临海市公交车动态发车频率分析数据

通过临海市民卡系统实时采集乘客刷卡记录，可得到市民乘车线路号、年龄、乘车时间等数据。基于市民卡系统刷卡数据，通过分析不同时间段，不同年龄段市民乘车出行情况，结合单班次车辆容量数据，可设计出公交线路动态发车频率模型，从而实现自动生成各线路分时段的发车频率建议，降低人工调度成本。同时可向网约车平台提供高需求时段的运力缺口数据。1.数据收集：公交线路动态发车频率模型是基于市民卡系统的脱敏数据（包括线路号、乘车时间、年龄段等）。 2.数据运算：通过核心运算公式：F=(m/n)×α，可计算得到动态发车频率。公式中，F为动态发车频率，代表经计算所得的线路i在时段t的最佳发车频率；m为乘车总人数，代表线路i在时段t的总客流量，n为该线路公交车单辆车的座位数。 α为弹性调节系数，根据线路i在时段t的60岁及以上老年乘客占比p进行弹性调节，p=d/m，d为60岁及以上老年乘客人数。调节规则为：p＞40%时，α设为1.5；30%≤p≤40%时，α设为1.3；p＜30%时，α设为1.2。 3.数据分析：例如样例数据结构中的动态发车频率F值为3.80，则证明2025年5月1日6:30-7:30这一时间段内，205线路公交车最佳发车频率为3.80，综合考虑当日的时间节点（节假日期间）、异常处理（如应对突发客流）和资源约束（总班次上限）等原因，为达到需求匹配与成本平衡，建议发车4个班次。Passenger swiping records are collected in real time via the Linhai Citizen Card System, allowing acquisition of data including citizens' bus route numbers, ages, boarding times and other relevant information. Based on this swiping data, by analyzing the travel patterns of citizens across different time periods and age groups, combined with per-bus capacity data of bus routes, a dynamic bus departure frequency model can be developed. This model can automatically generate time-segmented departure frequency recommendations for each route, thereby reducing manual scheduling costs. Meanwhile, data on capacity gaps during high-demand periods can be provided to online ride-hailing platforms. 1. Data Collection: The dynamic bus departure frequency model is built upon de-identified data from the citizen card system, including route numbers, boarding times, age groups and other related information. 2. Data Calculation: The dynamic departure frequency can be calculated using the core formula: F=(m/n)×α. In this formula, F denotes the optimal departure frequency of route i during time period t; m represents the total passenger volume of route i during time period t; n is the number of seats per bus of the target route; α is the elastic adjustment coefficient, which is adjusted based on the proportion p of passengers aged 60 and above on route i during time period t, where p=d/m and d is the number of passengers aged 60 and above. The adjustment rules are: set α=1.5 when p>40%; set α=1.3 when 30%≤p≤40%; set α=1.2 when p<30%. 3. Data Analysis: For example, if the dynamic departure frequency F value in the sample data structure is 3.80, this indicates that during the period from 6:30 to 7:30 on May 1, 2025, the optimal departure frequency of Bus Route 205 is 3.80. Taking into account factors such as the time node (holiday period), exception handling (e.g., responding to sudden passenger surges) and resource constraints (total shift limit) on that day, to balance demand matching and costs, it is recommended to arrange 4 shifts.

搜集汇总

背景与挑战

背景概述

该数据集聚焦临海市公交车动态发车频率分析，包含1006条记录，每日更新，通过市民卡系统采集乘客刷卡数据，结合乘车人数、座位数和老年乘客占比等字段，运用算法模型自动计算最佳发车频率，旨在优化公交调度效率和降低人工成本。