将航班时刻表转化为安检人流预测信号，提升机场安检通过量预报精度。
Schedule-Informed Temporal Fusion Forecasting of Hourly Airport Security-Checkpoint Throughput
- 用截断泊松核将航班座位分布到出发前各时段，生成可预测的客流信号。
- 六小时直接预测误差仅9.33%，优于RNN和LSTM模型，高峰时段表现更优。
- 无需乘客-航班匹配，适合用于提前排班、通道开启及多日规划。
安检人力配置需要准确预估安检口的客流高峰期，但航班时刻表仅记录起飞时间，未反映乘客实际到达安检口的时间。本研究构建了一个将已知航班时刻表转换为时间对齐的客流强度信号以预测每小时安检通过量的框架。基于2023-2024年美国运输安全管理局（TSA）在亚特兰大哈茨菲尔德-杰克逊国际机场的数据，以及Cirium Diio航班数据，使用截断泊松核将国内与国际航班座位容量分配至出发前各小时。时间融合变压器（Temporal Fusion Transformer）结合这些由航班计划生成的客流信号、历史通过量、计划活动及时间变量进行建模。模型采用时间顺序训练，2024年7月至12月数据用于测试，共五组随机种子，评估结果对比循环神经网络（RNN）和长短期记忆网络（LSTM）。在直接六小时预测中，该模型加权平均绝对百分比误差为9.33%，优于RNN的12.16%和LSTM的11.37%；在高峰时段误差最低。六小时递归更新下，24至96小时预测范围误差维持在10.60%至11.04%之间，但长期预测可用起始点较少。该方法无需乘客与航班匹配即可将航班计划转化为可解释的安检负荷信号，支持提前排班、通道开启和多日规划。由于实际通过量反映的是实际处理能力而非未受约束的到达量，建议结合本地人力、容量、排队及等待时间信息共同解读预测结果。
原文摘要 · Abstract (English)
Checkpoint staffing requires accurate forecasts of when screening demand will occur, yet flight schedules record departure times rather than passenger arrival times at security checkpoints. This study develops a framework that converts known flight schedules into temporally aligned signals for forecasting hourly checkpoint throughput. Using 2023-2024 Transportation Security Administration throughput data and Cirium Diio flight schedules for Hartsfield-Jackson Atlanta International Airport, domestic and international seat capacity was distributed across pre-departure hours using truncated Poisson kernels. A Temporal Fusion Transformer then combined these schedule-derived arrival-intensity signals with historical throughput, scheduled activity, and temporal variables. Models were trained chronologically, with July-December 2024 reserved for testing, and evaluated against recurrent neural network and long short-term memory models across five random seeds. For direct six-hour forecasts, the proposed model achieved a weighted mean absolute percentage error of 9.33%, compared with 12.16% for the recurrent neural network and 11.37% for long short-term memory, while also producing the lowest errors during peak periods. With six-hour recursive updates, errors remained between 10.60% and 11.04% across 24-96 hour horizons, although longer horizons contained fewer valid forecast origins. By transforming scheduled departures into interpretable pre-departure screening-load signals without requiring passenger-flight matching, the framework supports advance staffing, lane-opening, and multiday checkpoint planning. Because observed throughput reflects realized processing rather than unconstrained arrivals, the forecasts should be interpreted together with local staffing, capacity, queue, and wait-time information.
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