This study develops an integrated dynamic routing and off-block (IDRO) optimization framework for real-time airport surface operations. Instead of relying on complex taxi time prediction models, the proposed approach employs adynamic route impedanceformulation as a practical heuristic to capture real-time congestion conditions on the airport surface. The impedance integrates domain-specific operational factors, including free flow taxi time, turning penalties, and potential aircraft conflicts, enabling real-time route evaluation and decision-making without extensive simulation. Building upon this impedance representation, the IDRO framework coordinates both taxi routing and off-block release timing under a real-time First-Come-First-Served (FCFS) principle. The approach was implemented and tested using a high-fidelity cellular automata simulator calibrated with operational data from Beijing Capital International Airport. Compared with real-world baseline operations, the proposed framework reduced average taxi time, flight delay, and taxi conflicts by 10.43%, 24.27%, and 31.82%, respectively. A comparative study with another similar approach in the literature also reveals the advantage of the proposed strategy. These results demonstrate the framework’s computational efficiency, consistent performance across scenarios, and strong potential for real-world deployment in large-scale airport environments.

We introduce Aeolus, a large-scale Multi-modal Flight Delay Dataset designed to advance research on flight delay prediction and support the development of foundation models for tabular data. Existing datasets in this domain are typically limited to flat tabular structures and fail to capture the spatiotemporal dynamics inherent in delay propagation. Aeolus addresses this limitation by providing three aligned modalities: (i) a tabular dataset with rich operational, meteorological, and airportlevel features for over 50 million flights; (ii) a flight chain module that models delay propagation along sequential flight legs, capturing upstream and downstream dependencies; and (iii) a flight network graph that encodes shared aircraft, crew, and airport resource connections, enabling cross-flight relational reasoning. The dataset is carefully constructed with temporal splits, comprehensive features, and strict leakage prevention to support realistic and reproducible machine learning evaluation. Aeolus supports a broad range of tasks, including regression, classification, temporal structure modeling, and graph learning, serving as a unified benchmark across tabular, sequential, and graph modalities. We release baseline experiments and preprocessing tools to facilitate adoption. Aeolus fills a key gap for both domain-specific modeling and general-purpose structured data research.Our source code and data can be accessed at https://github.com/Flnny/Delay-data