共享单车驿站用车需求量预测数据

将共享单车投放区域进行区域划分，基于历史需求数据，对下一时间段用车量进行预测，通过实时收集的驿站现有车辆，及早进行调度，有利于解决城市交通“最后一公里”问题，方便市民出行，同时缓解了城市交通污染，促进城市绿色交通、慢行交通的发展。1.数据采集：由中控系统上报驿站共享单车的借车历时数据。
2. 数据预处理：对采集到的数据进行清洗、去重、格式转换等处理，使其满足后续分析和建模的要求。
3. 数据分析：以驿站为基本单位，在时间上，以时间粒度Δt为基本单位，即每Δt时间预测一次使用需求，并且区分考虑工作日、周末、节假日、重大活动不同时期，使用加权移动平均法，Ft＝ω1At‑1+ω2At‑2+……+ωnAt‑n(1)其中Ft为t时期的预测值，ω1为(t‑1)时期实际值的权重，At‑1为(t‑1)时期的实际借车量值。
4. 数据应用：根据预测驿站下个时间段的用车量，结合驿站实时停车量，及时做好车辆调度工作

This dataset is tailored for shared bike dispatching optimization. It involves dividing shared bike operation areas into zones, predicting bike demand in the subsequent time window based on historical demand data, and carrying out real-time dispatching using real-time available bikes at stations. This solution addresses the urban "last-mile" traffic challenge, facilitates residents' travel, mitigates urban traffic pollution, and boosts the development of urban green and non-motorized transportation.

1. Data Collection: Historical bike borrowing data of each bike station is reported via the central control system.
2. Data Preprocessing: Clean, deduplicate, convert formats and perform other necessary processing on the collected data to meet the requirements of subsequent analysis and modeling.
3. Data Analysis: Take each bike station as the basic unit, with the time granularity Δt as the basic time interval — that is, conduct demand prediction once per Δt period, and differentiate and consider different periods including weekdays, weekends, public holidays, and major events. The weighted moving average method is adopted, with the prediction formula as follows:
$F_t = omega_1 A_{t-1} + omega_2 A_{t-2} + dots + omega_n A_{t-n}$ (1)
Where $F_t$ is the predicted value at time t, $omega_1$ is the weight of the actual value at time (t-1), and $A_{t-1}$ is the actual bike borrowing volume at time (t-1).
4. Data Application: Conduct timely bike dispatching operations based on the predicted bike usage volume of the next time window for each station, combined with the real-time parked bike stock at the station.