关键词: 交通运输工程；航空运输；多维特征增强模块（MFE）；消融实验；Informer模型；日客运量预测

Abstract: Aiming at the problem of insufficient utilization of multi-dimensional features of holiday in current daily passenger volume prediction models in the transportation sector, an MFE-Informer model that incorporated multi-dimensional feature enhancement (MFE) module was proposed, in which the information such as holiday type, duration, and surrounding time periods was mapped into multi-dimensional features. By integrating the MFE module with the multi-head attention layer, the proposed model could adaptively focus on feature variations of holidays, thereby enhancing its capability to model the impact of holidays and long-term trends. Taking the daily passenger volume data from a certain airport in 2019 as an example. The daily passenger valume forecasting was carried out. The research results show that compared to baseline models such as long short-term memory (LSTM) model, Informer and Informer with binary labels, the MFE-Informer model performs better in terms of evaluation metrics such as correlation coefficient (R2), mean squared error (EMS), mean absolute error (EMA), and root mean squared error (ERMS). Furthermore, three typical travel scenarios (Tomb-Sweeping Day, a certain 5-day period during peak season, and National Day holiday) are selected for validation. The MFE-Informer model maintains smaller prediction errors and faster trend response capability, which confirms the robustness of the proposed model across different travel patterns.

Key words: traffic and transportation engineering; air transportation; multi-dimensional feature enhancement module (MFE); ablation experiments; Informer model; daily passenger volume forecasting