Annual Number of Safety Supervision Violations / Maximum Annual Number of Safety Supervision Violations) * 0.5 4. Instructions for Safety Index Usage: This tool is designed to conduct comprehensive safety assessment of vehicles and drivers during on-road driving in engineering transport scenarios. The assessment is primarily based on the final calculated value of the safety index: the closer the safety index is to 1, the better the overall assessment effect, which covers both the vehicle's safety status and the driver's driving habits. A lower safety index value indicates that obvious problems exist in at least one aspect, such as vehicle condition or driving behavior. Timely interventions including routine vehicle inspections and driver safety training can be implemented to improve the safety status and prevent safety accidents in advance. In addition to the comprehensive evaluation based on the overall safety index, single-item scores can also be visually displayed, and their changes over a specific time period can be compared. Targeted measures such as vehicle maintenance, education and training, reducing engineering load and driving mileage can be adopted to rectify the safety hazards identified in single-item evaluation.
