基于航班时刻的机场安检点每小时吞吐量时间融合预测

中文摘要 AI 辅助

安检点人员配置需要准确预测安检需求的发生时间，但航班时刻表记录的是离港时间而非旅客到达安检点的时间。本研究开发了一种框架，可将已知航班时刻表转换为时间对齐的信号，用于预测安检点每小时吞吐量。使用2023-2024年美国运输安全管理局（TSA）的吞吐量数据以及亚特兰大哈茨菲尔德-杰克逊国际机场的Cirium Diio航班时刻表，通过截断泊松核将国内和国际座位容量分配到离港前的各个时段。随后，采用时间融合Transformer（Temporal Fusion Transformer）将这些由时刻表衍生的到达强度信号与历史吞吐量、计划活动及时间变量相结合。模型按时间顺序训练，预留2024年7-12月作为测试集，并在5个随机种子下与循环神经网络（RNN）和长短期记忆（LSTM）模型进行对比评估。对于直接6小时预测，所提模型的加权平均绝对百分比误差（WMAPE）为9.33%，而循环神经网络为12.16%，长短期记忆模型为11.37%，且在高峰时段误差最低。在6小时递归更新下，24-96小时的预测区间内误差保持在10.60%至11.04%之间，尽管较长区间的有效预测起点较少。该框架通过将计划离港转换为可解释的离港前安检负荷信号，无需进行旅客-航班匹配，即可支持提前人员配置、通道开放及多日安检点规划。由于观测到的吞吐量反映的是已实现的处理情况而非无约束的到达情况，因此这些预测应结合当地人员配置、容量、队列及等待时间信息进行解读。

英文摘要

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.

发表机构

• School of Aviation and Transportation Technology, Purdue University（普渡大学航空与运输技术学院）
• Department of Geography, The Ohio State University（俄亥俄州立大学地理系）
• College of Aeronautics and Engineering, Kent State University（肯特州立大学航空与工程学院）

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