Flexible Airport Passenger Flow Estimation

准确确定机场客流量（APF）对于有效地管理机场流程和分配资源至关重要，对于提供无缝的旅客旅程体验至关重要。目前，APF的估计存在一些局限性。一个主要的限制是缺乏解释能力的APF估计模型。可解释的网络将允许设计一个透明网络，通过提供可追溯的推理过程，帮助明确识别机场运营瓶颈和与客流相关的资源背后的原因，从而实现有效的决策，以优化运营。博士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。研究成果将为加快智能机场的发展提供重要机遇。

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.