宁波机场旅客流量预测数据

1.宁波机场可以利用预测的每日不同时段旅客流量数据识别客流量的变化情况，合理分配安检、地勤、清洁等人力资源，以及登机口、行李传送带等物理资源。
2.本数据可为机场内商家优化营业时间和库存以最大化利润提供决策支持。
3.本数据可为航空公司利用客流量预测数据优化航班调度提供一定的数据支持。
4.本数据可共享给交通管理部门，其可根据预测的客流量数据为优化公共交通服务配置提供支持，如公交、地铁的班次和时间。1.数据收集和预处理：（1）数据收集：收集每日机场客流量的统计信息，包括统计时间、统计地点、统计类别、0-6点客流量、6-12点客流量、12-18点客流量、18-24点客流量、当天总客流量。（2）数据预处理：对采集到的原始数据进行处理，去除缺失和异常数据。
2.计算当天时段平均客流量：当天时段平均客流量=当天总客流量/4；
3.计算各时段的客流量指数：各时段的客流量指数=（该时段的客流量/当天总客流量）×100%，各时段的客流量指数大于100%表示该时段客流量高于平均，小于100%表示低于平均。
4.使用ARIMA（自回归积分滑动平均）模型（一种用于分析按时间顺序排列的数据点，以识别趋势、周期性和其他模式的统计模型）进行时间序列分析，基于历史各时段客流量数据，并利用各时段的客流量指数作为调整因子来提高预测准确定，来预测未来各时段的机场客流量。

1. Ningbo Airport can utilize the predicted passenger flow data at different daily time periods to identify changes in passenger volume, and rationally allocate human resources such as security inspection personnel, ground handling staff, and cleaning staff, as well as physical resources including boarding gates and baggage conveyor belts.
2. This dataset can provide decision support for merchants within the airport to optimize their business hours and inventory in order to maximize profits.
3. This dataset can provide data support for airlines to optimize flight scheduling using passenger flow prediction data.
4. This dataset can be shared with traffic management departments, which can use the predicted passenger flow data to support the optimization of public transportation service configurations, such as bus and subway schedules and timetables.
1. Data Collection and Preprocessing:
(1) Data Collection: Collect statistical information of daily airport passenger flow, including statistical time, statistical location, statistical category, passenger flow volume from 0:00 to 6:00, from 6:00 to 12:00, from 12:00 to 18:00, from 18:00 to 24:00, and total daily passenger flow.
(2) Data Preprocessing: Process the collected raw data to eliminate missing and abnormal data.
2. Calculate the average passenger flow per daily time segment: Average passenger flow per daily time segment = Total daily passenger flow / 4;
3. Calculate the passenger flow index for each time segment: Passenger flow index for each time segment = (Passenger flow of this time segment / Total daily passenger flow) × 100%. A passenger flow index greater than 100% indicates that the passenger flow of this time segment is higher than the average, while a value less than 100% indicates lower than the average.
4. Use the ARIMA (AutoRegressive Integrated Moving Average) model, a statistical model used to analyze chronologically ordered data points to identify trends, periodicity and other patterns, to conduct time series analysis. Based on historical passenger flow data of each time segment, and using the passenger flow index of each time segment as an adjustment factor to improve prediction accuracy, predict the airport passenger flow for each future time segment.

搜集汇总

数据集介绍

特点

宁波机场旅客流量预测数据是由宁波机场集团有限公司提供的企业数据，包含993条记录，每日更新。数据详细记录了宁波栎社国际机场的客流量信息，并利用ARIMA模型进行未来客流量的预测，应用场景广泛，包括机场资源分配、商家决策支持等。