杭州市临平区公共自行车站点分析数据

通过杭州市智能单车调度系统，查询临平区各公共自行车站点数据，分析各站点重要程度及综合评分，为站点维护及车辆调度提供数据支撑，助力不同站点自行车数量保持相对平衡,避免出现部分站点供不应求或需求过剩的情况，提升运营管理效率。(1)、数据处理：利用智能单车调度系统导出2024年6月杭州市临平区公共自行车站点租还车数据，以站点编号为唯一标识，清洗去除无效数据。
(2)、数据加工：
租还比A=租车数/还车数，A越接近100%该站点租还越均衡，A越大于或小于100%，该站点越需要车辆调度支持；
站点车流量B=租车数+还车数，通过MEDIAN函数计算B的中位数，MODE函数计算B的众数，AVERAGE函数计算B的平均数，再利用IF函数计算站点重要值C=IF(B>中位数,1,0)+IF(B>众数,1,0)+IF(B>平均数,1,0)，C取值区间[0,6],C越大该站点越重要；
站点综合评分D=租车数*X+还车数*Y-异常租车数*Z，X、Y、Z为指标权重，本次计算中分别为40%、40%、20%，D越高该站点受欢迎程度越高，该站点越重要；
租还差=还车数-租车数，租还差离散度E=AVEDEV（租还差），租还关联度F=CORREL(租车数，还车数)，E、F越大临平区整体租还越均衡；
(3)、数据应用：通过A、C、D评估各站点运营管理情况，通过E、F评估临平区整体运营管理情况，区域与整体结合为站点维护及车辆调度提供数据支持。

This study collects data of public bicycle stations in Linping District via the Hangzhou Intelligent Bicycle Dispatching System, analyzes the importance and comprehensive scores of each station, and provides data support for station maintenance and vehicle dispatching. The work aims to maintain a relatively balanced number of bicycles across different stations, avoid situations where some stations face supply shortages or excess demand, and improve operational management efficiency.
(1) Data Preprocessing: The rental and return data of public bicycle stations in Linping District, Hangzhou, in June 2024 were exported through the Intelligent Bicycle Dispatching System. Taking station IDs as unique identifiers, invalid data were cleaned and removed.
(2) Data Indicator Calculation:
The rental-return ratio A = number of rentals / number of returns. The closer A is to 100%, the more balanced the rental and return operations at the station are; the more A deviates from 100% (either greater or smaller), the more the station needs vehicle dispatching support.
Station traffic volume B = number of rentals + number of returns. The median, mode, and average of B were calculated using the MEDIAN, MODE, and AVERAGE functions respectively. The station importance value C was then calculated via the IF function as: C = IF(B>median,1,0) + IF(B>mode,1,0) + IF(B>average,1,0). The value range of C is [0, 6], and a larger C indicates a more important station.
The comprehensive score D of the station = number of rentals * X + number of returns * Y - number of abnormal rentals * Z, where X, Y, and Z are indicator weights, set to 40%, 40%, and 20% respectively in this calculation. A higher D means the station is more popular and important.
The rental-return gap = number of returns - number of rentals. The dispersion of the rental-return gap E = AVEDEV(rental-return gap), and the rental-return correlation F = CORREL(number of rentals, number of returns). Larger values of E and F indicate a more balanced overall rental and return operation in Linping District.
(3) Data Application: The operational management status of each station was evaluated using indicators A, C, and D, while the overall operational management status of Linping District was evaluated using E and F. Combining station-level and regional-level analysis provides data support for station maintenance and vehicle dispatching.

搜集汇总

数据集介绍

特点

该数据集包含杭州市临平区公共自行车站点的租还车数据，共562条记录，每月更新。通过分析站点租还比、车流量、重要值、综合评分等指标，为站点维护和车辆调度提供数据支持，提升运营管理效率。