ISSN 2529-3303 PASSENGER RAIL PERFORMANCE IN EUROPE: REGIONAL AND TERRITORIAL ACCESSIBILITY INDICATORS FOR PASSENGER RAIL Working Papers A series of short papers on regional research and indicators produced by the Directorate-General for Regional and Urban Policy WP 01/2022 Martijn Brons, Hugo Poelman, Linde Ackermans, Juan Nicolás Ibáñez and Lewis Dijkstra Regional and Urban Policy This document should not be considered as representative of the European Commission’s official position. Luxembourg: Publications Office of the European Union, 2021 © European Union, 2021 The reuse policy of European Commission documents is implemented by Commission Decision 2011/833/EU of 12 December 2011 on the reuse of Commission documents (OJ L 330, 14.12.2011, p. 39). Unless otherwise noted, the reuse of this document is authorised under a Creative Commons Attribution 4.0 International (CC-BY 4.0) licence (https://creativecommons.org/licenses/by/4.0/). This means that reuse is allowed provided appropriate credit is given and any changes are indicated. For any use or reproduction of elements that are not owned by the European Union, permission may need to be sought directly from the respective rightholders. The European Union does not own the copyright in relation to the following elements: Cover image: copyright iStock/GettyImagesPlus PDF ISBN 978-92-76-46288-0 doi:10.2776/576280 ISSN 2529-3303 KN-AK-21-005-EN-N PASSENGER RAIL PERFORMANCE IN EUROPE: REGIONAL AND TERRITORIAL ACCESSIBILITY INDICATORS FOR PASSENGER RAIL Authors: Martijn Brons, Hugo Poelman, Linde Ackermans, Juan Nicolás Ibáñez and Lewis Dijkstra 2 CONTENTS Abstract....................................................................................................................................................................................................................4 Country codes.......................................................................................................................................................................................................5 Introduction............................................................................................................................................................................................................6 An accessibility framework...........................................................................................................................................................................7 A brief description of the methodology......................................................................................................................................7 Analysing rail performance in Europe..................................................................................................................................................11 Grid-level results...................................................................................................................................................................................11 National results......................................................................................................................................................................................16 Regional results.....................................................................................................................................................................................19 Degree of urbanisation results.....................................................................................................................................................22 City-level analysis.................................................................................................................................................................................25 Conclusions.........................................................................................................................................................................................................30 Annex – Methodological description.....................................................................................................................................................31 Input data..................................................................................................................................................................................................31 Determining pairs of rail stations for origin/destination computations................................................................33 Determining the spatial relationship between stations and grid cells...................................................................35 Calculating accessible population by grid cell......................................................................................................................35 From accessibility to transport performance: taking proximity into account ...................................................36 Aggregating by region or territory...............................................................................................................................................36 References...........................................................................................................................................................................................................37 Acknowledgments...........................................................................................................................................................................................38 Data.........................................................................................................................................................................................................................38 PASSENGER RAIL PERFORMANCE IN EUROPE: REGIONAL AND TERRITORIAL ACCESSIBILITY INDICATORS FOR PASSENGER RAIL LIST OF FIGURES Figure 1: Scenarios of rail travel time calculations combined with active mobility modes....................................8 Figure 2: Accessibility, proximity and transport performance by rail (optimal travel time) plus a short walk, 2019...........................................................................................................................................................................................................10 Figure 3: Rail transport performance per country for different walk and bike combinations, 2019...............17 Figure 4: Population by level of transport performance by rail (average travel time) combined with short walks, 2019...........................................................................................................................................................................................18 Figure 5: Population by level of transport performance by rail (optimal travel time) combined with short bike rides.................................................................................................................................................................................................18 Figure 6: Relationship between transport performance by rail plus a short walk (optimal travel time) and population concentration around the place of departure, at NUTS 3 level..........................................................22 Figure 7: Transport performance by rail plus a short walk (optimal travel time), by country and refined degree of urbanisation, 2019.................................................................................................................................................23 Figure 8: Transport performance by rail plus a short walk (average travel time), by country and refined degree of urbanisation, 2019.................................................................................................................................................24 Figure 9: Transport performance by rail plus short bike rides (optimal travel time), by country and refined degree of urbanisation, 2019.................................................................................................................................................24 Figure 10: Transport performance by rail plus short bike rides (average travel time), by country and refined degree of urbanisation, 2019.................................................................................................................................................25 Figure 11: Transport performance by rail plus short bike ride (average travel time) and network density around cities, 2019.......................................................................................................................................................................28 Figure 12: Transport performance by rail plus short bike rides (average travel time) and average distance to the nearby population (within 120 km) per city...................................................................................................29 Figure 13: Example of origin/destination calculation requests during morning peak hours...............................34 Figure 14: Grid cells within walking and/or cycling distance from departure and arrival station.....................35 LIST OF MAPS Map 1: Stations accessible within 1.5 hours and an area of 120-km radius around the city of Luxembourg...........................................................................................................................................................................................................9 Map 2: Proximity: population within a 120-km radius, 2018................................................................................................12 Map 3: Transport performance by rail plus a short walk (optimal travel time), 2019............................................13 Map 4: Transport performance by rail plus a short walk (average travel time), 2019...........................................14 Map 5: Transport performance by rail plus short bike rides (optimal travel time), 2019......................................15 Map 6: Transport performance by rail plus short bike rides (average travel time), 2019.....................................16 Map 7: Transport performance by rail (average travel time) per NUTS 3 region, 2019.........................................20 Map 8: Transport performance by rail (optimal travel time) per NUTS 3 region, 2019..........................................21 Map 9: Transport performance by rail plus a short walk (optimal travel time) per urban centre, 2019......26 LIST OF TABLES Table 1: City pairs with a rail connection speed of at least 150 km/h and an optimal travel time of a maximum of 90 minutes........................................................................................................................................................................27 Table 2: City pairs with a rail connection speed of less than 60 km/h and an optimal travel time of a maximum of 90 minutes........................................................................................................................................................................27 Table 3: City pairs with no rail connection within 90 minutes...............................................................................................27 Table 4: Timetable datasets used to construct an integrated European dataset......................................................32 3 ABSTRACT This paper presents a detailed analysis of the performance of rail passenger services using an accessibility framework developed with the International Transport Forum and the Organisation for Economic Co-operation and Development, along with a near-complete collection of Europe-wide rail timetables. The framework compares the population that can be easily reached by train to the total population living nearby, in order to measure how well the rail system performs. This analysis starts from the more than 2 million inhabited grid cells of 1 km2 in the EU, the European Free Trade Association (EFTA) countries and the United Kingdom. The detailed information allows us to assess rail services at the national, regional and local levels. At the national level, Spain, Denmark, Austria and Switzerland have the highest rail transport performance, while Lithuania and Romania score lowest. Cities consistently perform better than towns, suburbs and rural areas in all countries. However, some cities score better than others: large cities with frequent service do well, as do smaller cities with a fast connection to a nearby large city. There are 72 cities with no rail services during peak hours. Comparing rail trips combined with either a short walk or a bike ride to and from the train station shows that the combination with a bike ride more than doubles rail performance. Given the relatively low cost of providing bike infrastructure and promoting active mobility and micro-mobility, including e-bikes and e-scooters, this is likely to be a highly cost-effective way of promoting more rail travel. PASSENGER RAIL PERFORMANCE IN EUROPE: REGIONAL AND TERRITORIAL ACCESSIBILITY INDICATORS FOR PASSENGER RAIL COUNTRY CODES AL AT BE BG CH CY CZ DE DK EE EL ES FI FR HR HU IE IT LT LU LV ME MK MT NL NO PL PT RO RS SE SI SK UK XK (*) (*) Albania Austria Belgium Bulgaria Switzerland Cyprus Czechia Germany Denmark Estonia Greece Spain Finland France Croatia Hungary Ireland Italy Lithuania Luxembourg Latvia Montenegro North Macedonia Malta Netherlands Norway Poland Portugal Romania Serbia Sweden Slovenia Slovakia United Kingdom Kosovo This designation is without prejudice to positions on status, and is in line with UNSCR 1244/1999 and the ICJ Opinion on the Kosovo declaration of independence. 5 6 INTRODUCTION 2021 is the European Year of Rail, forming part of the EU’s efforts to achieve climate neutrality by 2050 under the European Green Deal. This initiative is an opportunity to highlight the benefits of rail as a sustainable, smart and safe means of transport. Travel by rail has been growing in the EU. It grew from 358 billion passenger km in 2010 to 421 billion passenger km in 2019, an increase of 18 % (1). This growth also translated into an increase in the modal share of passenger km from 6.6 % to 7.0 %. Although this share may seem small, many people in the EU use rail to commute to work, especially in and around larger cities. In these cities, rail travel helps to reduce congestion, air pollution and greenhouse gas emissions. In addition, rail can provide fast connections between many city centres and thus provides a faster connection than air travel. The growth of high-speed rail has helped to make these connections even more attractive. Between 2010 and 2019, high-speed rail in the EU grew from 105 to 132 billion passenger km, an increase of 26 %. As a result, the share of high-speed rail passenger km within total rail passenger km grew from 29 % to 31 %. Despite the growing importance of rail travel, it has been difficult to analyse this mode of transport on a pan-European scale, because the data on rail services are highly fragmented and are not harmonised. A virtually complete database containing the 2014 timetables of all passenger trains, constructed by DG Regional and Urban Policy, was used in several papers. This includes a paper on the speed and frequency of rail services (Poelman and Ackermans, 2016) and another on cross-border services (Poelman and Ackermans, 2017). Poelman et al. (2020) relied on the same data but took the analysis several steps further. First, it used a new (1) (2) (3) (4) accessibility framework, which shows how well a particular mode allows people to reach nearby destinations. Second, it took into account trips that require people to switch trains. Third, it required a far more detailed and comprehensive analysis, covering each of the 2 million inhabited square grid cells of 1 km2 in the EU, European Free Trade Area (EFTA) and United Kingdom and assessing accessibility for every 15-minute period during the morning and evening peak times (2). This paper is an update, extension and methodological refinement of Poelman et al. (2020) (3). First, the reference year of the population data is updated to 2018. Second, the timetable data are updated to reference year 2019. Third, the updated timetable data are based on timetable information collected from a wide range of data sources and integrated into a single railway dataset, resulting in an increase of over 20 % in the total number of stops, connections and rail services covered by the analysis. Finally, the integrated dataset also includes metro networks for those cities where such links are an essential component in ensuring efficient links between rail stations. Creating a database with timetables for all European passenger trains is complex and challenging. This paper has managed to improve the coverage of European passenger trains in 2019 compared to 2014 (Poelman et al., 2020). However, these two papers should not be compared, as the changes between them are the result of changes in the services offered and the addition of missing services in 2019. The annual publication of comprehensive passenger rail data in a standardised, machinereadable format would allow a much faster and more reliable analysis of the rail system. A new legal framework (4) requires each Member State to collect these data in a harmonised format, but it does not specify who is allowed to access or use the data. EU Transport in Figures: Statistical pocketbook 2021 (https://transport.ec.europa.eu/media-corner/publications_en). These correspond to the 2-hour periods in the morning and the evening, respectively, during which the maximum number of departures are observed at the level of the Member State in which the departure station is located. The data used for the analysis cover the EU + EFTA + UK and the western Balkans. While the results for the western Balkans are shown in the maps, the discussion and figures focus on the EU + EFTA + UK. Commission Delegated Regulation (EU) 2017/1926 of 31 May 2017 supplementing Directive 2010/40/EU of the European Parliament and of the Council with regard to the provision of EU-wide multimodal travel information services (http://data.europa.eu/eli/reg_del/2017/1926/oj). PASSENGER RAIL PERFORMANCE IN EUROPE: REGIONAL AND TERRITORIAL ACCESSIBILITY INDICATORS FOR PASSENGER RAIL AN ACCESSIBILITY FRAMEWORK This paper uses an accessibility framework that was developed by the European Commission, the International Transport Forum and the Organisation for Economic Co-operation and Development (ITF, 2019). It relies on three simple metrics that are easy to interpret. 1. Accessibility is the total number of destinations that can be reached within a fixed amount of time. For rail, accessibility depends on the proximity of a rail station, the speed and frequency of rail services and the spatial distribution of the destinations. In this paper, we use a 90-minute threshold with the population as the destination, i.e. the total population that can be reached within 90 minutes by rail. This threshold was chosen to measure regional accessibility within a time frame that allows for a return trip, plus time for a half-day meeting. 2. Proximity is the total number of destinations located within a fixed distance. It captures the spatial distribution of destinations and depends on planning, policy and investment decisions. In this paper, we define proximity as the population within a radius of 120 km, i.e. the nearby population. 3. Transport performance is the ratio between accessibility and proximity. It compares the accessible population to the nearby population. In other words, it shows the performance of a transport mode while accounting for the spatial distribution of destinations. In this paper, the indicator is the ratio of the population that can be reached within 90 minutes to the population within a radius of 120 km. This ratio is multiplied by 100. For rail, a ratio of 30 or more means that the mode performs well, while a ratio close to zero means that the mode performs poorly. This analysis was done using grid cells of 1 km², so that the results could be easily aggregated by local administrative units, regions, countries, cities or degree of urbanisation without having to change the methodology or the data. It also means that the values for cities, regions and countries are fully comparable. To capture the experience of an average resident, these three indicators are aggregated using a populationweighted average. A BRIEF DESCRIPTION OF THE METHODOLOGY This section provides a description of the main elements of the methodology. A more in-depth description can be found in the annex. (5) (6) (7) To calculate the accessible population, we needed to identify which stations were connected by a trip duration of a maximum of 90 minutes and which cells could be reached from each train station. The train timetables allowed us to identify the stations that were less than 90 minutes’ travel apart. We identified the stations that could be reached within 90 minutes using the fastest connection during peak hours, while taking into account the average travel time during peak hours. For each station, we identified which cells could be easily reached on foot, i.e. within a 15-minute walk (5). We only had to calculate the accessible population for cells within a short walk of a station, because this is by definition zero for all of the other cells. All cells within walking distance of a particular departure station have the same accessible population (6): the sum of the population within a short walk of all of the stations that can be reached within 90 minutes from that departure station. We did the same for a short bike ride (7). This methodology allows the calculation of different scenarios, each presenting a different combination of active mobility mode and rail travel. We calculated the accessible population by assuming that rail travel is (a) preceded and followed by a short walk, (b) preceded by a short bike ride and followed by a short walk, and (c) preceded and followed by a short bike ride. We used two types of travel time: the optimal travel time and the average travel time. 1. Optimal travel time means that the fastest connection available during peak hours is used, without any waiting time before first boarding the train. In other words, the traveller can adapt their behaviour, in particular the timing of their trip, to the available connections. This assumes that the traveller reaches the departure station just in time to board the fastest train available during peak hours. Hence, this metric indicates the performance of the best available service on a particular connection, yet without giving information about the frequency of such services. This level of accessibility only refers to travel by rail, without the use of any feeder mode such as public transport or car. The travel times observed between stations are only allocated to the populated grid cells in the immediate neighbourhood of the stations, assuming that only a short walk of less than 15 minutes is needed to reach the station. 2. The average travel time reflects the experience of people who cannot choose when they leave, for example because they have fixed working hours or must attend school. It takes into account the average waiting time needed before boarding the (first) train and the speed of different train services. This is a more usercentric approach: it shows the situation where a traveller is constrained in terms of departure or arrival time. Consequently, depending on the availability of rail For walking, we selected the eight grid cells surrounding the cell with the train station. This corresponds to a 3 × 3 km square, or roughly 1.5 km in a straight line from the train station. If a cell is within a short walk from multiple train stations, its accessible population will be higher than that of the cells within a short walk of only one of those stations. For cycling, we selected all grid cells that have their centroid at a maximum of 3.2 km from the station. This approximates an area that should be easily accessible within 15 minutes by bike. 7 8 services at the chosen time of departure, the traveller will face less or more waiting time before being able to board the train. This indicator provides a more realistic picture of the level of service that citizens can expect for day-to-day travel. The combination of three types of trips with two types of travel times leads to a total of six variants, illustrated by Figure 1. Figure 1: Scenarios of rail travel time calculations combined with active mobility modes To express the performance of rail services, we also needed a neutral benchmark against which to measure the accessible population. For this purpose, we computed the population living in a circular area with a 120-km radius around the point of departure. This is the population that would be reachable if one could travel in a straight line and without any obstacles at a speed of 80 km/h. Finally, by dividing the population accessible within 1.5 hours by the population living in an area of 120-km radius around the place of departure (and multiplying the result by 100), we obtained the transport performance. Map 1 illustrates the different concepts in the case of Luxembourg. Starting from the city of Luxembourg, the area of 120-km radius includes cities like Liège, Metz, Nancy and Saarbrücken. By using the fastest rail services available, Trier, Metz and Nancy can be reached within 1.5 hours, as well as a series of smaller cities in Belgium, France and Germany. By taking into account the waiting times between scheduled train services, fewer destinations (mostly located inside the country), can be reached within 1.5 hours. 9 PASSENGER RAIL PERFORMANCE IN EUROPE: REGIONAL AND TERRITORIAL ACCESSIBILITY INDICATORS FOR PASSENGER RAIL Map 1: Stations accessible within 1.5 hours and an area of 120-km radius around the city of Luxembourg Bruxelles / Brussel Leuven Maastricht Lille Douai Düren Aachen Liège Quaregnon / La Louvière Frameries Charleroi Valenciennes Köln Porz Heerlen Herzogenrath Bonn Verviers / Dison Gießen Namur Koblenz " " Bad Soden am Taunus / Kelkheim Frankfurt Wiesbaden am Main Rüsselsheim Mainz am Main " Saint-Quentin " " " """ " " " " " " " " " " " " " " " " " " "" " "" " " " " " " Trier "" " """ " "" " " " " " """ " " " "" " " " " " " " Luxembourg " "" " " " "" " "" " "" " """ "" " " " " " " "" "" "" """ "" " " " "" Darmstadt Kaiserslautern " " Reims " " Mannheim/Ludwigshafen Heidelberg " " " " " """ " "" Hanau Saarbrücken "" " " " " " " Metz " " " " " " " Karlsruhe " " Pforzheim Stuttgart Accessible rail stations " Optimal travel time " Average travel time Area withinTroyes 120 km radius Nancy " Sindelfingen Strasbourg Tübingen 0 50 100 km Source: REGIO-GIS. Figure 2 shows accessibility, proximity and transport performance at the national level. It reveals large differences in all indicators. Accessibility by rail is highest in the United Kingdom and almost zero in Lithuania, which has only a few rail lines. Proximity is highest in the Netherlands and Belgium – both small, densely populated and highly urbanised countries – while it is lowest in Estonia, Finland, Latvia and Norway. The highest rail performance is in Spain, Denmark and Switzerland. Average rail performance at the country level tends to be rather low, although it masks large regional and territorial differences, as discussed below. 10 18 18 16 16 14 14 12 12 10 10 8 8 6 6 4 4 2 2 0 Transport performance by rail Populations in millions Figure 2: Accessibility, proximity and transport performance by rail (optimal travel time) plus a short walk, 2019 0 LT RO SK SI BG HR EL PL IE CZ HU FI IT EE PT NL LU BE NO SE LV DE FR UK AT DK ES CH EU+EFTA+UK Performance (right-hand axis) Source: REGIO-GIS. NB: The reference years used for this analysis depend on the availability of the required datasets. The population grid refers to 2018 (8), while the rail timetables are generally from 2019 (9). Cyprus, Iceland and Malta are not shown, as they do not have any rail service. (8) (9) Currently this is the latest reference year for which Europe-wide grid data were available. The next edition of the 1 km² population grid will refer to census year 2021. Most of the timetables refer to 12.9.2019, complemented with data referring to other weekdays in 2019–2020. PASSENGER RAIL PERFORMANCE IN EUROPE: REGIONAL AND TERRITORIAL ACCESSIBILITY INDICATORS FOR PASSENGER RAIL ANALYSING RAIL PERFORMANCE IN EUROPE GRID-LEVEL RESULTS Accessibility and transport performance patterns measured at grid-cell level provide a subtle and varied pattern (10). This is not surprising, as we are dealing with more than 2 million observations, i.e. the number of populated grid cells in the EU, EFTA countries and the western Balkans and the United Kingdom. Although the scope of the analysis covers all populated places, and thus the entire population of Europe, in many places daily rail accessibility is zero because these places are located beyond walking or cycling distance from any station or stop where rail services are available. Even in areas close to a station, daily accessibility can be very low. This can be due to the absence of any large destinations within a reasonable distance, to the layout and the characteristics of the rail network or to the poor availability or performance of the rail services provided on that network. At the other end of the spectrum, we find grid cells from which up to 10 million people can be reached within 1.5 hours, provided the optimal rail schedules are used, combined with a short walk to/from the stations. However, these extremely high values occur only in London and similar centres, where there is a high concentration 11 of residential population anyway, making it easier to reach high values of accessibility. Consequently, we needed a benchmark against which we can interpret the accessibility values. This benchmark is the proximity of population, shown on Map 2. It is the population living in a circular neighbourhood of 120-km radius around each of the grid cells. This map shows a smoothed pattern of the large population concentrations in Europe. The highest values of population proximity are found in places surrounded by major agglomerations: in the south-eastern Netherlands and the adjacent German areas – all located between the Ruhr area, the Dutch Randstad, Antwerp and Brussels – or between London and the Midlands in the United Kingdom. Map 3 shows the transport performance of rail trips, combined with a short walk before and/or after the trip, in optimal circumstances, which means that the traveller is assumed to be able to adjust their departure time to make use of the fastest connection. Under these assumptions, transport performance can exceed 25 % in or near cities. Decent levels of rail performance can be found in many places in Austria, Belgium, Czechia, Germany, the Netherlands, Spain, Switzerland and the United Kingdom, and in and around major cities in some other countries. More than 5 million inhabitants can be reached within 1.5 hours from areas in and around Paris, the Ruhr area and London. (10) These patterns may be quite hard to capture on small Europe-wide maps. For an easier visualisation on small maps, the 1 km² grid cells have been aggregated to 5 × 5 km cells by computing population-weighted averages of the 1 km² metrics. A complete and more in-depth exploration of the original results by grid cell is possible using the interactive map viewer that accompanies this paper (https://ec.europa.eu/regional_policy/mapapps/transport/rail_accessibility_2019.html). 12 Map 2: Proximity: population within a 120-km radius, 2018 Canarias Guadeloupe Martinique Guyane Mayotte Réunion Açores Madeira REGIOgis Millions of inhabitants 0-2 10 - 14 Uninhabited 2-4 14 - 18 No data 4-6 18 - 22 6-8 > 22 The map shows the population-weighted average for cells of 5 × 5 km for better visualisation. The analysis was done for 1 × 1 km cells. Sources: REGIO-GIS; JRC-GEOSTAT 2018 grid. 8 - 10 0 500 km © EuroGeographics Association for the administrative boundaries The impact of service frequency and speed of different services is reflected in Map 4. Here we only take into account the population that can be reached within 1.5 hours of an average total travel time, including the average waiting time needed before boarding the (first) train. In comparison to the previous map, we see that the areas with good accessibility have shrunk to a smaller set of major agglomerations. High-frequency services offering good relative accessibility are mostly limited to Paris, London and the largest cities in Austria, Denmark, Germany, Spain and Switzerland. Good performance is also found in areas like the main cities of the Netherlands, Riga, Stockholm, Oslo and Glasgow. A large difference between the metrics on Map 3 and Map 4 indicates a low frequency of efficient rail connections. In other words, fast services are possible in these locations but are quite rare in practice. This may indicate underutilisation of the existing infrastructure. 13 PASSENGER RAIL PERFORMANCE IN EUROPE: REGIONAL AND TERRITORIAL ACCESSIBILITY INDICATORS FOR PASSENGER RAIL Map 3: Transport performance by rail plus a short walk (optimal travel time), 2019 Canarias Guadeloupe Martinique Guyane Mayotte Réunion Açores Madeira REGIOgis Population within a 1.5-h journey / population within a 120-km radius × 100 0 20.1 - 25 Uninhabited 0.1 - 10 25.1 - 35 No data 10.1 - 15 35.1 - 45 15.1 - 20 > 45 The map shows the population-weighted average for cells of 5 × 5 km for better visualisation. The analysis was done for 1 × 1 km cells. Sources: REGIO-GIS; UIC; railway operators; JRC. 0 500 km © EuroGeographics Association for the administrative boundaries 14 Map 4: Transport performance by rail plus a short walk (average travel time), 2019 Canarias Guadeloupe Martinique Guyane Mayotte Réunion Açores Madeira REGIOgis Population within a 1.5-h journey / population within a 120-km radius × 100 0 20.1 - 25 Uninhabited 0.1 - 10 25.1 - 35 No data 10.1 - 15 35.1 - 45 15.1 - 20 > 45 The map shows the population-weighted average for cells of 5 × 5 km for better visualisation. The analysis was done for 1 × 1 km cells. Sources: REGIO-GIS; UIC; railway operators; JRC. 0 500 km © EuroGeographics Association for the administrative boundaries The attractiveness of rail travel obviously depends on the speed or the frequency of the services, but also on the possibilities of feeder transport before and after the train trips. In the scenario we just described, rail travel is only combined with a short walk, resulting in a rather minimalistic vision of rail accessibility. Assessing multimodal accessibility faces additional challenges, especially when considering complementary public transport. Currently, no comprehensive data on bus, tram and metro timetables are available at the European level. Despite this obstacle, we can evaluate the effect of using short bike rides as complementary travel. Such trips could also be done on e-scooters or e-bikes. Combining rail and bike improves the levels of accessibility by enlarging the area around the stations that can be reached quickly. The effect of this combination is shown on Map 5 and Map 6. Optimal rail services combined with a short bike ride before and/or after the train ride (Map 5) result in transport performance levels reaching more than 75 %, mostly in areas like Berlin, Paris, Barcelona and part of London, but also in 15 PASSENGER RAIL PERFORMANCE IN EUROPE: REGIONAL AND TERRITORIAL ACCESSIBILITY INDICATORS FOR PASSENGER RAIL various other cities in the north of France and in Spain, where fast connections to large cities are available. Average total travel time, more convenient for day-to-day-travel, combined with a bike ride (Map 6) still results in good levels of performance (more than 60 %) in most of the cities mentioned before, but also in Madrid and many smaller Spanish cities, Copenhagen, Malmö and Glasgow. Under the same scenario, at least 40 % of population can be reached from areas in the Netherlands, Rome, Stockholm, Lisbon, Munich and Hamburg. In comparison to the average performance of rail + walking, combining rail and bike results in a substantial improvement of transport performance, for instance in Ciudad Real and Cuenca in Spain, where the increase is close to or even higher than the total population in the 120-km radius neighbourhood. It should be noted that these performance levels assume that the traveller uses a bike both before taking the train and after arriving at the destination station. For instance, the traveller can use a folding bike, a scooter or a bike-sharing system. Map 5: Transport performance by rail plus short bike rides (optimal travel time), 2019 Canarias Guadeloupe Martinique Guyane Mayotte Réunion Açores Madeira REGIOgis Population within a 1.5-h journey / population within a 120-km radius × 100 0 20.1 - 25 Uninhabited 0.1 - 10 25.1 - 35 No data 10.1 - 15 35.1 - 45 15.1 - 20 > 45 The map shows the population-weighted average for cells of 5 × 5 km for better visualisation. The analysis was done for 1 × 1 km cells. Sources: REGIO-GIS; UIC; railway operators; JRC. 0 500 km © EuroGeographics Association for the administrative boundaries 16 Map 6: Transport performance by rail plus short bike rides (average travel time), 2019 Canarias Guadeloupe Martinique Guyane Mayotte Réunion Açores Madeira REGIOgis Population within a 1.5-h journey / population within a 120-km radius × 100 0 20.1 - 25 Uninhabited 0.1 - 10 25.1 - 35 No data 10.1 - 15 35.1 - 45 15.1 - 20 > 45 The map shows the population-weighted average for cells of 5 × 5 km for better visualisation. The analysis was done for 1 × 1 km cells. Sources: REGIO-GIS; UIC; railway operators; JRC. 0 500 km © EuroGeographics Association for the administrative boundaries NATIONAL RESULTS Rail transport performance in the EU + EFTA + UK is only 7 % for average travel time combined with two short walks (Figure 3). Switching the short walks to a short bike ride, however, has a big impact. It increases performance from 7 % to 11 % with one bike ride and even to 16 % with two bike rides. In other words, replacing two short walks with two short bike rides more than doubles the number of people that can be reached by train. Rail performance using optimal travel time combined with two short walks is higher (10 % compared to 7 %). Optimal travel time means that a traveller selects the fastest connection and does not have to wait before boarding the train. As a result, that person can reach more stations within 90 minutes and can thus reach more people. Also, for optimal travel time, riding a bike instead of walking makes a big difference: it increases 17 PASSENGER RAIL PERFORMANCE IN EUROPE: REGIONAL AND TERRITORIAL ACCESSIBILITY INDICATORS FOR PASSENGER RAIL performance in the EU + EFTA + UK from 10 % to 15 % with one bike ride and to 23 % with two bike rides. It should not come as a surprise that in the Netherlands, half of the people taking the train also ride their bike to and/or from the station (Verkade and Te Brömmelstroet, 2020). Figure 3: Rail transport performance per country for different walk and bike combinations, 2019 (a) Average travel time (b) Optimal travel time EU + EFTA + UK EU + EFTA + UK ES DK CH AT UK FR LV DE SE NO LU EE NL PT FI BE IT HU IE CZ EL PL HR BG SI SK RO LT CH ES DK AT UK FR DE LV SE NO BE LU NL PT EE IT FI HU CZ IE PL EL HR BG SI SK RO LT 0 5 10 Walk + rail + walk 15 20 25 30 35 Bike + rail + walk Bike + rail + bike Population within a 1.5 hour journey / population within a 120-km radius x 100 0 5 10 Walk + rail + walk 15 20 25 30 35 Bike + rail + walk Bike + rail + bike Population within a 1.5 hour journey / population within a 120-km radius x 100 Source: REGIO-GIS. The rail performance using both the average travel time and optimal travel time varies substantially between countries (Figure 3). Most of the EU Member States that joined in 2004 or later tend to score low to very low on rail performance, while most of the others score better. A few exceptions stand out: Estonia, Hungary and Latvia score rather well, while Greece and Ireland score rather low. The impact of replacing a walk by a bike ride varies by country, depending on the population density around the train stations. For example, the United Kingdom has a lower rail performance with average travel time and two short walks than Austria. However, if one or both walks are replaced by bike rides, the United Kingdom scores much higher, which implies that the population density around United Kingdom train stations (and beyond the area within a short walk’s distance) is higher than around Austrian ones. Figure 4 and Figure 5 provide another look at the variety in transport performance by country. The population of each country is classified according to the level of transport performance the inhabitants have at their disposal. Under the most minimalistic scenario, when combining average travel time with short walks to/from the stations (Figure 4), performance levels of more than 50 % are uncommon. Only in France and Spain does at least 10 % of population benefit from such a performance level. However, when using the fastest connections available in combination with bike trips to and from the station, performance of more than 50 % is available to 16.1 % of the EU + EFTA + UK population, with shares of more than 30 % of the population in Latvia and Spain (Figure 5). 18 Figure 4: Population by level of transport performance by rail (average travel time) combined with short walks, 2019 100 % Share of population, in % 90 % 80 % 70 % 60 % 50 % 40 % 30 % 20 % 10 % 0% LT RO SK SI BG HR PL EL CZ IE HU IT BE FI PT NL EE LU NO SE DE LV FR UK AT CH DK ES EU-27 + EFTA + UK 0-5 5-10 10-20 20-30 30-40 40-50 >50 Source: REGIO-GIS. Figure 5: Population by level of transport performance by rail (optimal travel time) combined with short bike rides 100 % Share of population, in % 90 % 80 % 70 % 60 % 50 % 40 % 30 % 20 % 10 % 0% LT RO SI SK HR BG IE PL CZ EL LU FI HU PT IT EE NO BE NL LV AT SE FR DE DK ES CH UK EU-27 + EFTA + UK 0-5 Source: REGIO-GIS. 5-10 10-20 20-30 30-40 40-50 >50 PASSENGER RAIL PERFORMANCE IN EUROPE: REGIONAL AND TERRITORIAL ACCESSIBILITY INDICATORS FOR PASSENGER RAIL REGIONAL RESULTS While grid data provide the highest level of spatial detail, there is also a clear need for more aggregated indicators, especially at geographical levels, to allow an easy combination with other sources of indicators. In this section, we present results aggregated at the level of NUTS 3 (11) regions. In many areas, this level still provides an adequate variety of spatial patterns. Obviously, the same indicators can also be produced at NUTS 2 level or higher (12). All NUTS regional values are populationweighted averages of the grid data. This means that the accessibility level of each grid cell in a particular region is taken into account, according to the number of inhabitants living in that grid cell. This section focuses on the NUTS 3 results for rail performance. Performance levels by NUTS 3 region are illustrated in the map series 7 and 8, showing six variants of performance metrics. Considering the average total travel time of the services – thus taking into account the frequency of the services, combined with a short walk to/from the stations (Map 7A) – only a few, mostly urban regions attain relatively decent levels of accessibility. Accessibility levels that are lower than 5 %, which exist in many – even urban – regions, scarcely encourage people to envisage rail travel for day-to-day purposes. If we consider the fastest available travel option, again combined with walking (Map 8A), NUTS 3 regional accessibility reaches 19 values of more than 35 % in some regions. Amongst the hotspots are Paris, Berlin, Barcelona, Copenhagen and London, but also regions such as Zaragoza and Valladolid in Spain, because of the presence of efficient links to high-speed train services. Rail performance can be improved by combining the train trips with a short bike ride, replacing a short walk. Map 7B and Map 8B depict the combinations of a bike ride to the departure station and a walk from the arrival station to the final destination. Performance can be further improved by replacing the walk to the destination with a short bike ride. This scenario is also illustrated by Map 7C and Map 8C. In average circumstances (Map 7C), this combination results in a transport performance higher than 20 % in 230 out of 1 441 regions. The top performance levels are then in the 70 % + range. Finally, combining short bike rides with the fastest available train trips opens up many more opportunities for efficient day-to-day travelling (Map 8C). Under this scenario, the number of regions with a performance level higher than 20 % becomes 440 out of 1 441 regions. Combining rail travel with a short bike ride boosts optimal rail transport performance to more than 75 % in regions such as Berlin, Paris and parts of London. At the lower end of the performance spectrum, combining rail with cycling can quite easily double the accessibility levels in comparison to those reached with a rail plus walking combination. (11) The NUTS classification (nomenclature of territorial units for statistics) is a hierarchical system for dividing up the economic territory of the EU for the purpose of the collection, development and harmonisation of European regional statistics and socioeconomic analyses of the regions. Three different NUTS levels are distinguished, i.e. (i) NUTS 1: major socioeconomic regions, (ii) NUTS 2: basic regions for the application of regional policies and (iii) NUTS 3: small regions for specific diagnoses. (12) The data annex to this paper provides aggregated values for all NUTS levels. 20 Transport performance by rail (average travel time) per NUTS 3 region, 2019 Map 7: Transport performance by rail (average travel time) per NUTS 3 region, 2019 (A) Walk + rail + walk (B) Bike + rail + walk (C) Bike + rail + bike Population within a 1.5-hour journey / population within a 120 km radius × 100 <= 2.5 15 - 20 2.5 - 5 20 - 27.5 5 - 10 27.5 - 35 10 - 15 > 35 Performance using the average travel time (including waiting before boarding) of trips available for departure during morning peak hours. Sources: REGIO-GIS; UIC; railway operators; JRC. 0 1 000 km © EuroGeographics Association for the administrative boundaries 21 PASSENGER RAIL PERFORMANCE IN EUROPE: REGIONAL AND TERRITORIAL ACCESSIBILITY INDICATORS FOR PASSENGER RAIL by rail2019 (optimal travel time) per NUTS 3 region, 2019 Map 8: Transport performance by rail (optimalTransport travel time)performance per NUTS 3 region, (A) Walk + rail + walk (B) Bike + rail + walk (C) Bike + rail + bike Population within a 1.5-hour journey / population within a 120 km radius × 100 <= 2.5 15 - 20 2.5 - 5 20 - 27.5 5 - 10 27.5 - 35 10 - 15 > 35 Performance using optimal trips available for departure during morning peak hours. Sources: REGIO-GIS; UIC; railway operators; JRC. 0 1 000 km © EuroGeographics Association for the administrative boundaries 22 We can expect higher levels of transport performance if the population is concentrated closer to the point of departure. A straightforward way of examining this concentration is to compute the share of population in a 60-km radius as a percentage of the population within a 120-km radius. The higher levels of rail transport performance are mostly found in regions where a large share of the neighbouring population is found within a 60-km radius (Figure 6). This is logical, as it will be easier to serve a population within 60 km in less than 1.5 hours than to provide the same level of accessibility to a population further away. Still, a high population concentration relatively close to the place of departure is by no means a guarantee of high transport performance. Figure 6: Relationship between transport performance by rail plus a short walk (optimal travel time) and population concentration around the place of departure, at NUTS 3 level Population within a 60-km radius as % of population within a120-km radius 100 90 80 70 60 50 40 30 20 10 0 0 5 10 15 20 25 30 35 40 45 50 55 60 Bubble size represents NUTS 3 population Source: REGIO-GIS. NB: The figure covers all countries where NUTS 3 regions or equivalent statistical regions have been defined (EU + EFTA + AL + ME + MK + RS + UK). Integrated timetable data on all public transport modes in Europe are not yet available, so public transport performance – where rail is combined with other types of public transport – cannot be calculated. Nevertheless, an analysis of public transport performance in some major cities provides some hints. We evaluated the accessibility of the population inside these cities using public transport schedules (combined with walking if needed) and – alternatively – using cycling. In most of the cities, the findings suggest that transport performance levels by public transport are lower than by cycling when considering trips of a maximum of 30 minutes (13). Hence, we may assume that the levels of regional transport performance provided by a combination of rail and other public transport might be somewhat lower than the levels we found by combining rail and bike. DEGREE OF URBANISATION RESULTS The analysis shows that large cities tend to provide higher levels of rail performance. Aggregating the results by the degree of urbanisation (14) will help to assess whether more urbanised areas consistently have a better performance. The degree of urbanisation distinguishes (1) rural areas, (2) towns and suburbs and (3) cities, using a classification based on population size and density (15). This threefold typology is already very relevant for the assessment of accessibility levels, but an even better distinction between areas with a dispersed versus a concentrated population is certainly interesting. For this reason, we present results according to the degree of urbanisation level 2. This distinguishes towns from suburbs and creates three sub-categories of rural areas: villages, dispersed rural areas and mostly uninhabited grid cells (16). Rail performance in optimal circumstances (combined with a short walk) is illustrated in Figure 7. The cities with the best (13) The analysis of accessibility and transport performance in cities is described in Poelman, Dijkstra and Ackermans (2020). (14) The degree of urbanisation classifies grids and local administrative units. In this paper, only grid-level results are shown. For ease of reading, the terminology for the local administrative units is used (city, town, etc.) instead of the terminology for the grid cells (urban centre, dense urban cluster, etc.). (15) For a detailed description of the degree of urbanisation, see the Eurostat Methodological Manual on Territorial Typologies – 2018 edition (https://ec.europa.eu/ eurostat/web/products-manuals-and-guidelines/-/ks-gq-18-008). (16) For a detailed description, see Applying the Degree of Urbanisation – A methodological manual to define cities, towns and rural areas for international comparisons – 2021 edition (https://ec.europa.eu/eurostat/web/products-manuals-and-guidelines/-/ks-02-20-499). 23 PASSENGER RAIL PERFORMANCE IN EUROPE: REGIONAL AND TERRITORIAL ACCESSIBILITY INDICATORS FOR PASSENGER RAIL accessibility performance are in Denmark, France, Austria, Spain and Switzerland. The high value for Danish cities can be explained by the presence of a dense suburban network in Copenhagen and its surroundings. At the other extreme of the graph, rail performance is almost zero in Lithuanian cities. Compared to larger cities, smaller towns are less wellconnected for rail travel in most countries, although their performance is not far behind in Luxembourg, Sweden and Switzerland. Population accessible by rail within 1.5 hours / population in a 120-km radius x 100 Figure 7: Transport performance by rail plus a short walk (optimal travel time), by country and refined degree of urbanisation, 2019 40 DK 35 AT FR CH ES 30 25 DE UK LV NO 20 15 10 5 LT RO EL SK SI BG HR PL IT NL LU EE HU CZ IE SE BE FI PT 0 -5 NB: Countries ranked by the value of cities; countries without railways (CY, MT) are not shown. Bubble size is the share of the national population living in the area Cities Suburbs Dispersed rural areas Towns Mostly uninhabited Villages Source: REGIO-GIS. When looking at rail performance in average circumstances – thus taking into account the effect of the frequency of services – Figure 8 at first glance presents a similar picture, although obviously with lower levels of performance. A closer look reveals differences in the country rankings. The top scores for cities are again found in Denmark, France, Spain and Austria, suggesting very good levels of service frequency in and around the cities in those countries. Belgium and the Netherlands now have almost the same values for cities, although Belgium scores much better when looking at optimal connections (see Figure 7). This indicates that services in Belgium provide a better potential connectivity than in the Netherlands, but with lower average frequencies. In practice, the values of Figure 8 are the most relevant for day-to-day travel. Indeed, the attractiveness of rail services for commuting purposes implies a good frequency of the services. For smaller towns, average rail performance still reaches decent levels in Denmark, Austria and Switzerland. 24 Population accessible by rail within 1.5 hours / population in a 120-km radius x 100 Figure 8: Transport performance by rail plus a short walk (average travel time), by country and refined degree of urbanisation, 2019 40 35 DK 30 FR AT ES 25 20 15 10 5 LT RO SK PL SI EL BG HR CZ NL BE HU IE LU IT EE SE PT FI UK DE CH LV NO 0 -5 NB: Countries ranked by the value of cities; countries without railways (CY, MT) are not shown. Bubble size is the share of the national population living in the area Cities Suburbs Dispersed rural areas Towns Mostly uninhabited Villages Source: REGIO-GIS. Optimal rail trips combined with short bike rides lead to a relative accessibility of more than 55 % in cities in Denmark and France (Figure 9). In Germany, Spain, Sweden and Norway, performance is also close to 50 %. Population accessible by rail within 1.5 hours / population in a 120-km radius x 100 Figure 9: Transport performance by rail plus short bike rides (optimal travel time), by country and refined degree of urbanisation, 2019 FR DK 60 55 50 CH 45 BE EE IT FI NL PT HU 40 35 30 PL LU CZ 25 BG 20 15 10 DE ES SE NO UK LV AT LT RO IE EL HR SK SI 5 0 -5 NB: Countries ranked by the value of cities; countries without railways (CY, MT) are not shown. Source: REGIO-GIS. Bubble size is the share of the national population living in the area Cities Suburbs Dispersed rural areas Towns Mostly uninhabited Villages 25 PASSENGER RAIL PERFORMANCE IN EUROPE: REGIONAL AND TERRITORIAL ACCESSIBILITY INDICATORS FOR PASSENGER RAIL Population accessible by rail within 1.5 hours / population in a 120-km radius x 100 Figure 10: Transport performance by rail plus short bike rides (average travel time), by country and refined degree of urbanisation, 2019 60 55 50 SE ES 45 40 35 IT NL HU EL PT BE IE 30 25 20 SI SK 15 10 5 LT EE DE UK FI CH FR NO LV DK AT HR CZ LU BG PL RO 0 -5 NB: Countries ranked by the value of cities; countries without railways (CY, MT) are not shown. Bubble size is the share of the national population living in the area Cities Suburbs Dispersed rural areas Towns Mostly uninhabited Villages Source: REGIO-GIS. In all of the Member States, cities benefit the most from the rail + bike combination in terms of their transport performance score. However, smaller towns also benefit from the bike + rail + bike combination, especially in countries like Denmark, Germany, Latvia, Sweden, Norway, Switzerland and the United Kingdom. This benefit is most visible when considering the optimal available connections, but it becomes somewhat less obvious when looking at the average travel times during peak periods (Figure 10). In rural areas, performance also increases when combining the rail trip with a short bike ride instead of a short walk, although the impact is much lower. These results argue in favour of further developing cycling-friendly infrastructure in and around railway stations. CITY-LEVEL ANALYSIS The previous section showed that cities consistently outperformed other types of areas. However, the performance of individual cities also varies (Map 9). Two types of cities tend to score high on rail performance: large cities with an extensive rail system (notably Barcelona, Paris, Zaragoza, Copenhagen, Berlin, Madrid and London) and smaller cities with a fast connection to a nearby large city (such as Valladolid, Ciudad Real and Reims). At the other end of the spectrum, cities in Lithuania, Romania, Bulgaria and the western Balkans all score very low. In 66 mostly small cities, there are either no trains or no trains during the peak hours we investigated (shown with an empty circle on the map). 26 Map 9: Transport performance by rail plus a short walk (optimal travel time) per urban centre, 2019 Canarias Guadeloupe Martinique Guyane Mayotte Réunion Açores Madeira REGIOgis Population within a 1.5-hour journey / population within a 120-km radius × 100 Urban centre population < 100 000 0 or no rail 17.9 - 25 100 000 - 250 000 < 2.5 25 - 35 250 000 - 500 000 2.5 - 5 35 - 50 500 000 - 1 000 000 5 - 10 >= 50 Performance using optimal trips available for departure during morning peak hours. Sources: REGIO-GIS; UIC; railway operators; JRC. 1 000 000 - 5 000 000 10 - 17.9 >= 5 000 000 0 500 km © EuroGeographics Association for the administrative boundaries 27 PASSENGER RAIL PERFORMANCE IN EUROPE: REGIONAL AND TERRITORIAL ACCESSIBILITY INDICATORS FOR PASSENGER RAIL These data also reveal the city pairs that have a fast connection, a slow connection or no connection within 90 minutes. In total, 16 city pairs have a connection speed of over 150 km/h (Table 1). They are located in Belgium, France, Germany, Italy, Spain and the United Kingdom. Most of them are domestic connections. There are only three cross-border connections: Paris-Brussels, Brussels-Lille and London-Lille. City B (smaller) Milan Bologna Straight line speed (km/hour) City A (larger) City B (smaller) Bergamo Busto Arsizio 40 Greater Nottingham Coventry 47 Rotherham Newcastle-underLyme 48 Portsmouth Bournemouth 49 Leicester Coventry 49 226 Copenhagen Malmö 53 Brighton 56 Table 1: City pairs with a rail connection speed of at least 150 km/h and an optimal travel time of a maximum of 90 minutes City A (larger) Table 2: City pairs with a rail connection speed of less than 60 km/h and an optimal travel time of a maximum of 90 minutes Straight line speed (km/hour) Madrid Saragossa 211 Southampton Paris Lille 190 Liverpool Rotherham 57 Paris Brussels 190 Florence 188 Newcastle-underLyme 59 Rome Greater Nottingham Barcelona Saragossa 181 London Lille 179 Madrid Valladolid 175 Malaga Cordoba 171 Milan Turin 171 Naples Rome 168 Berlin Hannover 168 Seville Cordoba 167 Brussels Lille 159 London Coventry 158 London Newcastleunder-Lyme 153 Source: REGIO-GIS. NB: Only cities with at least 250 000 inhabitants were considered. Only nine city pairs have a rail connection that is slower than 60 km/h (Table 2). Seven of these are located in the United Kingdom. There are 17 city pairs that are less than 120 km apart with no rail connection within 90 minutes, of which nine are located in the United Kingdom (Table 3). Source: REGIO-GIS. NB: Only cities with at least 250 000 inhabitants that are less than 120 km apart were considered. Connections between stations that are less than 25 km apart were excluded. Table 3: City pairs with no rail connection within 90 minutes Straight line distance (km) City A (larger) City B (smaller) Upper Silesia Metropolitan Union Cracow 75 Rouen Cergy-Pontoise 84 Newcastle-underLyme Wakefield 85 Malaga Granada 86 Bristol Bournemouth 93 Leeds / Bradford Newcastle-underLyme 95 Greater Manchester Greater Nottingham 95 Brighton Southend-on-Sea 99 Bristol Southampton 102 Stockton-on-Tees Kingston-upon-Hull 105 Greater Nottingham Kingston-upon-Hull 106 Rotherham Coventry 107 Stuttgart Strasbourg 110 Lyon Geneva 113 Porto Vigo 118 Geneva Grenoble 118 Prague Dresden 120 Source: REGIO-GIS. NB: Only cities with at least 250 000 inhabitants that are less than 120 km apart were considered. 28 The city-level data allow us to test to what extent rail performance depends on the extent of the rail network. A simple comparison between the availability of rail networks around cities and the level of rail performance (Figure 11) shows that transport performance levels do not depend on the length of the network when taking into account population and area. For instance, both Copenhagen and Prague have a railway length index of around 200 % of the city average. However, only 28 % of Prague’s neighbouring population is accessible, while for Copenhagen the figure is 62 %. Hence, the available network around Copenhagen provides better accessibility than the network around Prague. Further analysis of this relationship could be interesting, for instance taking into account the actual characteristics of the network, such as the presence of doubletrack versus single-track railways or the level of electrification. Information about the maximum operational speed of the railway lines would also help in interpreting the differences in transport performance. Railway length (index, average around urban centres = 100) Figure 11: Transport performance by rail plus short bike ride (average travel time) and network density around cities, 2019 250 200 150 100 50 0 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 Bubble size represents city population Railway length is expresed relative to land area and the population within a 120-km radius Sources: REGIO-GIS; railway length: EuroGeographics EuroRegionalMap. NB: The railway length index is the average of the network length in an area of a 120-km radius around the city’s central point divided by the population, and that same network length divided by the land area of that circle. High levels of transport performance from within cities usually occur where the population is quite concentrated in areas close to the centre of the city (Figure 12). For example, Madrid has a high performance (68 %) and its average distance to population is less than 25 km. Cities with a high distance to the population tend to have a lower rail performance. Nevertheless, the distance to the population only explains a small share of the variation in rail performance (17). Cities such as Barcelona, Copenhagen, Berlin and Glasgow combine a high transport (17) The R-square value is 0.17. performance with a distance to population of between 30 and 40 km. Conversely, other cities with a similar distance to population only reach moderate (Rome, Tallinn and Lisbon) or low (Naples) rail performance. A combined analysis of population concentration, railway network characteristics and transport performance could provide further insights into opportunities for improvement of rail accessibility and performance levels. 29 PASSENGER RAIL PERFORMANCE IN EUROPE: REGIONAL AND TERRITORIAL ACCESSIBILITY INDICATORS FOR PASSENGER RAIL Figure 12: Transport performance by rail plus short bike rides (average travel time) and average distance to the nearby population (within 120 km) per city. 100 Distance to population (km) 90 80 70 60 50 40 30 20 10 0 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 Bubble size represents city population Source: REGIO-GIS. NB: Distance to the population is the average of the straight-line distances to each of the populated grid cells within a 120-km radius, whereby each of these cells is weighted by its residential population. 30 CONCLUSIONS This paper presents an overview of rail accessibility using a recently developed framework. By comparing the number of people who can be reached by train to the number of people nearby, we can measure and compare rail performance across countries, regions and territories. The analysis reflects the experiences of different types of rail travellers by providing indicators for optimal and average travel times as well as for rail trips combined with short walks, short bike rides or both. The rail performance metric is intended to support the assessment of investment needs in regions, cities and territories. To obtain the full picture, the metric should be combined with a description of the spatial distribution of population and activities, the geography and terrain of the areas and characteristics of the rail network. This combination can show whether the priority should be: (a) better use of existing infrastructure; (b) upgrading of infrastructure; (c) measures favouring the densification of land use around train stations and limiting urban sprawl; or (d) promoting and facilitating cycling to train stations. The results of the analysis reveal wide disparities. Most eastern Member States have a rail performance that is below the EU average. In these countries, low performance is seen even in the more densely populated regions. In such regions where potential demand for rail trips may be sufficient, rail performance could be improved by increasing service frequencies or by upgrading the rail network to accommodate faster trains. Extending the rail network to improve access should be considered where benefits are likely to exceed the costs. Performance is much lower in rural regions as compared to capital regions and cities. In these regions, most people live far from a train station, which cannot be resolved by increasing the frequency or speed of trains. Due to the dispersed population, it is likely to be too expensive to address this by extending the rail network. Instead, policies for these regions could focus on improving access to the existing rail network and ensuring connectivity by complementary modes. Concerning this aspect, the analysis shows that rail performance more than doubles when each walk to and from the station is replaced with a short bike ride. Given that bike lanes, paths and parking areas tend to be inexpensive, promoting cycling and other micro-mobility modes must be a cost-effective way of improving rail performance. The detailed level of the analysis allows for the results to be easily aggregated to municipalities, regions and countries. Therefore, the same data can feed debates at the EU, national, regional and local levels. Annual updates of this analysis could help to monitor rail passenger performance. The main obstacle to an annual update, however, is the lack of a single authoritative source of harmonised and integrated passenger rail timetables. In the future, the legal framework on the provision of EU-wide multimodal travel information services (18) is likely to address this obstacle. Additional sources of information could also further enrich this analysis. Comprehensive data on public transport would allow multi-modal trips to be taken into account, such as trips combining bus and train. It would also allow a more in-depth analysis of the importance of access to the station, taking into account the role of public transport as an access mode. Including other modes of public transport would, however, significantly increase the computational demands of this analysis and would require more powerful IT tools and infrastructure, as well as developing and testing methods and tools to reduce calculation times. A day-time population and/or workplace-based employment grid could provide a more realistic indicator to capture destinations of interest. Reliable geo-referenced data on the infrastructure characteristics of the railway network could help to identify why speeds or frequencies are low on specific lines. The analysis in the paper focuses on regional trips where rail mainly competes with the car. A similar analysis, using longer travel time thresholds and longer distance connections (e.g. 500 km), would allow an assessment of the viability of rail passenger transport as an alternative to short-distance flights in terms of travel time. (18) Commission Delegated Regulation (EU) 2017/1926 (http://data.europa.eu/eli/reg_del/2017/1926/oj). PASSENGER RAIL PERFORMANCE IN EUROPE: REGIONAL AND TERRITORIAL ACCESSIBILITY INDICATORS FOR PASSENGER RAIL ANNEX – METHODOLOGICAL DESCRIPTION INPUT DATA An analysis of rail performance requires data on the location of stations and on timetables of all available passenger services. Although various sources of machine-readable data are available, there is no single source that guarantees complete coverage of all of the services available in the EU. The Multiple East-West Railways Integrated Timetable Storage (MERITS) database, produced by the International Union of Railways (UIC), covers a vast number of services and is currently the best starting point to create a database of rail timetables in Europe. However, the MERITS data do not cover all operators, regions or countries. This lack of completeness may be related to the fact that the MERITS data are sourced from UIC members, and therefore the coverage may be incomplete for some countries where some railway undertakings are UIC members and others are not. This is, for instance, the case for Germany, where quite a large number of undertakings operating regional and local rail services do not seem to have their data included in MERITS. Moreover, for other countries (e.g. France, Spain) where all railway undertakings are members of UIC, the MERITS data have also proven to be incomplete. Therefore, a considerable effort was required to find additional sources and to integrate them into a single database. TOWARDS AN INTEGRATED DATASET OF PASSENGER RAIL TIMETABLES Additional national and regional sources were needed to enhance the completeness of the timetable information. At the same time, overlaps between MERITS data and timetables harvested from other sources had to be avoided. Hence, we needed to compare the coverage of MERITS timetables with timetables provided by other (national or regional) sources. This step consisted of splitting the services for each country in the MERITS data, separating (fully) national from international services (19). For each country and type of service, a separate set of general transit feed specification (GTFS) files was produced. For each of the countries where MERITS data were incomplete (e.g. France, Germany, Spain), the corresponding MERITS GTFS file sets mentioned above were deleted. A new slimmed-down version of the MERITS data was built, containing only the countries for which the coverage of railway services was deemed satisfactory. For each of the countries in the MERITS database, and each of the days covered in the data, the total number of services, total number of train-km and total number of stops were analysed, for both national and international services. The same analysis 31 was done for complementary national or regional sources of timetables, obtained mainly from national railway infrastructure managers and railway undertakings. When the difference in the coverage indicators was significant for a given country, the data for that country were removed from MERITS and replaced by the data from the national source(s). The comparison of the coverage was mainly done at country level, but to help identify variations within the country, the analysis metrics were also provided at NUTS 3 level (this was of particular relevance for larger countries, where the data typically came from regional/local operators). The process of comparing the data required some manual interventions, because the databases being compared did not always refer to exactly the same period. Some case-by-case evaluation was needed to ensure that the days selected for each of the databases represented the same type of day (in particular a typical working day outside of holiday periods). Manual intervention was also needed where the national sources were significantly more complete in terms of national services, but not necessarily in terms of international services. Some national sources contained only the domestic part of the international services. Manual analysis and verification of international services was needed to avoid double counting or data gaps. ASSESSING THE COMPLETENESS OF THE INTEGRATED DATASET After having integrated selected MERITS timetables with complementary national and regional timetable sources, a final assessment was needed to evaluate to what extent the integrated dataset included all of the expected data. For that purpose, the database was compared with the previous European railway services database. This was built by DG Regional and Urban Policy and the Joint Research Centre (JRC) in 2015, using data with reference year 2014 from MERITS and complemented with some national sources for countries and regions that were missing from MERITS. The visualisation of the network defined by the lines contained in the 2014 dataset and the new database confirmed that some railway lines were present in the former but not in the latter. These lines were identified and manually checked to see if the services were still available in 2019 and could thus be transferred from the previous dataset to the new one. The result of this integration work is a database of timetables, composed of the sources listed in Table 4. The data were selected to represent a working weekday in a typical working month (excluding the summer months or holiday periods) during 2019. As can be seen in the table, the range of dates covered in each available dataset did not always guarantee this. For this reason, additional research was carried out in order to check, wherever possible, that the differences due to considering different days in the integrated database were small (stable supply), and that the data did not reflect (19) The MERITS database was analysed for each separate country by separating the services running within each country (national services) and those that departed or arrived from a different country (international services). For the international services, the segment of the trips that actually crossed a national border was identified and the parts of the trips corresponding to each country were identified. This was done to facilitate the comparison with railway services databases, where international services were only available for the part of the trips that occurred within the country to which the database referred. 32 temporary reductions of services like those associated with lockdown measures due to the COVID-19 pandemic. TRAVELLING VIA MAJOR CITIES Some major cities may include terminal stations, each serving part of the surrounding territory. Adequate railway links crossing those centres may require travel from one (terminal) station to another, for instance by means of metro networks. In these cases, the metro network can be considered as a natural extension of the rail services, as it is used to link rail stations. To assess accessibility in the surroundings of such cities, the integrated dataset also takes into account major metro networks wherever possible. In particular, metro networks are included for those cities where it can be expected that metro links are an essential component in ensuring efficient links between rail stations (20). Table 4: Timetable datasets used to construct an integrated European dataset Country/area Level Source Date of services Multinational MERITS (UIC) 12 September 2019 AL National DG Regional and Urban Policy based on rail operator 17 Seeptember 2020 BA National DG Regional and Urban Policy based on rail operator 12 December 2019 DE National DELFI (NeTEx version) 24 June 2020 EE National PEATUS 7 November 2019 ES National RENFE Larga/media distancia 12 November 2019 ES National RENFE Cercanias 7 July 2020 ES National FEVE 2 July 2013 ES Regional Euskotren 21 November 2019 ES Regional FGC 25 June 2019 Various ES Regional CTM Mallorca 2 September 2020 FR National SNCF 15 October 2020 FR Regional Corse 5 September 2019 EL National National railways 21 November 2019 IE National National railways 5 September 2019 LT National National railways 1 October 2020 LV National National railways 12 December 2019 ME National DG Regional and Urban Policy based on rail operator 17 September 2020 MK National DG Regional and Urban Policy based on rail operator 17 September 2020 RO National National railways 5 September 2019 RS National DG Regional and Urban Policy based on rail operator 17 September 2020 UK National ATOC (Great Britain) 4 June 2020 UK Regional Northern Ireland Rail 9 January 2020 XK (*) National DG Regional and Urban Policy based on rail operator 17 September 2020 Multinational MERITS (UIC) 4 September 2014 Additional services Various EE National National railways 14 December 2017 EL National National railways 10 September 2015 FR Regional Corse 10 September 2015 IE National National railways 5 September 2013 NO National National railways 7 December 2017 UK Regional Northern Ireland Rail 10 September 2015 Source: JRC. (*) This designation is without prejudice to positions on status, and is in line with UNSCR 1244/1999 and the ICJ Opinion on the Kosovo declaration of independence. (20) Metro networks in Prague, Madrid, Barcelona, Paris, Rome, Milan, Budapest, Vienna, Lisbon, Warsaw, Bucharest and London have been taken into account. PASSENGER RAIL PERFORMANCE IN EUROPE: REGIONAL AND TERRITORIAL ACCESSIBILITY INDICATORS FOR PASSENGER RAIL TECHNICAL DATABASE INTEGRATION When integrating the various sources into a single database, each source was tagged in order to guarantee the uniqueness of the identifiers of stops and rail services, and to ensure that the data of each source could still be retrieved from the integrated dataset. In order to remove double stops, which are an inevitable result of combining multiple data sources, these doubles were eliminated as much as possible by clustering stops that were within a distance of 100 metres of each other. From each cluster, one stop was retained. In the timetables, all of the ‘stop_id’ tags of the stops in the cluster were replaced by the ‘stop_id’ tag of the retained stop. The integration of datasets covering different time zones required particular attention, so that the resulting integrated dataset did not introduce artificial/unrealistic delays or improvement in speed when services operated between stops in different time zones. In the integrated dataset, departure and arrival times in a common Europe/Brussels time were included. Corrections were also needed for services running later than midnight, to ensure that proper travel times could be calculated. DETERMINING PAIRS OF RAIL STATIONS FOR ORIGIN/DESTINATION COMPUTATIONS Assessing rail performance by comparing rail accessibility with proximity of population requires vast amounts of origin/ destination calculations between station pairs. The performance metrics defined in this paper took into account rail trips of a maximum duration of 1.5 hours. Hence, before starting the actual travel time calculations, it was useful to determine a list of station pairs between which it might be possible to travel within the determined maximum travel time. For each departure station, a list of potential arrival stations was drawn up. This list took into account the potential travel speed of services running from the departure station. At the same time, it was unnecessary to include destination stations that were too far away from the point of departure to be reachable within 1.5 hours. PRELIMINARY ANALYSIS OF TRIPS BETWEEN CITIES It can be expected that rail travel between cities is more efficient and faster than between smaller places. For that reason, travel times between cities were examined in a first step. The results of this analysis, in particular the maximum distances that can be travelled within 1.5 hours, were used to determine a reasonable radius around other (non-urban) departure stations. Finally, all destination stations within such a radius were taken into account for the origin/destination calculations. First, a distance matrix containing the geodesic distance linking each pair of city central points in Europe was computed. From this matrix, pairs of cities that were too far from each other to be reachable within the maximum travel time were already 33 excluded. In practice, all pairs linked by a distance of more than 450 km were excluded. In addition, we thought it useful to try to limit the list of city pairs even further. For this purpose, the results of the analysis of the average speed of all direct rail trips were used. Direct rail trips are defined as trips linking a stop with the next one. The straight-line length and related speed of these direct trips can easily be calculated using the timetables and the station location data. The aggregated figures of straight-line trip length (in vehicle km) by country of departure and by speed revealed substantial differences between countries. For each country, the speed corresponding to the 99th percentile (P99) of all direct trips was calculated. Some manifest outliers were excluded, because these could be due to errors in the timetables and/or the coordinates representing the location of the stations. The P99 speed, rounded to the nearest multiple of 10, defines a maximum distance that will be applied to constrain the list of city pairs. For instance, if almost all direct trips starting in a country operated at a straight-line speed of a maximum of 160 km/h, the maximum distance for potential trips of up to 1.5 hours was set at 240 km. Next, the list of city pairs was linked to the maximum distance per country, using the maximum distance relative to the country where the city of departure is located. Only those pairs of cities which fell within that maximum distance were selected. In order to run origin/destination calculations, the list of city pairs was translated into a list of station pairs. From the point locations representing the stations, we selected all of the stops for which rail timetables were reported and which were located in a city. For some cities, this led to a list of several dozen stations. Many of these may be stops of local importance that are not relevant for assessing the best available rail trips to other major cities. For this reason, a selection of major stations in each of the cities was made. First, the total number of departures per station was computed, as observed during a weekday between 6 a.m. and 8 p.m. From the timetable dataset, we also derived the maximum length of direct trips starting at each of the stations. If this maximum length was very low, it gave an indication that only local trains operate at that station. Consequently, a qualitative selection of major stations in cities with more than 10 stations was applied, guided by the variety in the numbers of departures by station, by the maximum length of direct trips starting at the station and by the names of the stations (21). Origin/destination calculations were performed for departure times, for every quarter of an hour during a 2-hour morning peak period (i.e. nine origin/destination calculation requests). This repetition was needed in order to capture differences in travel time that are due to a varying availability of (fast) services throughout the morning peak period. To define the peak hour period, we first counted the total number of departures within a country, summed by half hour. From this series, we computed a running sum of the number of departures by 2-hour period. The peak hour period was then defined as the 2-hour period in the morning during which the maximum number of departures was observed. (21) For instance, stations with well-known names were selected, as well as stations with a name containing certain keywords (for instance ‘Hbf’ in German). 34 Figure 13: Example of origin/destination calculation requests during morning peak hours 7.00 7.15 Requested departure �me 7.30 7.45 8.00 8.15 8.30 8.45 10.15 10.00 9.45 9.30 9.15 9.00 8.45 8.30 8.15 8.00 7.45 7.30 7.15 7.00 9.00 Requested departure �me Wai�ng �me (before boarding or due to transfer) Effec�ve travel �me Arrival �me Source: REGIO-GIS. For each requested departure time, an OpenTripPlanner Analyst (OTPA) trip calculation was launched, looking for the trip that provided the earliest arrival at the destination station (in the example shown in Figure 13 this is the shortest red line, leaving at 8.40 a.m.). The OTPA software allows many parameters to be used and changing these parameters influences the results. Determining the values for each parameter was an empirical choice. The following parameters were set: ― ― ― ― ― ― ― ― ― ― ― ― maxWalkDistance = 900 (m) maxTransferWalkDistance = 900 (m) walkSpeed = 1.33 (m/s) walkReluctance = 2.0 (= default) waitReluctance = 1.0 (= default) waitAtBeginningFactor = 0.4 (= default) transferSlack = 300 maxTransfers = 3 maxTimeSec = 10 800 clampInitialWait = -1 (= default) dominanceFunction = EarliestArrival transit modes = RAIL, SUBWAY, WALK. From the origin/destination calculations between cities, the fastest trips starting in main stations of each city were derived. First, we aggregated the travel time results by station pair, and determined the fastest trip (without any initial waiting time before boarding). We combined this result with the straight-line distance between the stops to calculate the speed of the connection. The optimal connections between stations were further aggregated by city pair. For each city pair, we kept the best possible connection and its length and speed. Finally, from that dataset we got the best available speed for each departure city. Multiplying this speed by 1.5 gave us an indication of the maximum expected possible distance that can be travelled in 1.5 hours starting from a station in the city. USING THE RESULTS OF THE TRAVEL TIMES BETWEEN CITIES The expected maximum travel distances by city helped us to determine a search radius from each of the stations on the European territory. The aim was to draw up a list of station pairs where we could expect that trips of a maximum of 1.5 hours might be possible. Hence, the search radius needed to cover all possible destinations. However, it was also important to avoid using an unnecessarily large radius, in order to somewhat limit the volume of origin/destination calculations. Using spatial interpolation, we determined the maximum expected distances observed in cities to obtain a 1 km² grid covering the entire territory (22). Consequently, areas that were relatively close to cities from where long distances can be travelled also got a relatively high search radius. To be on the safe side, we multiplied this raster result by 1.1, adding 10 % to all of the calculated distances. We also took into account the observed differences in travel speed between countries. For this purpose, we aggregated the observed maximum distances from cities by country and computed the 10th percentile of that distance. This value was allocated to the country as a proxy value representing the least performant connections, while avoiding obvious outliers. Furthermore, any country values of less than 90 km were replaced by a default minimum search radius of 90 km. Finally, for each of the grid cells we took the maximum of two values. (22) By means of inverse distance weighting, using 10 points for the interpolation, with a variable search radius and a distance power of 1.5. 35 PASSENGER RAIL PERFORMANCE IN EUROPE: REGIONAL AND TERRITORIAL ACCESSIBILITY INDICATORS FOR PASSENGER RAIL 1. The value interpolated from the cities’ expected travel distances. 2. The minimum search radius expected by country. The grid output, containing distances varying between 90 km and 326 km, was transferred to the point locations of all rail stops. Each stop got the distance value of the grid cell in which the stop is located. Hence, each stop now had a plausible search radius, within which a possible travel time within 1.5 hours could be expected. To determine the actual list of station pairs that required origin/ destination calculations, we first computed a table of all station pairs, whereby the distance between stations was a maximum of 360 km. Then we added the search radius around each of the departure stations to the table of station pairs, and selected only those pairs where the distance between stations was smaller or equal to the search radius around the departure stations. This selection was the final input for the OTPA origin/ destination calculations. In the same way as for the stations in cities, the calculations of travel times between all selected stations were performed nine times, for every quarter of an hour within a 2-hour morning peak period on a weekday. The calculations used the same set of OTPA parameters as mentioned above. From these calculations, two metrics per station pair were derived. 1. The optimal travel time, being the fastest travel time, excluding waiting time before the first boarding, but including transfer times if the trip was composed of more than one train ride. 2. The average total travel time, being the average of the time between the requested departure time (one of the nine selected departure times) and the arrival time. Hence, this travel time included initial waiting before first boarding, and reflected the availability of frequent or less frequent services providing the earliest arrival at the destination station. For each departure station and for each of the two metrics, the arrival stations that could be reached within 1.5 hours were selected. DETERMINING THE SPATIAL RELATIONSHIP BETWEEN STATIONS AND GRID CELLS The results of the origin–destination calculations needed to be converted to information related to (populated) grid cells, located in the neighbourhood of the departure and arrival stations. For this purpose we needed some spatial relationships between all of the stations and grid cells that are located nearby. Population data are provided by 1 km² grid cell (23). To combine the information about rail travel time with a short walk, we assumed that all populated grid cells in a square neighbourhood of 3 × 3 cells around a station were accessible within a 15-minute walk to or from the station. When combining rail travel time with a short bike ride, we took into account all of the grid cells that have their centroid at less than 3.2 km from the centroid of the cell in which the station is located. This criterion was used as a proxy for the area that is accessible within a 15-minute bike ride to or from the station. Figure 14: Grid cells within walking and/or cycling distance from departure and arrival station (a) Walk + rail + walk (b) Bike + rail + walk . . . Station Grid cell close to departure station Grid cell close to arrival station (c) Bike + rail + walk . . . Station Grid cell close to departure station Grid cell close to arrival station CALCULATING ACCESSIBLE POPULATION BY GRID CELL All populated grid cells close to an arrival station (i.e. within walking or cycling distance) were considered to be accessible from all of the populated grid cells that are located close to the . . Station Grid cell close to departure station Grid cell close to arrival station departure station. Hence, the spatial relationship between the stations and the nearby grid cells allowed the calculation of the number of people that can be reached from any grid cell by means of rail trips of a maximum of 1.5 hours, combined with a short walk or a short bike ride (24). (23) Data refer to 2018, from the JRC–GEOSTAT 1 km² grid. (24) The process that transfers the information about travel time between stations to population accessible from nearby grid cells is described in more detail on pp. 33–36 of an earlier working paper: Poelman et al. (2020). 36 During the aggregation process by grid cell, we took into account any potentially overlapping areas. A grid cell may be located close to more than one departure station, and the same destination grid cell may be located close to more than one arrival station. Hence, double counting of accessible population was avoided. For each grid cell, we stored the sum of the population living in grid cells that are close to any station that can be reached within 1.5 hours rail travel from any station that is close to the departure grid cell. FROM ACCESSIBILITY TO TRANSPORT PERFORMANCE: TAKING PROXIMITY INTO ACCOUNT The absolute figures on accessible population provided the values of the numerator of the transport performance calculation. The denominator (i.e. the proximity) is the population living within a 120-km radius around the departure grid cell. The population within that radius was computed for all populated grid cells, excluding the people that were living in overseas areas, as these could not be reached by rail (25). Finally, a grid of transport performance was calculated by taking the accessibility grid, dividing it by the proximity grid and multiplying the result by 100. AGGREGATING BY REGION OR TERRITORY The accessibility, proximity and performance that were computed at grid cell level were all aggregated by region or territory. All of the aggregates are population-weighted averages of the grid-level data. In this process, all of the populated grid cells were taken into account, including those with zero accessibility (because they were far away from any station). (25) More details on the proximity calculation can be seen in Poelman et al. (2020), p. 37. PASSENGER RAIL PERFORMANCE IN EUROPE: REGIONAL AND TERRITORIAL ACCESSIBILITY INDICATORS FOR PASSENGER RAIL REFERENCES Dijkstra, L. and Poelman, H., ‘A harmonised definition and rural areas: The new degree of urbanisation’, Working Paper 1/2014, European Commission, DG Regional and Urban Policy, Brussels, 2014 (http://ec.europa.eu/regional_policy/en/information/ publications/working-papers/2014/a-harmoniseddefinition-of-cities-and-rural-areas-the-new-degree-ofurbanisation). Dijkstra, L., Poelman, H. and Ackermans, L., ‘Road transport performance in Europe’, Working Paper 1/2019, Publications Office of the European Union, Luxembourg, 2019 (https:// ec.europa.eu/regional_policy/en/information/publications/ working-papers/2019/road-transport-performance-in-europe). Eurostat, Methodological Manual on Territorial Typologies – 2018 edition, Publications Office of the European Union, Luxembourg, 2019 (https://ec.europa.eu/eurostat/web/productsmanuals-and-guidelines/-/KS-GQ-18-008). Eurostat, Applying the Degree of Urbanisation – A methodological manual to define cities, towns and rural areas for international comparisons – 2021 edition, Publications Office of the European Union, Luxembourg, 2021, (https:// ec.europa.eu/eurostat/web/products-manuals-and-guidelines/-/ ks-02-20-499) International Transport Forum, 'Benchmarking accessiblity in cities: Measuring the impact of proximity and transport performance', International Transport Forum Policy Papers 68, OECD Publishing, Paris, 2019. Poelman, H. and Ackermans, L., ‘Towards regional and urban indicators on rail passenger services, using timetable 37 information’, European Commission, DG Regional and Urban Policy, Brussels, 2016 (http://ec.europa.eu/regional_policy/en/ i n fo r m a t i o n / p u b l i c a t i o n s / w o r k i n g - p a p e r s / 2 0 1 6 / from-rail-timetables-to-regional-and-urban-indicators-on-railpassenger-services). Poelman, H. and Ackermans, L., ‘Passenger rail accessibility in Europe’s border areas’, European Commission, DG Regional and Urban Policy, Brussels, 2017 (http://ec.europa.eu/regional_ policy/en/information/publications/working-papers/2017/ passenger-rail-accessibility-in-europe-s-border-areas). Poelman, H., Dijkstra, L. and Ackermans, L., ‘How many people can you reach by public transport, bicycle or on foot in European cities? Measuring urban accessibility for low-carbon modes’, Working Paper 1/2020, Publications Office of the European Union, Luxembourg, 2020 (https://ec.europa.eu/regional_policy/ en/ info rma t io n/ p ubl ic a t io ns / wo rking - p a p ers / 2020/ low-carbon-urban-accessibility). Poelman, H., Dijkstra, L. and Ackermans, L., ‘Rail transport performance in Europe: Developing a new set of regional and territorial accessibility indicators for rail’, Working Paper 3/2020, Publications Office of the European Union, Luxembourg, 2020 (https://ec.europa.eu/regional_policy/sources/docgener/ work/032020_rail_transport_performance.pdf). Spiekermann & Wegener, TRACC – TRansport ACCessibility at regional/local scale and patterns in Europe, ESPON, Luxembourg, 2012 (https://www.espon.eu/tracc). Verkade, T. and Te Brömmelstroet, M., Het recht van de snelste (The Right of the Fastest), De Correspondent, Amsterdam, 2020. 38 ACKNOWLEDGMENTS DATA This analysis would not have been possible without the help of several people. In particular, we thank Olivier Draily and Emile Robe for their essential contributions to the data preparation, transformation and analysis procedures, Theresa Gutberlet and Miguel Olariaga Guardiola at JRC Seville for their work on the integrated railway timetable, and Teresa Perez Castaño for the preparation of the maps. The aggregated indicators by region and by type of territory, along with the grid datasets of accessibility, proximity and transport performance, are provided in separate data packages. Getting in touch with the EU IN PERSON All over the European Union there are hundreds of Europe Direct information centres. You can find the address of the centre nearest you at: https://europa.eu/european-union/contact_en ON THE PHONE OR BY EMAIL Europe Direct is a service that answers your questions about the European Union. 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