Abstract:

The arrival of passengers at airports exhibits certain aggregation and randomness, and an non-stationary arrival process can easily lead to wastage of service resources. To dynamically allocate service resources at airport security checks, accurate prediction of passenger flow at various times based on flight schedules is necessary. By the time the security equipment reads the passenger identification information, the passenger has already completed the queuing process, which implies that the actual arrival time at the security check is earlier than the recorded security check time. The current process of security checks cannot record the exact time at which each passenger arrives at the inspection area. Existing research on airport passenger flow forecasting has replaced the time at which passengers begin to undergo security checks with the time at which they arrive at the security check area, ignoring the queuing time for security checks, which leads to lower prediction accuracy. To address this issue, a method for forecasting passenger flow at airport security checks based on a non-stationary queuing model is proposed. First, a security check queuing model with a general distribution, non-stationary random arrival, and service processes is established, and an approximate algorithm is proposed to solve the system's queuing performance indicators. Second, a heuristic iterative algorithm based on a non-stationary queuing model is designed to iteratively calculate passenger arrival times. Finally, a passenger arrival distribution model is established based on a time-segmented clustering and fitting method. The analysis results show that, compared to existing methods of calculating passenger arrival times, the proposed method reduces the error by approximately 15.07%. The proposed model demonstrates good predictive accuracy and versatility in multiple case studies. It can also make relatively accurate predictions for specific dates such as holidays and has a good over-forecasting effect, facilitating the allocation of service resources. The forecasting method based on the non-stationary queuing model can provide a data-dependent foundation for the dynamic allocation of service resources at airport security checks.

Key words: passenger flow prediction, clustering analysis, distribution fitting, non-stationary queuing model, Stationary Backlog-Carryover(SBC) approximate method

摘要：

机场旅客到达过程存在一定的聚集性和随机性, 不平稳的到达过程很容易导致服务资源浪费。为了动态配置机场安检区的服务资源, 根据航班计划精准预测各个时间段的安检旅客流量。在安检过程中, 安检设备读取旅客证件信息, 旅客此刻已经完成了排队过程, 旅客实际到达安检区的时间早于安检时间。目前的安检流程无法记录每个旅客到达旅检区的时间。针对机场旅客量预测的研究中, 都是将旅客开始接受安检时刻替代为旅客到达安检区时刻, 忽略旅客等待安检的排队时间, 导致预测结果精确度降低。针对此问题, 提出一种基于非平稳排队模型的机场安检客流预测方法。首先, 建立具有一般分布、非平稳随机到达和服务过程的安检排队模型, 提出近似算法求解系统排队性能指标; 其次, 设计基于非平稳排队模型的启发式迭代算法, 迭代推算旅客到达时间; 最后, 基于分时段聚类-拟合方法, 建立航班旅客到达分布模型。实验结果表明, 相比现有的旅客到达时间处理方法, 推算到达时间的预测方法误差降低约15.07%, 分时段聚类-拟合方法建立的航班旅客到达分布模型在多个算例中都具有良好的预测准确性和泛用性, 在处理节假日等特殊日期的数据时, 也能够得到较精确的预测结果, 且具有较好的过量预测效果, 便于后续的服务资源配置。基于非平稳排队模型的机场安检客流预测方法可为动态配置机场安检区服务资源提供数据基础。

关键词: 旅客量预测, 聚类分析, 分布拟合, 非平稳排队模型, 积压后移平稳近似方法