Airlines' Network Analysis on an Air-Rail Multimodal System

Journal of Open Aviation Science (2023), Vol.1 doi:10.59490/joas.2023.7223 PROCEEDINGS | The 11th OpenSky Symposium Airlines’ Network Analysis on an Air-Rail Multimodal System Luis Delgado,*,1 Gérald Gurtner,1Tatjana Bolić,1César Trapote-Barreira,2and Adeline Montlaur2 1University of Westminster, London, United Kingdom 2Universitat Politècnica de Catalunya, Barcelona, Spain *Corresponding author: [email protected] (Received: 25 October 2023; Revised: 29 February 2024 and 5 March 2024; Accepted: 28 March 2024; Published: 30 March 2024) (Editor: Tatiana Polishchuk; Reviewers: Vishwanath Bulusu and Max Li) Abstract This article explores the potential impact of short-haul flight bans in Spain. We build the rail and flight network for the Spanish peninsula, merging openly available ADS-B-based data, for the reconstruction of air schedules and aircraft rotations, and rail operator data, for the modelling of the rail network. We then simulate a ban that would remove flights having a suitable train replacement, i.e., representing a trip shorter than a threshold that we vary continuously up to 15-h. We study the impact in terms of 1) air route reduction, 2) aircraft utilisation and fleet downsizing for airlines, 3) airport infrastructure relief and rail network requirements, 4) CO2emissions and 5) possible itineraries and travel times for passengers. We find that a threshold of 3 hours (banning all flights with a direct rail alternative faster than three hours) presents some notable advantages in emissions while keeping the aircraft utilisation rate at an adequate level. Interestingly, passengers would then experience an increase in their itinerary options, with only a moderate increase in their total travel times. Keywords: multimodality; modal shift; network analysis; airline fleet; emission reduction Abbreviations: SIBT: Schedule In-Block Time, SOBT: Schedule Off-Block Time 1. Introduction The route network is a key airline asset that defines its market and resource allocation. Main network models are point-to-point (usually used by low-cost airlines) and hub-and-spoke (traditionally operated by legacy airlines and airline alliances); the latter increases the airline’s potential connectivity, as the short-haul flights bring passengers to a hub where they are distributed to onward flights (often long-haul ones). However, short-haul flights are less fuel efficient per passenger-km performed [1], thus creating a higher environmental impact. European mobility strategy calls for green, smart and affordable mobility [2], calling for emissions reductions across transport modes and multimodality. In that context, railways are becoming an important part of the transport network as their emissions are lower and, in the context of door-to-door mobility, they offer easy access due to the city-centrality of rail stations. These characteristics point to the possibility of replacing certain flights by rail, reducing the emissions, to be balanced out by the high level of connectivity offered. For example, policies such as banning short-haul flights in France have very limited impact [3] on emissions as connecting fights are except. A similar policy is part of the governance arrangements for the potential coalition Spanish government, driving a reduction on domestic flights when suitable, faster than 2.5 hours, rail alternative is available, but still excluding flights with international ©2023 by the authors. This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) licence (https://creativecommons.org/licenses/by/4.0/) 2Luis Delgado et al. connections [4]. If more ambitious policies are to be implemented, the multimodal analysis of the remaining network, focusing on connectivity, is required [5]. The substitution of flights by rail alternatives in a disjointly planned rail-air network would impact passengers’ connectivity and travel time; airlines should re-design their fleet assignment to account for removed flights; airports would experience modified demand for operations, and rail operations would need to provide capacity for additional passenger demand. Thus, any new policy should be informed by the appropriate analyses. Modal choice studies are not new, and the substitution potential of air and rail has been addressed, with travel time and frequency being among the most relevant factors determining travel behaviour [6,7]. Another important factor is the environmental impact of different transport modes. For example, Avogadro et al. analysed air and rail route substitutability in Europe and found that when the main factors are travel time and costs, substitution could reduce emissions by about 5% [8]. As discussed, any analysis of substitution or cooperation between air and rail needs to assess the impact of the changes on the operators’ networks, infrastructure requirements, passenger connectivity, and travel times. Despite the interest in this paradigm, until recently, it has been difficult to carry out these types of analyses due to the lack of public and integrated datasets for railway and airline services in Europe. New data-sharing initiatives in the rail community are arising, such as the release of datasets, including rail timetables, by Renfe, the main Spanish rail service provider [9]. Due to the economic sensitivity of schedules and fleet usage, airlines are reluctant to share equivalent datasets covering adequate periods [10]. Research contribution. The research presented here analyses a case study of full-mode substitution between air and rail applied to operations within Spain. The goal is to assess the impact of substituting flights impacted by a banning policy and airline fleet reorganisation, potential passenger connectivity changes, travel times and total emissions estimation. As the available data allows assessing different assumptions, we consider a particular form of substitution: flight/s which operate on a route served by at least a train faster than a given threshold (0-15 hours) are banned. The case study centres on airlines’ operations with flights within Spain considering their alliances enabling passengers’ connectivity and itineraries1. OpenSky data [11] is used to approximate airlines’ schedules and track the aircraft to model their fleet utilisation, and Renfe data [9] to model the rail network. In Section 2, the data sourcing and preparation and methodology applied are presented before describing the results in Section 3. Finally, conclusions, future work and limitations of the data are discussed in Section 4. 2. Data and approach This section covers the data and methodology used. We start by describing the data sources and the needed data cleaning and preparation for the case study. We then describe the methodology applied in the case study. We will analyse the impact of introducing a flight ban in Spain, which eliminates the flights operating routes served by rail faster than a given threshold. We will explore the impact of these thresholds by ranging them from zero (no-ban) to 15 hours at 15-minute intervals for particular days in May 2023. 1Itinerary consists of one or more flights (and trains) between an origin-destination pair. Journal of Open Aviation Science 3 2.1 Data description and preparation A week of air traffic in May 2023 has been used in the analysis, with flights arriving/departing from the 1st to the 7th of May 2023, to, from and within Spain. Note that some flights might depart on the 30th April or land on the 8th of May. Further, the 1st of May is a public holiday in Spain, and the 2nd May is a public holiday in the region of Madrid. This week has been selected as it is a busy week in spring. We expected that public holidays might impact rail service availability; however, after analysing the dataset, we found a similar number of rail services daily. Using a week, instead of a single day, enabled us to capture air services not provided daily within Spain. As we focus on potential mobility, i.e., potential flight and multimodal itineraries that passengers could use, and not on the actual demand, results can be considered generic enough to represent the typical possible mobility within Spain. Results are expected to be stable due to the characteristics of the Spanish air and rail network (with air hubs in Madrid, Barcelona and Malaga, and a radial high-speed network to/from Madrid), as shown in Section 3.1. Further analysis, including passenger demand, should be performed considering different periods of the year to capture potential demand season patterns. Table 1summarises the different data sources used for the analysis, while the particular data cleaning and preparation is described in the following sections. All data used can be accessed from [12]. Table 1. Data sources Data name Description Scope Provider Flight data 4 Table from OpenSky historical database with basic flight information per flight Worldwide OpenSky [11,13] Aircraft database Table with information on aircraft (transponder Id, registration, model, etc.) Worldwide OpenSky [11,13] Trips Information on train trips. A trip is a given train service following a set of stops at defined times. A trip is for a given route and service Spain Renfe [9] Stop times Lists of stops with stopping times per trip Spain Renfe [9] Routes Information on routes by rail services. Different trips might use the same route, stopping at the same or different stations. Routes are classified by the type of rail service, e.g., AVE, Regional, Intercity Spain Renfe [9] Calendar Dates of the week in which services are run between given dates Spain Renfe [9] Stops Information on stops (stations) Spain Renfe [9] Rail fleet Information on seats per rail service Spain Renfe [14] Ecopassenger Information on emissions per rail service European International Railways Union [15] Airports (a/p) Airports’ coordinates Worldwide Collected by the authors [16] A/p manually modified List of airport codes swapped as erroneous departure or arrivals, explained in Section 2.1.1. – Generated by the authors [16] 2.1.1 Airline network data preparation First, using the data from OpenSky (flight data 4), we identified the airlines operating commercial flights within Peninsular Spain, as these could be potentially replaced by rail. Thus, only flights operated by these airlines are considered in the analysis: Vueling (VLG), Ryanair (RYR), AirNostrum (ANE), AirEuropa (AEA), Iberia (IBE) and IberiaExpress (IBS). To use the data in this case study, it 4Luis Delgado et al. still needed to be cleaned and prepared. Despite the improvement in identifying the departure or arrival airports of flights (in the flight data 4) by OpenSky, they are still often erroneously identified2. The errors are generated due to the potential loss of ADS-B traces near the ground in some regions. For example, the small airfield of Lucca (LIQL), which cannot accommodate passenger aircraft, is recorded as the destination of a commercial flight instead of the nearby International Airport of Pisa (LIRP). By manually exploring these cases and using domain knowledge, 61 airport substitution pairs are defined by the authors. The airport substitutions list is available in [16]. Further checks were performed, as aircraft rotations3were broken in some cases. For example, an aircraft arrives at airport X, but the same aircraft departs subsequently from airport Y, or an aircraft with arrival/departure to an unidentified destination (NULL). An algorithm has been developed to correct these rotations. The process is as follows (for each aircraft where the arrival and departure of subsequent flights do not match): 1. If in one flight one of the airports is not identified in the dataset, i.e., recorded as NULL, the code of the one available is used instead. 2. If both airports are identified in the flight data 4 dataset but are different, i.e., the arrival and subsequent departing airports differ, and these airports are located at a great circle distance > 80 km, a new flight between those airports is added if: (a) there have been historical fights operating between those two airports, (b) the average flight time between those airports is greater than one hour, and (c) the time between the two flights is greater than the average flight time between the airports plus two minimum turnaround times4(defined as 50 minutes) to ensure enough time for this extra flight to be added. 3. If both airports in a turnaround are identified but different and close by, great circle distance ≤80 km, one is assumed to be mislabelled. The airport with the most operations is considered the most likely, and the other airport is replaced accordingly. A total of 801,020 flights were processed for the week of May 2023, 799,527 of which have a call sign, and 30,114 flights are from one of the airlines of interest (VLG, RYR, ANE, AEA, IBE and IBS) (3.8%). The sourced flight data covers 26 airports within Peninsular Spain with flights (96 routes, i.e., origin-destination pairs). Over the 30,114 flights considered, 0.3% of the departures (80) and 1.1% of all the arrival airports (338) are manually modified; and, as part of fixing the rotations, 4,905 departure and 5,035 arrival airports are further changed, with only three flights added. Finally, OpenSky provides first seen and last seen for each aircraft. These correspond to the start and end times of the ADS-B traces. We need, however, the scheduled times (Scheduled In-Block Time (SIBT) and Scheduled Off-Block Time (SOBT)). These are estimated in the following way: the dataset also contains information on the aircraft’s altitude at the first and last points of the trace; therefore, we estimate the take-off and landing time by assuming a constant climb speed of 2,000 ft/min and a descend vertical speed of 1,500 ft/min, which are nominal performance values. Note that the route time is, therefore, composed of the ADS-B traces and eventual additions for the estimates of the initial climb and final descent segments (if missing). According to EUROCONTROL taxi times reported data for summer 2021 [17], the average taxi-out time for all airports in Spain was 10.6 minutes, with a maximum average value of 15 minutes for Barcelona (LEBL). On average, across all airports, the 90th percentile taxi-out time was 14 minutes, with a maximum of 23 minutes for Madrid (LEMD). The reported average taxi-in time for all Spanish 2This is true at the time of the writing for the data used 3A rotation is a sequence of flights performed by the same aircraft. 4Time between arrival and departure by an aircraft. Journal of Open Aviation Science 5 airports was 4.8 minutes (9.1 minutes for LEMD and 5.5 minutes for LEBL), with an average 90th percentile of 7 minutes. With these considerations, we subtract from the estimated take-off time 20 minutes for taxi-out and add 10 minutes for taxi-in times to estimate the departure and arrival block times. Even if more accurate values could be used, selecting 20 minutes for taxi-out and 10 minutes for taxi-in ensures that the block times are usually within these estimates. The potential overestimation of taxi times could also account for some schedule padding, which is not possible to estimate with the OpenSky data. 2.1.2 Rail network data preparation Renfe (Spanish Public Rail Service Operator) provides an open dataset containing information on long and medium-distance rail services for high and conventional speeds. These data are processed to extract all possible direct rail trips between the airports in Spain. A set of stations within a 25 km radius are identified for each airport. Then, instead of considering only rail services between cities linked by direct flights, the direct rail services for all origindestination combinations are identified (529 pairs) for each day. In total, close to 340k (rail) stationto-rail station pairs are analysed. This allows us to consider direct trains that replace passenger itineraries on connecting flights. The rail services obtained are filtered so that the most suitable origin-destination rail station between each origin and destination airport pair is kept, i.e., the trains which use the main stations from all the ones close to the airports. After this process, a daily average of 1040 train services are obtained, with between one (for most) and three (for Madrid) rail stations per airport. 2.2 Methodology To assess the impact of the substitution, which we will term ban from here on, we apply several steps: flight replacement; fleet usage estimation; airport usage; emissions calculation and potential passenger itineraries computation. Flight replacement. We want to apply an incremental ban on flights. For this, we set a threshold in time (for instance, two hours), and we ban all flights between two cities connected by at least a train, which makes the trip under the time threshold. The fastest train service is filtered for each origin and destination pair to detect such a train. For instance, with a two-hour threshold, all flights between Valencia (LEVC) and Madrid (LEMD) are removed as the fastest train between the cities takes 1h50. Note that to detect the faster train, the day is not considered, i.e., the fastest train on all analysed days is used. Fleet usage estimation. When considering the fleet utilisation of an airline, all the sets of aircraft rotations performed in a day need to be considered. Figure 1shows a basic time-space diagram with an example of the rotation where six flights are assigned to a given aircraft (with five turnarounds). We use the notation 1 - 2 - 3 - 4 - 5 - 6 to represent the flights and rotations in a simple manner. An aircraft rotation for the entire day is a set of flights (j∈A) flown by the same aircraft chronologically in a given period. This accomplishes two conditions, given two consecutive flights j–k: i) the arrival airport of flight jis the departure airport for flight k, ii) the SIBT for the arrival flight jis smaller than the SOBT for the departing flight k. The aircraft rotation problem [18] consists of formulating the tours for the entire airline fleet to cover once and only once each flight, minimising costs and satisfying all the operational requirements, which could include visiting the maintenance base, minimum turn around time, respecting commercial schedules, etc. Considering the tracking of each aircraft rotation when evaluating the impact of removing flights due to a ban is important, as gaps can be generated. In the example of Figure 1, if flights 3 and 4 are removed, the resulting rotation pattern becomes 1 - 2 - 5 - 6. Therefore, if nothing is done, 6Luis Delgado et al. Figure 1. A simple aircraft rotation problem. the corresponding block times (bt3and bt4) are transferred to a new ground time (gt25), which is calculated as the difference between the SOBT of flight 5 and the SIBT of flight 2. The ground time is, therefore, the time that the aircraft spends at the airport, which can be larger than the strict minimum turnaround time required. Based on these concepts, three metrics are used in this paper: •Fleet utilisation: the ratio between the sum of the total block times for all the rotations and the total available fleet time (we consider 24 hours per day and aircraft used in this study). •Variation of ground time: the ratio between the increment of the total ground time divided by the total ground time used as a reference value (this is the total ground time for the original scenario, corresponding to the ban time equal to zero). Ground time does not consider the sleeping time of the aircraft – from the end of the last and start of the first flight in consecutive turnarounds. •Fleet size: we estimate the new fleet size requirements considering the origin-destination pairs removed by the ban. If airlines can re-assign the fleet, considering also re-scheduling and recomposition of their fleets, assuming as a target to maintain the original utilisation factors, a strategic re-sizing of fleets can be done. A continuous approximation works for strategic purposes, providing a lower bound. The result is obtained as the upper integer of the aggregated block time divided by the utilisation factor and the daily work time window. The resulting number is the best level that can be achieved. In reality, some airlines require different sizes because they serve markets with heterogeneous demand, and this diversification does not let them reduce their fleet sizes more. Even if not all the flights are impacted by possible bans or operated in Spain, all the flights (30,114) operated by the six airlines of interest are considered for the fleet utilisation analysis, as they are needed to reconstruct the rotations properly. Infrastructure usage. The flight ban and movement of passengers by rail have two impacts on the transport infrastructure: first, the demand at airports (and airspace) will be reduced. We consider the number of departures and arrivals at Madrid Barajas (LEMD) to indicate this aspect. Second, the seats available on banned flights must be transferred to the rail network. We estimate this additional rail demand with respect to the current supply of seats computed from information on the rail services and fleet composition [9,14]. Emissions calculation. Air and rail emissions are calculated slightly differently due to the transport mode characteristics and data availability. For air, the analytical model developed by [1] is used to compute CO2emissions based on the route’s great circle distance and the available seats. This model also accounts for taxi fuel consumption (based on statistical European data, as detailed in [1]). A distance correction is applied to consider that actual routes do not exactly follow the great circle distance. Some aircraft models overpass the limit of the maximum seats considered in the analytical Journal of Open Aviation Science 7 model; in these few cases, emissions were directly calculated using EUROCONTROL’s IMPACT tool [19]. The rail CO2emissions for seats transferred to rail were obtained from the EcoPassenger calculator [15], which calculates the specific train energy consumption, then considers the energy chain and converts the required energy into CO2emissions per passenger. As the emissions are estimated per passenger, only the seats transferred to rail are considered when estimating the additional emissions generated by the air passengers in the rail network. The model feature maximum load factor and the option of national mix of electricity production were selected. Possible passengers itineraries. A passenger can use a flight to travel directly between origin and destination but can also use short-haul connecting flights that enable the connectivity of passengers to more destinations. The introduction of air bans might impact the network’s potential connectivity from this passenger’s perspective. As demand data has not been used, all potential connectivity is computed. This requires, however, some assumptions on which connecting itineraries are possible to avoid generating potential itineraries that would not be suitable, e.g., a connecting flight itinerary with a final destination too close to the origin or for which a suitable direct flight (or train) is available. Some parameters used in the criteria below have been adjusted to ensure that the results obtained as connecting flight itineraries are reasonable. Connecting flights are therefore considered as long as: •The connection is between flights from the same airline or alliance (IBE, IBS and ANE). •A minimum connecting time between flights of 45 minutes is used, i.e., the SOBT of the connecting flight must be at least 45 minutes after the SIBT of the inbound flight. •The origin and final destination of the connecting itinerary are located at least 250 km apart. This is to avoid connecting itineraries where the destination is too close to the origin airport. •There is no direct train between the origin and final destination, or any direct train is longer than 4h30. •If a direct flight exists between the origin and final destination, the connecting itinerary should not exceed 1.5 times the direct flight alternative. •If other alternatives (via another connecting airport) are available between the origin and destination, the itinerary is no longer than 1.5 times the median of all other alternatives. •If the origin and destination airports are in Spain, the connecting airport is not outside Spain. Finally, suppose the same itinerary with the same airline(s) is available. In that case, the options which minimise the time at the connecting airport are kept, i.e., avoiding long connecting times if an earlier alternative with a lower connection is possible. Possible rail and multimodal alternatives are computed as flights are removed due to applied bans. This is done by removing the flight (and flight-flight) itineraries impacted by the ban. Then, if direct rail services are available to substitute origin-destinations served by flights (direct flights or flightflight connections), these rail services are added to the pool of possible passenger itineraries. Then, with those rail services and remaining flights, multimodal (air-rail and rail-air) possible itineraries are computed considering: •A minimum connecting time of 100 minutes for rail-air connections and 60 minutes for air-rail connections except for Madrid and Barcelona airports, for which more specific values are used. These have been estimated using Google Maps considering public transport transfer times and, among others, average time between service (15 minutes) and kerb-to-gate times (45 minutes), additional required walking time (10 minutes): Madrid-Chamartin – LEMD (22 minutes by train 8Luis Delgado et al. transfer time) →85 minutes, Madrid-Puerta de Atocha – LEMD (45 minutes by train or metro transfer time)→108 minutes, Madrid-Principe Pio – LEMD (50 minutes by train or metro transfer time) →113 minutes, and Barcelona-Sants – LEBL (30 minutes by train transfer time) →93 minutes. •There is no direct flight between the origin and final destination. 3. Results The results are structured as follows: first, an analysis of the air routes (and flights) impacted by the different bans is presented in Section 3.2. As explained previously, eliminating flights will impact the airlines’ fleet usage; the analysis of these aspects is detailed in Section 3.3. Section 3.4 shows how flight bans translate into air and rail infrastructure demand changes. The environmental impact of these measures and the changes in potential passengers’ itineraries are presented in Section 3.5 and Section 3.6, respectively. The number of flights and rail services within Peninsular Spain varies as a function of the day of the week5. Therefore, average values across the seven days will generally be reported, even though connectivity and flight/rail usage differences might depend on the day. 3.1 Spanish air and rail network As shown in Figure 2a and Figure 2c, the Spanish Peninsular air network pivots around the airports of Madrid (LEMD) in the centre of the country with the hub of Iberia, Barcelona (LEBL) dominated by the operation of Vueling, followed by Malaga (LEMG) and Seville (LEZL). Most of the operations feed Iberia operations; therefore, short-haul flights within Spain mostly follow a hub and spoke diagram with Madrid in the centre. Besides these operations, the distances prevent most of the short-hauls in the same region (e.g., between cities in the south of Spain), and routes operated by low-cost carriers dominate the connectivity of the periphery (e.g., Vueling and Ryanair flights linking the north-west with the east and the north with the south). From a rail perspective, Spain has developed a radial high-speed rail infrastructure (see Figure 2b) with slower conventional lines linking regions not via Madrid. Therefore, as shown in Figure 2d, it is expected that when rail is used as a substitute for flights, most rail links will be to-from Madrid and only direct rail connections between cities in the periphery are used when the flight ban is significantly large (as presented in Figure 3). As shown, even considering the maximum ban time, not all cities (and air links) can replace any of their flight with direct trains (see, for instance, LEXJ in the north of Spain, which is connected by flight with the south region, i.e., LEZL, LEMG, LEAM, for which direct rail alternatives are not possible). 3.2 Routes replacement Figure 3shows an example of four ban thresholds (0h, 3h, 5h and 9h) used to replace flights within Peninsular Spain for a given day (3rd of May 2023). As observed, as the ban increases, the number of origin-destination pairs served by flights decreases while the rail network gains importance. Note that only rail services that could replace routes impacted by the air ban are considered here, as the work focuses on analysing the displacement of passengers from air to rail. Figure 4shows the average number of routes (origin-destination pairs) operated per airline and rail services as a function of the ban threshold. Without a ban, on average, 91 daily routes are operated by all the airlines considered6. As the ban increases past the 2-hour threshold, the number of routes 5With a mean value of 219 flights and 221 rail services per day. 6Considering the same origin-destination by different airlines as different routes. There are 89 unique origin-destinations. Journal of Open Aviation Science 9 (a) Commercial airports 2022 (green state ownership, orange regional ownership) [20] (b) Rail network 2021 (Red: Conventional Iberian gauge, Blue: High-speed lines, Green: Narrow gauge)[21] (c) Connectivity (number flights 01MAY2023-07MAY2023) between airports in Peninsular Spain (d) Connectivity (number rail services 01MAY23-07MAY2023) used to replace flights. Figure 2. Spain air and rail infrastructure and network. 16 Luis Delgado et al. characteristics. This should be the subject of future research. Merging OpenSky’s data with the data of railway operators allowed us to develop a complete bimodal network model, to understand the principles of airline network design and to analyse the potential contributions of railways in the middle distance market, considering a new scenario of multimodal transport and zero-emissions commitment. OpenSky is the first open database that provides enough flight and aircraft information to perform network, schedule and resource analysis, which is of great value to science and academia. This work has some areas for future development related to the airline network design and resource allocation problems. For those, it is necessary to have accurate information related to the airports where the airline operates (algorithms to deduce missing airports), to know the airline’s overnight and maintenance bases, to reconcile icao24’s transponder ids with aircraft type, and to add information related to actual and scheduled flight times. Eliminating sources of error in this information is vital to accurately determining rotations and adjusting network and fleet assignment models. Funding statement This work is part of the MultiModX project which is funded by the European Union’s Horizon Europe – SESAR 3 programme (Grant Agreement 101114815). Open data statement Section 2and Table 1describe the data used for this article. All datasets are open and available. The datasets used to produce the results of this article can be accessed from [12]inhttps://doi.org/10. 5281/zenodo.10642324. Reproducibility statement Section 2describes in detail the methodology used to compute the results presented in this article. The code has been developed in Python and R, and it is accessible in https://github.com/UoW-ATM/ joas_air_rail_network_analysis. References [1] Adeline Montlaur, Luis Delgado, and César Trapote-Barreira. “Analytical Models for CO2 Emissions and Travel Time for Short-to-Medium-Haul Flights Considering Available Seats”. In: Sustainability 13.18 (2021). issn: 2071-1050. doi:10.3390/su131810401. [2] European Commission. Sustainable & smart mobility strategy, putting European transporton track for the future. July 20, 2021. url:https://transport.ec.europa.eu/document/download/ be22d311-4a07-4c29-8b72-d6d255846069_en?filename=2021-mobility-strategy-and-actionplan.pdf. [3] Journal officiel Lois et Décrets France. LOI n°2021-1104 du 22 août 2021 portant lutte contre le dérèglement climatique et renforcement de la résilience face à ses effets.https://www.legifrance. gouv.fr/jorf/id/JORFTEXT000043956924. Accessed on 25/10/2023. 2023. [4] PSOE and Sumar. España avanza – Una nueva colacición de gobierno progresista – Agreement of investiture and governance arrangements between Spanish Socialist Party and Sumar. 2023. [5] Luis Delgado, Tatjana Bolić, Andrew Cook, Elham Zareian, Ernesto Gregori, and Annika Paul. “Modelling passengers in air-rail multimodality”. In: Proceedings of the 11th EUROSIM Congress. EUROSIM. Amsterdam, The Netherlands, 2023. Journal of Open Aviation Science 17 [6] Christiaan Behrens and Eric Pels. “Intermodal competition in the London–Paris passenger market: High-Speed Rail and air transport”. In: Journal of Urban Economics 71 (2012), pp. 278– 288. [7] P. Arich, T. Bolic, I. Laplace, N. Lenoir, S. Parenty, A. Paul, and C. Roucolle. “Substitution path between air and rail in Europe: a measure of demand drivers”. In: Air Transport Research Society World Conference (2022). [8] Nicolò Avogadro, Mattia Cattaneo, Stefano Paleari, and Renato Redondi. “Replacing shortmedium haul intra-European flights with high-speed rail: Impact on CO2 emissions and regional accessibility”. In: Transport Policy 114 (2021), pp. 25–39. issn: 0967-070X. doi:https: //doi.org/10.1016/j.tranpol.2021.08.014. [9] Renfe. Horarios de alta velocidad, larga distancia y media distancia.https://data.renfe.com/ es/dataset/horariosdealtavelocidadlargadistanciaymediadistancia. Accessed on 25/10/2023. 2023. [10] Jan K. Brueckner. “Network Structure and Airline Scheduling”. In: The Journal of Industrial Economics 52.2 (2004), pp. 291–312. doi:https://doi.org/10.1111/j.0022-1821.2004.00227.x. [11] OpenSky. A Quick Guide To OpenSky’s Impala Shell.https://openskynetwork.org/data/ impala. Accessed on 25/10/2023. 2023. [12] Luis Delgado, Gerald Gurtner, Tatjana Bolic, Cesar Trapote-Barreira, and Adeline Montlaur. Data for Airlines network analysis on an air-rail multimodal system. Journal of Open Aviation Science, 1(2). Zenodo, Feb. 2024. doi:10.5281/zenodo.10642324.url:https://doi.org/10.5281/ zenodo.10642324. [13] Matthias Schäfer, Martin Strohmeier, Vincent Lenders, Ivan Martinovic, and Matthias Wilhelm. “Bringing up OpenSky: A Large-Scale ADS-B Sensor Network for Research”. In: Proceedings of the 13th International Symposium on Information Processing in Sensor Networks. 2014, pp. 83–94. [14] Renfe. Flota de trenes.https://www.renfe.com/es/va/grup-renfe/grup-renfe/flota-de-trens. Accessed on 29/02/2024. 2024. [15] EcoPassenger. Compare the energy consumption, the CO2 emissions and other environmental impacts for planes, cars and trains in passenger transport.http://ecopassenger.hafas.de. Accessed on 29/02/2024. 2024. [16] Luis Delgado, Gerald Gurtner, Tatjana Bolic, Cesar Trapote-Barreira, and Adeline Montlaur. Additional data (airport static and airport modification) needed for Airlines network analysis on an air-rail multimodal system paper from the 11th OpenSky Symposium. Zenodo, Oct. 2023. doi:10.5281/zenodo.10038841. [17] EUROCONTROL. Taxi times - Summer 2021. Tech. rep. https://www.eurocontrol.int/publication/ taxi-times-summer-2021. Accessed on 27/02/2024. EUROCONTROL, Mar. 2022. [18] Lloyd W. Clarke, Ellis L. Johnson, George L. Nemhauser, and Zhu Zhongxi. “The aircraft rotation problem”. In: Annals of Operations Research 69 (1997), pp. 33–46. issn: 1572-9338. doi: 10.1023/A:1018945415148. [19] EUROCONTROL. Integrated aircraft noise and emissions modelling platform.www.eurocontrol. int/platform/integratedaircraftnoiseandemissionsmodellingplatform. Accessed on 29/02/2024. 2024. [20] Wikipedia. Commercial airports in Spain – Andreuvv.https://commons.wikimedia.org/wiki/ File:Aeropuertos_comerciales_en_Espa%C3%B1a.svg. Accessed on 26/02/2024. 2022. [21] Wikipedia. Railways in Spain – Alejandro Navarro López.https://commons.wikimedia.org/ wiki/File:Red_actual_de_ferrocarriles_de_Espa%C3%B1a.svg. Accessed on 26/02/2024. 2021. [22] Phillip J. Lederer and Ramakrishnan S. Nambimadom. “Airline Network Design”. In: Operations Research 46.6 (1998), pp. 785–804. doi:https://doi.org/10.1287/opre.46.6.785.