金华行服务平台公交车营运分析数据

通过对金华行服务平台的公交车营运数据进行挖掘分析，掌握各公交路线的票务退款情况，有助于公交营运部门及时掌握公交车票务退款预警信息，及时指导运营车辆加强票务监督和管理，为车队驾驶员考核评优提供决策依据。1、数据采集：从金华行服务平台实时采集金华市公交车辆的刷卡信息、售票信息、退款次数和退款金额等数据；
2、数据处理：对数据集进行分类、汇总和清洗，包括去除异常数据、处理缺失值，形成可加工的数据集合；
3、数据加工：计算所有车辆的累计退款笔数∑R，计算累计退款笔数∑Ra，a为不同的公交车辆，值为1、2…n；计算所有车辆的累计退款金额∑Q，计算累计退款金额∑Rb，b为不同的公交车辆，值为1、2…n；计算退款笔数预警指数X=某一车辆累计退款笔数∑Ra/所有车辆的累计退款笔数∑R*10000；计算退款金额预警指数Y=某一车辆累计退款金额∑Rb/所有车辆的累计退款金额∑Q*10000；并根据X和Y的大小进行降序排序获取排序值。
4、数据应用：通过图表可视化分析某一车辆的退款笔数预警指数X和退款金额预警指数Y，以及对应的排序值，指数超过10的为高度预警；指数在1~10为轻度预警；指数在1以下的为无预警，通过预警指数为加强票务监督管理提供决策依据。

By mining and analyzing bus operation data from the Jinhua Xing Service Platform to grasp the ticket refund status of each bus route, bus operation departments can timely obtain early warning information of bus ticket refunds, timely guide operating vehicles to strengthen ticket supervision and management, and provide decision-making basis for driver performance appraisal and merit-based selection.
1. Data Collection: Collect real-time data including swipe card records, ticket sales information, refund times and refund amounts of Jinhua's buses from the Jinhua Xing Service Platform.
2. Data Processing: Classify, aggregate and clean the dataset, including removing outliers and handling missing values, to form a processable data set.
3. Data Calculation: Calculate the total number of refund tickets across all buses (∑R), and the total number of refund tickets per individual bus (∑R_a, where a represents each bus with values 1, 2…n); Calculate the total refund amount across all buses (∑Q), and the total refund amount per individual bus (∑R_b, where b represents each bus with values 1, 2…n); Calculate the refund ticket number early warning index X = (∑R_a / ∑R) * 10000; Calculate the refund amount early warning index Y = (∑R_b / ∑Q) * 10000; Then sort the buses in descending order based on the values of X and Y to obtain their ranking scores.
4. Data Application: Analyze the refund ticket number early warning index X, refund amount early warning index Y and their corresponding ranking scores of a specific bus through chart-based visualization. Buses with an index exceeding 10 are categorized as high-level early warning; those with an index between 1 and 10 are categorized as mild early warning; those with an index below 1 are categorized as no early warning. These early warning indexes provide decision-making basis for strengthening ticket supervision and management.

搜集汇总

数据集介绍

特点

金华行服务平台公交车营运分析数据包含776条公交车票务数据，涵盖扣款日期、分公司、刷卡次数等12个字段，用于分析退款预警指数，辅助公交营运部门进行票务管理和驾驶员考核。