金华客运站内各渠道售票占比分析数据

在客运站点的票务系统中，通过对售票数据的深入分析，优化资源分配、提升售票效率，改善乘客体验，具体应用场景包括：①分析不同线路和时间段的客流量，优化售票方式，提高窗口和网售的效率，调整资源配置，确保高峰期有足够的售票窗口和网络宽带；②分析窗口售票和网售的比例，制定营销策略，鼓励更多乘客使用网售平台，减少乘客排队时间。1、数据采集：通过巴巴驿站智慧站务系统，实时获取发车日期、隶属站名称、线路名称、到站站名、总人数、总金额、窗口人数、窗口金额、网售人数、网售金额等数据。 2、数据处理加工：可通过数据进行统计分析，计算每条线路、每个站点在不同时间段的总人数和总金额（筛选出相应的路线、站点、时间段对总人数和总金额进行累加）；窗口人数占比=窗口人数/总人数；网售人数占比=网售人数/总人数；网售金额占比=网售金额/总人数；窗口销售效率=窗口金额/窗口人数； 3、数据应用：根据这些数据分析，可以设定售票人数和金额的阈值，自动检测异常售票情况（如窗口售票和网售比例异常），对异常情况进行预警，提箱相关人员及时处理，根据不同线路和时间段的售票情况，调整票价和促销方案，提升公司效益。

In the ticketing systems of passenger transport stations, in-depth analysis of ticketing data is conducted to optimize resource allocation, improve ticketing efficiency and enhance passenger experience. The specific application scenarios include: ① Analyze the passenger flow of different routes and time periods, optimize ticketing methods, improve the efficiency of window and online ticketing, adjust resource allocation, and ensure sufficient ticketing windows and network bandwidth during peak hours; ② Analyze the proportion of window ticketing and online ticketing, formulate marketing strategies to encourage more passengers to use online ticketing platforms, and reduce passenger queuing time.

1. Data Collection: Real-time data including departure date, affiliated station name, route name, arrival station name, total passenger volume, total transaction amount, window ticketing volume, window ticketing amount, online ticketing volume and online ticketing amount are collected via the Baba Yizhan Smart Station Management System.

2. Data Processing and Analysis: Statistical analysis can be performed on the collected data to calculate the total passenger volume and total transaction amount of each route and each station in different time periods (accumulate the total passenger volume and total transaction amount by filtering corresponding routes, stations and time periods); the proportion of window ticketing volume = window ticketing volume / total passenger volume; the proportion of online ticketing volume = online ticketing volume / total passenger volume; the proportion of online ticketing amount = online ticketing amount / total passenger volume; window ticketing efficiency = window ticketing amount / window ticketing volume.

3. Data Application: Based on the analysis results, thresholds for ticketing volume and amount can be set to automatically detect abnormal ticketing situations (such as abnormal proportions of window ticketing and online ticketing), issue early warnings for abnormal conditions, prompt relevant personnel to handle them in a timely manner, and adjust ticket prices and promotion plans according to the ticketing status of different routes and time periods to improve corporate benefits.

搜集汇总

数据集介绍

特点

该数据集记录了金华客运站内各渠道售票的详细数据，包括窗口和网售的人数、金额及其占比，用于优化售票资源分配和提升售票效率。数据每日更新，适用于客运站点的票务系统分析和营销策略制定。