工作日早高峰公交车客流稳定性分析数据

在智慧交通时代获取精准数据能为公交运营管理带来全新思路。金华行作为金华市公共交通出行的重要线上平台，汇聚了海量的用户乘车数据。通过收集一段时间内使用金华行APP 乘车的用户数据，将离散系数、标准差、变异系数三者结合，能够快速生成客流稳定性评估结果。公交运营部门可以依据这些结果，按照波动程度对线路进行排序，快速筛选出需要重点关注的线路，极大地缩小了问题排查范围。比如筛查出不稳定时段，运营部门可以深入分析其背后的原因，是由于途经区域有大型商业区、学校等导致特定时段客流集中，还是受到道路施工、临时交通管制等外部因素影响，从而采取不同的应对策略。1、数据采集：从金华行数据库里获取2025年4月的线路号、线路名、用户手机号、上车日期、上车时间等数据。2、数据筛选：根据金华市工作日早高峰时间点（早上七点到九点），增加时间标、日期类型、星期字段，提取出工作日早高峰时间段内数据，对数据进行清洗，去除极限值、缺失值。3、数据处理：用COUNTIF计算当天当前小时客流、当天客流总数，当天小时平均客流=当天客流总数/3（早上七点到九点）。用MAXIF、MINIF分别计算出当天小时尖峰客流、当天小时低谷客流，计算得到当天的离散系数=（当天小时尖峰客流-当天小时低谷客流）/当天小时平均客流。用STDEV计算标准差，得到变异系数=标准差/当天小时平均客流*100%。稳定分=离散系数*K1+标准差*K2+变异系数*K3，其中K1、K2、K3为各指标对应系数，系数分别为3、0.03、40。
4、数据分类：稳定分小于10判断为稳定，稳定分大于25判断为不稳定，其余情况判定为较为稳定。

In the era of smart transportation, acquiring accurate data can provide novel insights for bus operation management. Jinhua Xing, an important online public transit travel platform in Jinhua City, accumulates a vast volume of user ride data. By collecting user ride data from the Jinhua Xing APP over a designated period and combining the range coefficient of dispersion, standard deviation, and coefficient of variation, one can rapidly generate passenger flow stability assessment results. Bus operation departments can sort routes by their fluctuation levels based on these results, rapidly screen out routes requiring key attention, and greatly narrow the scope of problem investigation. For instance, after identifying unstable time periods, the operation department can conduct in-depth analysis of their underlying causes: whether the concentrated passenger flow during specific periods is caused by areas along the routes with large commercial districts, schools and other public facilities, or by external factors such as road construction or temporary traffic control, so as to adopt targeted response strategies. 1. Data Collection: Retrieve data including route number, route name, user's mobile phone number, boarding date, and boarding time from the Jinhua Xing database for April 2025. 2. Data Screening: According to the morning peak hours (7:00 to 9:00 AM) on working days in Jinhua City, add three fields: time stamp, date type, and week. Extract data within the morning peak period of working days, and clean the dataset by removing extreme values and missing values. 3. Data Processing: Use the COUNTIF function to calculate the passenger flow per hour on the current day and the total daily passenger flow; the daily average hourly passenger flow = total daily passenger flow / 3 (covering 7:00 to 9:00 AM). Use the MAXIF and MINIF functions to calculate the daily peak hourly passenger flow and daily lowest hourly passenger flow, respectively. The daily range coefficient of dispersion is calculated as (daily peak hourly passenger flow - daily lowest hourly passenger flow) / daily average hourly passenger flow. Use the STDEV function to calculate the standard deviation, and the coefficient of variation is obtained as (standard deviation / daily average hourly passenger flow) * 100%. The stability score is formulated as: Stability Score = Range Coefficient of Dispersion * K1 + Standard Deviation * K2 + Coefficient of Variation * K3, where K1, K2, and K3 are the respective weights of each indicator, with values of 3, 0.03, and 40, respectively. 4. Data Classification: A stability score less than 10 is classified as stable, a score greater than 25 is classified as unstable, and the remaining cases are classified as relatively stable.

搜集汇总

背景与挑战

背景概述

该数据集包含43163条记录，聚焦工作日早高峰公交车客流数据，通过离散系数、标准差和变异系数等指标评估客流稳定性，旨在辅助公交运营部门优化线路管理。数据来源于金华行APP的乘车记录，采用Excel格式，按需更新，适用于智慧交通场景下的问题排查和策略制定。