In this paper we propose a novel approach for validating a simulation model for a passengers' airport terminal. The validation approach is based on a "historical data" and "model-to-model" validation approach, and the novelty is represented by the fact that the model used as comparison uses historical data from different data sources and technologies. The proposed validation approach , which is presented as part of the IMHOTEP project, implements various data fusion and data analytics methods to generate the passenger "Activity-Travel-Diary", which is the model that is then compared with the results from the simulation model. The data used for developing the "Activity-Travel-Diary" comes from different sources and technologies such as: passengers data (personal mobile phone, apps), airport data (airport Wi-Fi, GPS, scanning facilities), and flight Information (flight schedules, gate allocation etc.). The simulation model is based on an agent-based simulation paradigm and includes all the passengers flows and operations within a terminal airport. The proposed validation approach is implemented in a real-life case study, Palma de Mallorca Airport, and preliminary results of the validation (calibration) process of the simulation model are presented.
The project X-TEAM D2D (extended ATM for door-to-door travel) has been funded by SESAR JU in the framework of the research activities devoted to the investigation of integration of Air Traffic Management (ATM) and aviation into a wider transport system able to support the implementation of the door-to-door (D2D) travel concept. The project defines a concept for the seamless integration of ATM and Air Transport into an intermodal network, including other available transportation means, such as surface and waterways, to contribute to the 4 h door-to-door connectivity targeted by the European Commission in the ACARE SRIA FlightPath 2050 goals. In particular, the project focused on the design of a concept of operations for urban and extended urban (up to regional) integrated mobility, taking into account the evolution of transportation and passengers service scenarios for the next decades, according to baseline (2025), intermediate (2035) and final target (2050) time horizons. The designed ConOps encompassed both the transportation platforms integration concepts and the innovative seamless Mobility as a Service, integrating emerging technologies, such as Urban Air Mobility (e.g., electric vertical take-off and landing vehicles) and new mobility forms (e.g., micromobility vehicles) into the intermodal traffic network, including Air Traffic Management (ATM) and Unmanned Traffic Management (UTM). The developed concept has been evaluated against existing KPAs and KPIs, implementing both qualitative and quantitative performance assessment approaches, while also considering specific performance metrics related to transport integration efficiency from the passenger point of view, being the proposed solution designed to be centered around the passenger needs. The aim of this paper is to provide a description of the activities carried out in the project and to present at high level the related outcomes.
This study examines the effect of seat assignment strategies on the transfer time of connecting passengers at a hub airport. Passenger seat allocation significantly influences disembarkation times, which can increase the risk of missed connections, particularly in tight transfer situations. We propose a novel seat assignment strategy that allocates seats to nonpaying passengers after check-in, prioritising those with tight connections. This approach diverges from traditional methods focused on airline turnaround efficiency, instead optimizing for passenger transfer times and reducing missed connections. Our simulation, based on real-world data from Paris-Charles de Gaulle airport, demonstrates that this passenger-centric model decreases missed connections by 12%, enhances service levels, reduces airline compensation costs, and improves airport operations. The model accounts for variables such as seat occupancy,luggage, and passenger type (e.g., business, leisure) and is tested under various scenarios, including air traffic delays.
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