共享单车调度系统数据

利用划分禁止停放区域和奖、惩方式激励、引导用户自发得将违停单车从禁止停放区域骑出并且停放在非禁止停放区域，降低了共享单车运营商的调度管理成本、增加了共享单车的使用率，并且调度用户享受了骑行折扣，降低了其骑行成本。1、数据采集：记录ID，用户ID，车辆编号，起始纬度，起始经度，开始时间，结束纬度，结束经度，结束时间，骑行距离(米)，骑行时长(分钟)，订单费用。 2、数据处理：对采集到的原始数据进行处理，去除缺失和异常数据，计算共享单车的位置偏差，通过预先训练得到的深度学习模型得出允许位置偏差值n。3、数据分析：如果用户将违停单车从禁止停放区域骑出并且停放在非禁止停放区域，该用户将成为调度用户，骑行费用将享受折扣。通过经纬度计算确认该用户是否为调度用户，P1=IFS(W1＞n, "折扣K1","正常骑行费用")，P2=IFS(W2＞n,"附加费率K2", "正常骑行费用")，W1：起始位置与最近固定停车位置偏差值，W2：终始位置与最近固定停车位置偏差值。4、数据应用：利用划分禁止停放区域和奖、惩方式激励、引导用户自发得将违停单车从禁止停放区域骑出并且停放在非禁止停放区域，降低了共享单车运营商的调度管理成本、增加了共享单车的使用率，并且调度用户享受了骑行折扣，降低了其骑行成本。

This dataset is developed to incentivize and guide users to voluntarily ride illegally parked shared bikes out of no-parking zones and park them in permitted parking areas via defining restricted parking zones and implementing reward and punishment mechanisms. Such measures reduce the dispatching and management costs of shared bike operators, increase the usage rate of shared bikes, and enable participating dispatching users to enjoy riding discounts, thereby lowering their own riding expenses.

1. Data Collection: The collected data includes Record ID, User ID, Vehicle Number, Starting Latitude, Starting Longitude, Start Time, Ending Latitude, Ending Longitude, End Time, Riding Distance (meters), Riding Duration (minutes), and Order Fee.

2. Data Processing: Process the collected raw data by eliminating missing and abnormal records, calculate the position deviation of shared bikes, and derive the allowable position deviation value n through a pre-trained deep learning model.

3. Data Analysis: If a user rides an illegally parked shared bike out of a no-parking zone and parks it in a permitted parking area, the user will be categorized as a dispatching user and eligible for a riding discount. Verify whether the user is a dispatching user by calculating via longitude and latitude, with the following decision formulas:
P1=IFS(W1＞n, "Discount K1", "Normal Riding Fee")
P2=IFS(W2＞n, "Additional Fee K2", "Normal Riding Fee")
Where W1 represents the deviation value between the user's starting position and the nearest fixed parking spot, and W2 represents the deviation value between the user's ending position and the nearest fixed parking spot.

4. Data Application: By defining restricted parking zones and implementing reward and punishment mechanisms, this solution incentivizes and guides users to voluntarily ride illegally parked shared bikes out of no-parking zones and park them in permitted parking areas, which reduces the dispatching and management costs of shared bike operators, increases the usage rate of shared bikes, and allows dispatching users to enjoy riding discounts to lower their own riding expenses.

搜集汇总

数据集介绍

特点

该数据集包含2001条共享单车骑行记录，每月更新，用于通过奖惩机制优化单车调度，降低运营成本并提高使用率。