Flexible Airport Passenger Flow Estimation

准确确定机场客流是有效管理机场流程和分配资源的关键，也是提供无缝旅客出行体验的基础。目前，机场客流的估计有几个局限性。一个主要的限制是缺乏对APF估计模型的解释能力。一个可解释的网络将允许设计一个透明的网络，通过提供一个可追溯的推理过程，使有效的决策能够优化运营，从而帮助清楚地识别机场运营瓶颈背后的原因和与客流有关的资源。PHD将演示可解释的时间序列数据建模，特别关注灵活的机场客流预测。研究成果将对加快智能机场的发展具有重大意义。

Accurately determining Airport passenger Flow (APF) is critical to efficiently manage airport processes and allocate resources and is fundamental to the provision of a seamless passenger journey experience.Currently, the estimation for APF have several limitations. One major limitation is the lack of explainable ability for the APF estimation model. An explainable network will allow the design of a transparency network that helps to clearly identify reasons behind the bottlenecks in airport operations and resources that relate to passenger flow, by providing a traceable inference process that enables effective decision-making to optimise operations.The PhD will demonstrate explainable time-series data modelling with the particular focus on flexible airport passenger flow prediction. The research outcomes will have significant opportunity to accelerate the development of smart airport.