东阳共享电动车高峰停车点需求调度数据

通过分析历史订单的时空分布规律，预测各停车点在高峰时段的车辆需求缺口。基于预测结果，在高峰前1小时生成调度指令，解决因车辆分布不均导致的用户找车难、订单取消率高的问题，同时降低人工调度成本，提升高峰时段订单转化率。1、数据来源：从东阳创享汽车服务有限公司的创享出行共享电动车运营平台系统中，采集停车点数据、用户骑行订单数据。
2、数据处理：对采集的数据进行数据清洗，剔除异常用户订单数据（如骑行时长小于1分钟或大于60分钟的订单数据等）。提取最近30天的数据用于后续计算。3、数据计算：
（1）利用count函数计算每个停车点每个时段的订单总量（即最近30天订单总数）
（2）利用count函数计算每个停车点每个时段的平均订单量（即最近30天平均订单数）。
（3）利用max函数，获取每个停车点最近一个时段的车辆数（即当前车辆数）
（4）基于车辆需求模型对各个停车点进行标记：缺车（当前车辆数<0.8*最近30天平均订单数）、车辆过剩（当前车辆数> 1.5*最近30天平均订单数）

This dataset predicts the vehicle demand gap at each parking point during peak hours by analyzing the spatio-temporal distribution patterns of historical orders. Based on the prediction results, dispatch instructions will be generated one hour before peak hours to address the issues of users' difficulty in finding vehicles and high order cancellation rate caused by uneven vehicle distribution, while reducing manual dispatch costs and improving order conversion rate during peak hours.
1. Data Source: Parking point data and user riding order data are collected from the shared electric vehicle operation platform system of Chuangxiang Travel under Dongyang Chuangxiang Automobile Service Co., Ltd.
2. Data Processing: Data cleaning is conducted on the collected data, and abnormal user order data (such as orders with riding duration less than 1 minute or more than 60 minutes) are filtered out. The data of the latest 30 days are extracted for subsequent calculations.
3. Data Calculation:
(1) Utilize the count function to calculate the total order volume at each parking point per time slot (i.e., the total number of orders in the latest 30 days)
(2) Utilize the count function to calculate the average order volume at each parking point per time slot (i.e., the average number of orders in the latest 30 days)
(3) Utilize the max function to obtain the number of vehicles at each parking point in the latest time slot (i.e., the current number of vehicles)
(4) Mark each parking point based on the vehicle demand model: Vehicle Shortage (current number of vehicles < 0.8 * average number of orders in the latest 30 days), Vehicle Surplus (current number of vehicles > 1.5 * average number of orders in the latest 30 days)

搜集汇总

背景与挑战

背景概述

该数据集包含东阳共享电动车高峰停车点的需求调度数据，涵盖停车点信息、时段车辆数和订单统计等关键字段，用于预测高峰时段车辆需求缺口并优化调度。数据规模为1707条，每月更新一次。