临平区共享单车过夜还车情况分析数据

通过收集和分析临平区各个共享单车站点的过夜还车数据，我们能够深入了解各个站点的使用频率、租车时长以及异常情况。不仅有助于监控站点的运营状况和效率，还能结合异常分布和站点评价来识别潜在的运营风险。特别是对于P1类型的站点，需要给予特别关注，并据此制定针对性的改进措施，以提升整体的服务质量和用户体验。1、数据采集：收集临平区共享单车过夜还车数据：序号、类型A、类型B、卡号、车号、租车网点编号等字段；2、数据处理：对采集到的数据进行清洗、分类汇总；3、数据加工：使用COUNTIFS、SUMIFS、VLOOKUP函数计算当月订单数、当月租车时长、当月异常订单数、站点平均异常率；当月异常率=当月异常订单数/当月订单数，异常分布情况=当月异常率*0.4+站点平均异常率*0.6，站点异常情况评价标准：异常分布情况<20%，为P3，异常分布情况<50%，为P2，异常分布情况≥50%，为P1，；4、数据应用：根据站点异常情况评价，对P1、P2状态的站点更加关注。

By collecting and analyzing overnight return data of shared bikes at various stations in Linping District, we can gain in-depth insights into the usage frequency, total rental duration and abnormal operational scenarios of each station. This work not only aids in monitoring the operational status and efficiency of the stations, but also identifies potential operational risks by integrating abnormal distribution metrics and station evaluation results. Particular focus shall be allocated to stations categorized as Type P1, and targeted improvement measures shall be developed correspondingly to elevate overall service quality and user experience.
1. Data Collection: Collect overnight shared bike return data across Linping District, including fields such as serial number, Type A, Type B, card number, bike number, rental station number, etc.
2. Data Preprocessing: Clean the collected dataset and perform classified aggregation.
3. Data Calculation & Enrichment: Utilize functions including COUNTIFS, SUMIFS and VLOOKUP to calculate the monthly order volume, total monthly rental duration, monthly abnormal order count and average abnormal rate per station. The relevant formulas are as follows:
- Monthly Abnormal Rate = Monthly Abnormal Order Count / Monthly Order Volume
- Abnormal Distribution Index = Monthly Abnormal Rate * 0.4 + Station Average Abnormal Rate * 0.6
The evaluation criteria for station abnormal situations are:
- Classified as P3 if Abnormal Distribution Index < 20%
- Classified as P2 if 20% ≤ Abnormal Distribution Index < 50%
- Classified as P1 if Abnormal Distribution Index ≥ 50%
4. Data Application: Prioritize monitoring stations with P1 and P2 status based on the results of station abnormal situation evaluation.

搜集汇总

数据集介绍

特点

该数据集记录了临平区共享单车的过夜还车情况，包含948条数据，每日更新，用于监控站点运营状况和识别潜在风险。通过算法计算异常订单数和异常率，评估站点异常情况，特别关注P1类型站点。