恒风行公交线路纠错上报分析数据

通过收集恒风行APP公交线路的纠错上报数据，对这些数据进行处理与分析，核算出不同公交路线纠错上报的数量及趋势，针对纠错环比、同比增长较高的路线进行异常问题的排查，并制定个性化的纠错解决方案，从而持续优化公交服务，满足市民的出行需求。(1)数据采集：利用恒风行APP导出2018年11月至2024年8月的公交路线纠错上报明细。 (2)数据处理：对数据进行清洗、去除无效数据等操作，提取出用户昵称，线路名称，意见类型，年月日。 (3)数据加工：通过Python的pandas库，导入数据，利用groupby函数根据年月日分组计算当天纠错总量；利用pandas库中的year、month函数提取出当月及上月纠错总数X1、X2，计算纠错月环比数据 A=(X1-X2)/X2；利用pandas库总的year、month函数，根据相同月份，提取出去年同期纠错总量。 (4)数据应用：月环比值为正则相对上月纠错频次降低，负责频次增加，结合当天、本月、上月、去年同期数据制成折现图，直观反映周期内问题出现的总频次及趋势，帮助管理人员快速了解不同线路的纠错问题所在，有利于运营管理部门进行针对性的整改。This dataset is constructed by collecting and analyzing bus route error correction report data from the Hengfengxing APP. First, we calculate the volume and trend of error correction submissions for different bus routes, investigate abnormal issues on routes with high month-on-month and year-on-year growth in error corrections, and develop personalized error correction solutions, thereby continuously improving bus services to meet citizens' travel needs. (1) Data Collection: Export detailed records of bus route error correction reports from the Hengfengxing APP spanning from November 2018 to August 2024. (2) Data Preprocessing: Conduct operations including data cleaning and removal of invalid entries, and extract user nicknames, route names, opinion types, and complete dates (year, month, day). (3) Data Processing and Calculation: Import the dataset using Python's pandas library. Calculate the total daily error correction submissions by grouping the data based on dates using the groupby() function. Use the year() and month() functions in pandas to extract the total monthly error correction submissions for the current and previous months, denoted as X1 and X2, then compute the monthly-on-month growth rate A=(X1-X2)/X2. Additionally, use the year() and month() functions in pandas to extract the total error correction submissions from the same period last year by matching the same calendar month. (4) Data Application: When the monthly-on-month growth rate is positive, the error correction frequency increases compared to the previous month; otherwise, the frequency decreases. Generate line charts using the data of the current day, current month, previous month, and the same period last year, which intuitively reflect the total frequency and trend of issues over the observation period. This helps managers quickly identify the root causes of error correction issues across different bus routes, enabling targeted rectification work for the operation management departments.