义乌市义乌行公交单日结算分析数据

基于义乌行App的义乌市公交单日结算数据，通过对这些数据进行处理，智能算法构建交易特征、交通活力指数及异常监测体系，为公交运营管理提供决策支持。通过交易均价与折扣率分析制定差异化票价策略；自动识别异常交易行为并预警；结合节假日特征预测高峰时段运力需求；提升公交到站时间预测精度，改善乘客出行体验。可服务于交通管理部门、公交运营企业及城市规划机构。
(1)数据采集：导出义乌行后台统计出来的公交结算每日交易报表
(2)数据处理：对公交结算原始数据进行清洗校验，剔除异常值，提取日期、交易笔数、金额等核心字段，确保数据完整准确。
(3)数据加工：基于公交结算数据（日期、笔数、金额（单位元)），先分析基础数据：星期（提取结算日期中的星期，0代表周一，1代表周二，以此类推）、是否周末（根据星期判断是否是周末，1代表是，0代表否。）、是否节假日（提取结算日期判断节假日，1代表是，0代表否）、月份（提取结算日期中的月份）。然后通过算法构建分析指标：1）计算交易均价=合计金额/合计笔数、单笔优惠(元）=合计金额 - 折后金额、折扣率=折后金额/合计金额等基础指标；2）采用7/30日移动平均生成笔数/金额平均值，得到笔数_ma30、笔数_ma7、金额_ma30、金额_ma7；3）计算金额指数=合计金额/金额_ma30，笔数指数 = 合计笔数/笔数_ma30，构建交通活力指数（0.6*金额指数+0.4*笔数指数）；
(4)数据应用：将加工后的数据应用于公交调度优化、票价动态调整、异常交易预警等场景，为运营决策提供数据支撑，提升公交服务效率。识别异常折扣线路防范票务风险、通过节假日特征分析优化特殊时期排班、基于长期趋势数据辅助公交线网规划等，最终实现运营效率提升15%以上

Based on the daily bus settlement data of Yiwu City from the Yiwu Xing App, this dataset processes the data, uses intelligent algorithms to construct transaction features, a traffic vitality index and an anomaly monitoring system, providing decision support for bus operation management. Specifically, it develops differentiated fare strategies through analysis of transaction average price and discount rate; automatically identifies and alerts abnormal transaction behaviors; predicts peak-period transport capacity demand by incorporating holiday characteristics; improves the accuracy of bus arrival time prediction and enhances passengers' travel experience. This dataset can serve traffic management departments, bus operation enterprises and urban planning agencies.

(1) Data Collection: Export the daily bus settlement transaction reports counted from the backend of the Yiwu Xing App.

(2) Data Processing: Clean and validate the original bus settlement data, eliminate outliers, extract core fields including date, number of transactions, and transaction amount, to ensure the completeness and accuracy of the data.

(3) Data Enrichment: Based on the bus settlement data (date, number of transactions, amount, unit: Yuan), first analyze basic data:
- Day of week: extract the day of the week from the settlement date, where 0 represents Monday, 1 represents Tuesday, and so on;
- Weekend flag: judge whether it is a weekend based on the day of the week, with 1 indicating yes and 0 indicating no;
- Holiday flag: judge whether it is a holiday by extracting the settlement date, with 1 indicating yes and 0 indicating no;
- Month: extract the month from the settlement date.

Then construct analysis indicators via algorithms:
1) Calculate basic indicators such as transaction average price = total amount / total transaction count, per-transaction discount (Yuan) = total amount - discounted amount, discount rate = discounted amount / total amount;
2) Generate average values of transaction count and amount using 7-day and 30-day moving averages, obtaining transaction_count_ma30, transaction_count_ma7, amount_ma30 and amount_ma7;
3) Calculate the amount index = total amount / amount_ma30, transaction count index = total transaction count / transaction_count_ma30, and construct the traffic vitality index (0.6 * amount index + 0.4 * transaction count index).

(4) Data Application: Apply the processed data to scenarios such as bus scheduling optimization, dynamic fare adjustment and abnormal transaction alert, providing data support for operational decision-making and improving bus service efficiency. Specific applications include identifying routes with abnormal discounts to prevent ticketing risks, optimizing scheduling during special periods through holiday characteristic analysis, assisting bus network planning based on long-term trend data, and ultimately achieving an increase of more than 15% in operational efficiency.

搜集汇总

背景与挑战

背景概述

该数据集包含义乌市公交单日结算数据，涵盖交易均价、交通活力指数等19个字段，共1453条记录，每日更新。它通过智能算法构建交易特征和异常监测体系，用于优化公交运营管理，如票价策略制定和运力需求预测，服务于交通管理部门和企业。