TOWARDS REGIONAL AND URBAN INDICATORS ON RAIL PASSENGER SERVICES, USING TIMETABLE INFORMATION

Transcription

1 Working Papers A series of short papers on regional research and indicators produced by the Directorate-General for Regional Policy WP 02/2016 TOWARDS REGIONAL AND URBAN INDICATORS ON RAIL PASSENGER SERVICES, USING TIMETABLE INFORMATION Hugo Poelman and Linde Ackermans Regional and Urban Policy

2 > EXECUTIVE SUMMARY This Regional Focus presents a large step forward in analysing rail services in Europe. For the first time, it provides comprehensive and comparable information on the speed and the frequency of passenger services, covering all of the EU and Switzerland. Up until now, nobody has been able to gather this information and analyse it. Thanks to strong efforts in data collection and transformation, we can now show the dramatic differences in rail services within Europe. This allows us to show which countries, regions and cities have a particularly poor offer. The indicators we have created provide better quantitative knowledge to support conceiving and implementing cohesion policy for rail transport. The policy relevance of enhanced rail transport indicators is highlighted by the fact that cohesion policy is allocating almost EUR 19 billion to rail investments in the period , with most of these investments taking place in the less developed regions of the Union. Disclaimer: This Working Paper has been written by Hugo Poelman and Linde Ackermans, European Commission Directorate-General for Regional and Urban Policy (DG REGIO) and is intended to increase awareness of the technical work being done by the staff of the Directorate-General, as well as by experts working in association with them, and to seek comments and suggestions for further analysis. The views expressed are the authors alone and do not necessarily correspond to those of the European Commission. Cover image Thinkstock; Acknowledgments: Several people have contributed to the outcome of this analysis. In particular, we thank Lewis Dijkstra for his many valuable and sometimes challenging suggestions, Olivier Draily, Emile Robe and Pierre Moermans for lots of additional data preparation and transformation processes including customisation of GTFS validation tools, Marc Guigon and Alekos Karvounis for having facilitated the access to UIC data and Greek railway data respectively, and Nicolás Ibañez for having facilitated additional georeferencing of stations.

3 A WALK TO THE PARK? ASSESSING ACCESS TO GREEN AREAS IN EUROPE'S CITIES 1 > Contents 1 Analysing rail network and timetable data in Europe: a challenging task 1 2 Mapping the frequency and speed of rail passenger services 1 3 Frequency and speed of services by country or region 4 4 Rail accessibility of cities 7 5 Conclusion 13 6 Methodological annex 13 7 References 16

4 2 1 ANALYSING RAIL NETWORK AND TIMETABLE DATA IN EUROPE: A CHALLENGING TASK Describing the rail infrastructure endowment in Europe, including its spatial patterns, is quite feasible using one of the existing geographic datasets on railway networks, or a combination of these [1]. But assessing the performance of the services running on this network consistently throughout the continent proves to be a much more challenging task. Comprehensive, open and interoperable data on network use, more specifically timetable data, are currently not available for all EU Member States, and certainly not in a way involving the regional or urban dimension. Before being able to produce meaningful indicators, we faced a complex task in collecting, analysing, transforming and harmonising a variety of multiple datasets. The methodological annex to this paper provides more details on these preparatory processes. 2 MAPPING THE FREQUENCY AND SPEED OF RAIL PASSENGER SERVICES For a comprehensive view on rail passenger services, we can first explore their frequency, speed and distribution over the European territory. To facilitate comparisons between countries and regions, we have focused on rail passenger services running on an ordinary weekday [3]. Services during weekends, nights or only running in specific periods (e.g. specific tourist services operating only during summer months) are not taken into account. Our basic unit of analysis is any direct train trip [2] connecting two stations and leaving between 6:00 and 20:00 on an ordinary weekday from any station in the area under review. This analysis covers the whole EU territory as well as Switzerland. Assessing the network speed of these services is more problematic. This is due to the complexity of the railway network, in which it is often possible to define more than one single physical connection between two stations. In addition, there is currently no direct link available between the timetable data and the geographic data depicting the physical railway network. For these reasons, we calculate speed estimates along straight lines [4] representing the direct connections between each pair of stations. While these speed estimates will allow us to assess how fast one can get from station A to station B, the actual vehicle speed along the railway line will almost always be higher than the calculated Euclidian speed. Given the current data availability, we can calculate the average Euclidian speed for each of the direct connections operating on a typical weekday. The metrics of frequency and speed of these direct connections are shown on Maps 1 and 2. Both maps reveal the substantial diversity of services over the European territory. It is useful to note that the lines on these maps are not a schematic representation of the physical railway lines, but that they represent all direct connections between stations. For example, on the same railway line, local and intercity trains may operate, serving a different set of stops. High-speed services leaving from the same station and using the same high-speed line can provide direct connections to various stations, depending on the actual time during the day. Each of these types of service is depicted separately on the maps. Typical examples are the high-speed services starting in Paris or Madrid, resulting in the star-shaped patterns visible on Map 2. Map 1 shows the frequency of direct train trips, expressed as the average number of trains per direction and per hour. Many of the high-frequency connections can be found around major cities and between them, especially in Germany, the UK, Belgium, the Netherlands, Switzerland, Denmark and Austria. The two biggest European agglomerations, London and Paris, show a pattern of many direct connections to surrounding cities. Most of these connections show a medium frequency. Decent service frequencies are generally a prerequisite for efficient daily trips, especially when considering commuting opportunities. Without an appropriate service frequency, rail transport can hardly be considered as a valid alternative for road trips. Almost the entire networks in Bulgaria, Greece, Romania and the Baltic states are characterised by low or very low frequencies. To a certain extent, low frequencies can be related to the physical characteristics of railway lines, especially single-track lines. Some of the secondary networks in other countries also represent low frequencies, for example in Spain and Portugal, or in more remote areas in Sweden, Finland and Croatia.. For each connection between two subsequent stops, we have calculated the average hourly number of trips by direction. This provides us with an indicator of the frequency of direct services between each pair of stations. 1. E.g. the railway network layers of EuroGeographics' EuroRegionalMap or the OpenStreetMap data. A more in-depth description of network characteristics is provided in the RINF (register of infrastructure), managed by the European Railway Agency. 2. Throughout this paper, "trip" refers to movements of vehicles (trains) and not of individual passengers. 3. The selected day is Thursday 2 October This day falls outside all main holiday periods. According to various sources, this day does not correspond with any official festive holiday in any of the EU countries or Switzerland. We selected a Thursday in order to avoid possible distortions from timetable deviations at the beginning or the end of the work week. 4. We calculated the Euclidian distance between the coordinates of the two stations.

5 3 A WA L K TO T H E PA R K? A S S E S S I N G A C C E S S TO G R E E N A R E A S I N E U R O P E ' S C I T I E S Canarias Guadeloupe Martinique Guyane Mayotte Réunion Açores Madeira REGIOgis Frequency of direct rail connections, 2014 trains/direction/hour <= Average number of trains per direction and per hour, connecting two subsequent stops. All direct train trips between geolocated stations, starting between 6:00 and 20:00 on 02/10/2014 (Estonia: 01/02/2013; Ireland: 11/01/2013; Greece: 01/09/2015; Corsica: 08/09/2015; Northern Ireland: 05/05/2015). Sources: UIC, National Transport Authority Ireland, TrainOSE Greece, Chemins de Fer de la Corse, Translink Northern Ireland Railways, EuroGeographics, OpenStreetMap, TomTom, RRG, DG REGIO > no data or incomplete data 500 Km EuroGeographics Association for the administrative boundaries Map 1: Frequency of direct passenger train trips

6 4 Map 2, showing the estimated speed of the direct services, highlights the outstanding performance of the dedicated highspeed railway lines, for example in France, Spain and Germany, and of the use of tilting trains on conventional tracks, for example in Sweden or Italy. In some other countries, only a few major connections are used by trains running at a reasonable speed. Issues of low speed are especially visible in Romania and Bulgaria. In addition, secondary lines in several countries also operate at rather low speeds, often due to physical limitations (slopes, outline of valleys in mountain areas). The diversity in speed observed on parts of the Greek network reflects differences in modernisation of network segments. While it is unrealistic to expect a general upgrade of the connections to provide (very) high-speed services, networks with low speed services could obviously play a more important role in passenger transport if services could operate at a more reasonable speed. 3 FREQUENCY AND SPEED OF SERVICES BY COUNTRY OR REGION The breakdown of the timetable information into direct connections, together with the location information on the stops, also allows us to create aggregated indicators of frequency and speed by country or by region. For all stations where passenger trains leave, we register the region and/or country. In this way we can aggregate the connection data by region or country of departure. We can obtain the total straight-line length of all direct vehicle trips starting in any station of the region, the total travel time of these trips, their average speed and classification of the trips according to speed categories. Finally, we can calculate an indicator of service intensity by dividing the aggregated vehicle trip length (in vehicle kilometres) by the total population of the region or country. Graph 1 shows the aggregated trip length per inhabitant of all trips departing in the country, classified by speed category. It first highlights the substantial diversity in service intensity between countries, but also the wide range in the share of higher speed services in the total length of all trips. Relatively high-speed trips account for a large share of the total trip length in countries such as Sweden, France, Finland or Spain. However, despite this, the total trip length in Spain is amongst the lower country values, relative to countries' populations. This reflects the relatively low density of the network, as well as the rather low frequencies on the secondary network, where a performance upgrade might be needed more than further extension of the (very) high-speed services. The regional dimension is illustrated on Map 3, showing the intensity of services with a speed of more than 80 km/h, aggregated by NUTS2 region of departure. As the values are expressed relative to regions' populations, some of the higher values shown on the map essentially reflect the effect of low population density (e.g. some regions in Sweden, Finland and Spain). Nevertheless, the map clearly shows the highest service intensities in central, northern and western regions, while most of the eastern EU regions and some of the southern ones are clearly lagging behind in terms of the availability of rail services running at a relatively high speed. Graph 1: Length by inhabitant of rail connections departing in the country, by speed category, 2014, by speed category, Vehicle km/1000 inhabitants < 60 km/h km/h km/h >=100 km/h EU total EU >= 60 km/h EU >= 80 km/h EU >=100 km/h CH LU DK SE AT CZ DE BE UK HU NL FR FI SK SI IT IE HR PL EE ES PT BG RO EL LV LT Rail connections departing on 02/10/2014 between 6:00 and 20:00 from any station in the country; EE, IE: 2013,EL, Northern Ireland: 2015

7 5 A WA L K TO T H E PA R K? A S S E S S I N G A C C E S S TO G R E E N A R E A S I N E U R O P E ' S C I T I E S Canarias Guadeloupe Martinique Guyane Mayotte Réunion Açores Madeira REGIOgis Average speed of direct rail connections, 2014 km/h <= > Speed calculated along straight lines representing the connection between two subsequent stops. All direct train trips between geolocated stations, starting between 6:00 and 20:00 on 02/10/2014 (EE, IE: 2013; EL, Corsica, Northern Ireland: 2015). Sources: UIC, National Transport Authority Ireland, TrainOSE Greece, Chemins de Fer de la Corse, Translink Northern Ireland Railways, EuroGeographics, OpenStreetMap, TomTom, RRG, DG REGIO 0 no data or incomplete data 500 Km EuroGeographics Association for the administrative boundaries Map 2: Average speed of direct rail connections

8 6 Canarias Guadeloupe Martinique Guyane Mayotte Réunion Açores Madeira Length by inhabitant of fast rail connections with departure in the region, 2014 vehicle km/1000 inhabitants < >= 15 no data Total euclidian length of rail connections with a speed of more than 80 km/h, departing between 6:00 and 20:00 from any station in the region, divided by regional population. EE, IE: 2013; EL, Corsica, Northern Ireland: 2015 Sources: UIC, National Transport Authority Ireland, TrainOSE Greece, Chemins de Fer de la Corse, Translink Northern Ireland Railways, EuroGeographics, OpenStreetMap, TomTom, RRG, Eurostat, DG REGIO REGIOgis Km EuroGeographics Association for the administrative boundaries Map 3: Length by inhabitant of relatively fast rail connections (> 80 km/h) with departure in the NUTS-2 region

9 A WALK TO THE PARK? ASSESSING ACCESS TO GREEN AREAS IN EUROPE'S CITIES 7 4 RAIL ACCESSIBILITY OF CITIES The combination of an EU-wide harmonised definition of cities [5] with comprehensive rail passenger timetables [6] opens new opportunities for the development of rail accessibility indicators between cities. We developed an indicator of potential accessibility [7] at the level of cities and greater cities. For each of the cities, we calculated the travel time to other cities located in a distance range within which one can expect to find a rail connection providing a travel time of maximum three hours. We chose this constraint to assess which trips might be relevant for day-time travel, as well as to limit the number of origin/ destination calculations. The travel time calculations took into account the presence of all rail passenger stations within each city. We assessed the total travel time, taking into account waiting times and including transfer times, if needed, for trips starting between 7:00 and 9:00 from each city. Within this time frame, we repeated the origin/destination calculation for every quarter of an hour. This allowed us to take into account the frequency of the available services, because waiting times will be different depending on the requested departure time. Summarising the results by combination of origin/destination cities, we first selected the destinations that can be reached within 3 hours of effective train travel. For these combinations, we calculated the average total travel time, including waiting times. These averages were then used to calculate the indicator of potential accessibility to other cities. For each of the destination cities, we took into account the total population of the urban centre [8]. The attraction to the destination cities' population was determined by the average total travel time, whereby longer travel times received less weight than shorter ones, using an exponential function. Finally, the weighted destination population figures were summed for each city of departure. Hence, the indicator is expressed as a (weighted) population figure. It can be interpreted as the total population of other cities that can be reached within a reasonable travel time, taking into account the total travel time of each trip, and limiting the destinations to those relevant for a day-time trip. Map 4 shows the results for all cities and greater cities. It reveals very substantial differences in accessibility levels throughout the European territory. A very high level of potential accessibility is found in and around the highly urbanised areas of the UK, the Netherlands, Belgium, northern France and the Rhine-Ruhr area in Germany. This is due to the combination of high population concentrations, a dense rail network, high-speed rail developments and relatively high frequencies of rail services. Relatively high accessibility ranges further to the west and east of France, substantial parts of Germany, the north of Italy and some of the bigger centres in Spain. Somewhat lower values are found in Austria and Switzerland, reflecting the limitations due to the mountainous environment. Still lower values are observed in more peripheral western parts of the EU (Ireland, Portugal and Spain) and in northern Europe, where these are due to the longer distances between cities and the relatively low population densities. In most of the eastern part of the EU, city accessibility is much weaker, which is often due to a combination of lack of frequent and/or sufficiently fast services. A closer inspection of the origin/destination results between cities shows the strengths and weaknesses of each of the links in terms of frequency and speed. Maps 5-7, focusing on selected cities, provide a more detailed picture of the connections to other cities. The speed shown on the maps is based on the actual travel time of the optimal trip available for a requested departure time between 7:00 and 9:00. The number of trips is the number of connections to the destination city, available for the same requested departure times [9]. We can use it as a proxy for service frequency. The connections from Berlin highlight the development of high-speed lines, especially towards the west. To most of the destinations around Berlin, frequent trips are available. Frequencies of trips starting from Budapest are somewhat lower, while frequent services from Warsaw seem to be rather exceptional. Most trips from Budapest to other cities operate at a moderate speed, while the speed of trips around Warsaw varies according to the destination city. Finally, Map 8 provides an overview of the optimal travel speed of trips to other cities within maximum 3 hours of travel time, with a departing time between 7:00 and 9:00. Hence, within the constraints of the requested departure time, this map shows the estimated speed of the best available trip between the cities. The substantial differences in travel speed shown on this map account for some of the variety in accessibility between cities (see Map 4). Major high-speed lines allow high-performing connections between many cities, not necessarily only those located on the high-speed lines themselves. Fast services between cities are particularly problematic around many of the Eastern European cities. In addition, many of the links across borders tend to be weak or even non-existent (at least within the maximum travel time of 3 hours). 5. Cities and greater cities according to the EC-OECD definition: see Dijkstra, L. and Poelman, H., We have taken into account all available timetable information provided by the UIC and specific national railway operators. While this collection covers the bulk of passenger services, certain specific regional or suburban services appear to be lacking. This is not problematic when assessing the overall cities accessibility throughout the European territory, but this lack may influence the accessibility levels of certain individual cities, especially smaller ones located near bigger cities. 7. For a discussion on various accessibility indicators, see Spiekermann, K., Wegener, M. e.a., Urban centre or high-density cluster: a cluster of contiguous grid cells of 1 km² with a density of at least inhabitants per km² and a minimum total population of This is the number of distinct trips given by the origin/destination calculations. As these calculations are repeated 9 times for each pair of cities, the number of distinct trips can vary between 0 and 9

10 8 As discussed before, the calculated speed of the connections can only be considered as an estimate. Speed between cities is estimated as travel time divided by the shortest line over the earth s surface, linking the centroid points of two cities [10]. Ideally, these connections should be mapped onto the actual railway network, providing additional information about vehicle speed, limitations due to physical characteristics of the infrastructure, the landscape, etc. While such an analysis could certainly be carried out in particular case studies, EU-wide datasets still require more integration to make this kind of analysis possible at the level of the whole EU. With some adaptations, the origin-destination calculations between cities can also help assess the performance of shortdistance connections between cities, especially looking at crossborder situations. For this purpose, we examined the trips between all main stations of cities located at maximum 100 km from each other. Given a uniform preferred departure time [11], we obtain the optimal travel time between each couple of these cities. Knowing the precise location of the departure and arrival stations, we can now calculate a more realistic estimate of the speed of the optimal services connecting nearby cities. These estimates cover around connections between cities. To synthesize this information, we aggregated all connections inside each country, as well as the ones connecting cities in one country to those in a neighbouring country. When averaging the connection speeds, we took into account the population size of the destination cities. City pairs without any rail connection are excluded from the calculation. Table 1 presents the results of this aggregation. Results are colour coded from red to green according to speed categories, and countries are ranked according to the average speed of domestic services, shown on the diagonal of the table. Where there are at least 10 city connections, figures are written in bold and underlined. While the diversity in speed of the connections can partly be explained by the presence of geographical obstacles (mountains, lakes, irregular coastlines, etc.) and/or infrastructure challenges (bridges, tunnels), the actual layout of the railway network and the efficiency of use of this network definitely play a major role in explaining the speed differences. Amongst the countries with more than 100 domestic city connections, average optimal speed varies from 47.3 km/h in Poland to 63.3 km/h in the Netherlands. The overall average optimal speed of all domestic services between nearby cities is 59.4 km/h, while the average for crossborder connections is only 45.8 km/h. In almost all countries, domestic services operate at higher speed than cross-border services, even when considering countries where domestic services operate at a relatively high speed: cross-border services from Germany or from the Netherlands only operate at an average speed of 42.6 km/h and 45.8 km/h respectively. Although beyond the scope of this paper, a closer inspection of cross-border links would probably show that waiting times and lack of coordination of service schedules between countries are some of the obstacles to be overcome to boost the performance of these services. It might also be relevant to take a closer look at connections between smaller centres located in border areas. 10. The cities centroid points are used as an approximation of the average location of all stations in a particular city. See the methodological annex for a more detailed discussion. 11. The origin-destination calculation provided us with the best available connection available for a preferred departure at 7:30.

11 A WALK TO THE PARK? ASSESSING ACCESS TO GREEN AREAS IN EUROPE'S CITIES 9 Table 1: Average optimal speed of rail connections between main stations in cities located at maximum 100 km from each other, by departure/arrival country Arrival country Departure country FI SE BE HU LT CH SK EL NL UK DE FR IT DK ES PL IE PT CZ RO HR BG AT LU SI FI SE BE HU LT 71.0 CH SK , EL 68.0 NL UK DE FR , IT DK ES PL IE 44.9 PT CZ RO HR BG AT LU SI

12 10 Canarias Guadeloupe Martinique Guyane Mayotte Réunion Açores Madeira Potential rail accessibility to other cities; by city, 2014 Population < >= No data Urban centre population < >= REGIOgis Potential accessibility to cities/greater cities that can be reached within 3 hours (fastest connection available). Excluding the population of the city/greater city of origin. Sources: UIC, National Transport Authority Ireland, TrainOSE Greece, Chemins de Fer de la Corse, Translink Northern Ireland Railways, EuroGeographics, OpenStreetMap, TomTom, RRG, DG REGIO Km EuroGeographics Association for the administrative boundaries Map 4: Potential rail accessibility to other cities and greater cities

13 A WALK TO THE PARK? ASSESSING ACCESS TO GREEN AREAS IN EUROPE'S CITIES 11 Olsztyn Białystok Neumünster Rostock Greifswald Lübeck Schwerin Toruń Hamburg Neubrandenburg Szczecin Lüneburg Stargard Włocławek Bremen Szczeciński Gorzów Płock Warszawa Siedlce Brandenburg Wielkopolski Poznań Celle an der Berlin Frankfurt Poznań Konin Hannover Havel Potsdam (Oder) Wolfsburg Zgierz Magdeburg Łódź Bielefeld Braunschweig Tomaszów Pabianice Hildesheim Salzgitter Dessau-Roßlau Cottbus Mazowiecki Radom Lublin Halle an Piotrków Göttingen der Saale Leipzig Trybunalski Kassel Görlitz Dresden Erfurt Gera Chemnitz Weimar Jena Zwickau km km Kraków Olomouc Number of trips =< Nitra Wien Bratislava Szombathely Gyõr km Pécs Budapest Székesfehérvár Kecskemét Miskolc Nyíregyháza Szeged Debrecen km/h > 6 <= > Cities Railway lines Maps 5-7: Connections from Berlin, Warsaw and Budapest to other cities, with an optimal travel time of less than 3 hours: speed of the optimal trip and number of trips available for requested departure times between 7:00 and 9:00

14 12 Canarias Guadeloupe Martinique Guyane Mayotte Réunion Açores Madeira REGIOgis Rail services between cities, 2014 km/h <= 40.0 Urban centre population < > >= Optimal speed of trips to other cities within maximum 3 hours travel, departing between 7:00 and 9:00. EE, IE: 2013; Northern Ireland: Sources: UIC, National Transport Authority Ireland, TrainOSE Greece, Translink Northern Ireland Railways, EuroGeographics, OpenStreetMap, TomTom, RRG, DG REGIO Km EuroGeographics Association for the administrative boundaries Map 8: Optimal speed of rail services to other cities within maximum 3 hours travel

15 A WALK TO THE PARK? ASSESSING ACCESS TO GREEN AREAS IN EUROPE'S CITIES 13 5 CONCLUSION The EU-wide analysis of passenger rail timetables has opened up new opportunities for the development of harmonised metrics on service speed and frequency throughout the territory. The results highlight the extreme diversity in terms of service performance throughout Europe. A special focus on cities and regions allows an improved assessment of rail services challenges and opportunities at a detailed spatial level. The analysis model inevitably contains some simplifications, due to the still limited availability and integration of relevant (spatial) data. Interesting new opportunities for an improved analysis are expected to occur once better-integrated data on railway infrastructure and network use becomes available. 6 METHODOLOGICAL ANNEX 6.1 DATA ON STATION LOCATIONS AND TIMETABLES Currently, no single integrated, accessible and open data source exists for rail timetables and station locations in Europe. UIC has provided MERITS datasets, including a list of European railway stations and rail timetables (for the year 2014), for internal analytical use by DG Regional and Urban Policy. Substantial transformation and selection work was needed to prepare datasets that were fit for our purposes. The aim of this process is to create a georeferenced dataset of station locations and a standardised set of tables containing rail timetables, compliant with the GTFS data model [12]. The data preparation process also involved the use of several additional data sources. 6.2 STATION LOCATIONS The MERITS stations dataset contained items, with a unique identifier for each station, its name and country code. A thematic classification is also provided (railway station, border crossing point, etc.), but almost all items are currently coded as stations. The file is designed to contain the latitude and longitude of the station locations, but these coordinates are missing for almost one third of the items ( cases). Coverage of the location data is unevenly spread over Europe. Major gaps in the location data were found in Bulgaria, Spain, France, Croatia, Poland, Portugal, Romania, Slovenia, Finland and the UK. Before attempting to enrich the location data by retrieving station locations from other sources, we checked whether all items in the MERITS station dataset are actually active railway stations. From the MERITS timetable datasets, we retrieved all unique station identifiers. This provides us with a complete list of stations for which there are timetables available for This list reveals stations. Of these, only can be found in the stations dataset. For about stations, there are timetables available, but there is no information at all about their name or precise location. Most of these "missing" stations are located in Russia or Ukraine. As both countries are out of the scope of our analysis, we did not investigate this issue further. The timetable data contain information about the service mode (train, bus, ferry). Most of the timetables are flagged as train timetables. A closer look at the "service brand" information in the MERITS data reveals that timetables classified as train trips are actually (replacement) bus trips. When we keep only those station items for which actual rail timetables have been provided, we have a total of stations, of which still more than one third ( cases) are without latitude/longitude coordinates. These cases needed to be solved to allow a geographical analysis of the timetable data. To enhance the station location information, we examined various additional data sources. Each of these contains point locations of stations, station names and country codes. The UIC station items where coordinates were missing were joined to the point location records using their country code and station name [13]. Where we found a match, the point coordinates of the auxiliary data source were used to geocode the UIC station. This process has been applied using the following datasets (in the order specified): EuroGeographics EuroRegionalMap railway station points France: SNCF TER stations list and coordinates (this list contains a station code compatible with the UIC station code) [14] UK: NapTAN public transport stops file, including coordinates [15] OpenStreetMap station points TomTom Multinet station locations. All these datasets have been converted into ETRS89 Lambert Azimuthal Equal Area projections, [16] with latitude/longitude values stored in metres. The completed MERITS station list that includes the added coordinates has been converted to a point layer. As a result, coordinates for rail station points for which timetables are available have been added, leaving stations with missing coordinates. These data gaps were mostly concentrated in Bulgaria, Croatia, Poland and Romania. 12. For a description of the GTFS specification, see: Before applying this join, it was verified that combinations of country code and station name in the joined point features are unique EPSG:3035

16 14 Most of the remaining missing coordinates have been identified by using additional external data sources [17] and, finally, due to some manual verifications (combining various internet-based maps and web research). Station locations will be used in origin/destination calculations based on timetables. As the timetables contain arrival and departure times in local time, stations need to be flagged with the time zone in which they are located. In principle, the MERITS location data contain a time zone item, but some doubts persist regarding the validity of this information, especially in some Eastern European areas. Hence, we have created an item "time zone difference", containing the difference in hours between Central European summer time (CEST) and the local time in the station [18]. For some rail connections, the subsequent analysis of the direct train trips has resulted in some impossibly low or high speeds. These anomalies indicated problems with the positional accuracy of the station locations. Most of the anomalies were found in the original coordinates of the MERITS data for Hungary. By comparing the MERITS station locations with other sources (especially EuroRegionalMap and OpenStreetMap), we were able to correct most of these anomalies. 6.3 TRANSFORMATION OF TIMETABLE DATA The UIC MERITS datasets are provided according to the EDIFACT standard. While this guarantees a structured organisation of the data, this format is not readily useable for our analysis. We will use tools designed for using timetable data in GTFS format. Hence, we processed the MERITS data to create tables according to the GTFS data specification. The basic unit of timetable reporting in the MERITS data is a trip. Each of the trips has an identifier, a service provider, a schedule listing the departure and arrival stops and the related times, and information about the period of operation. Part of this information is needed to populate the GTFS tables for "trips", "stop_times"," calendar" and "calendar_dates". First, we need to determine a unique identifier for each trip combined with a period of operation. This is necessary to be able to correctly list the days of operation related to that trip. In the MERITS data, there is no obvious relationship between a trip and the route it is serving. In other words, each of the trips is independent; the GTFS notion of a "service" or a "route" is not explicit in the MERITS model. The MERITS data model includes ways to encode schedules of trains that are merged or split during the trip. While converting the timetables into a GFTS structure, it is not possible to identify uniquely the split or merged trips. Hence, the schedules of the common part of a merged/split trip will occur twice in the GTFS output data. While this is not problematic when assessing origin/destination travel times, this issue should be taken into account when assessing the frequency of direct connections. The double counting of parts of trips will be avoided by aggregating the direct connections by departure and arrival stop_id and by departure and arrival time. The MERITS trips can also contain technical stops, not available for passenger departures or arrivals. These stops are removed from the sequence of stops when converting into the GTFS "stop_times" table. A transport mode is assigned to each MERITS trip, but it appears that this transport mode (train by default) is not always correct. Using the information from the "service brand" item, a number of bus trips can be identified and re-coded as such, in order to exclude these from the subsequent analysis. The MERITS data covered all trips of (almost) the entire year As our analysis was limited to a single day, we limited the content of the converted GFTS tables to only those trips and stop times valid on the selected day, in order not to burden the GTFS tables with superfluous content. This selection allowed the number of trips to be reduced from to and the number of stop times from 7 million to 1.7 million. 6.4 ADDITIONAL DATASETS While the MERITS datasets cover most of the EU countries and Switzerland, some regions and countries were missing. We completed the information in various ways. For Estonia and Ireland, we retrieved published GTFS datasets, covering all public transport in both countries [19]. For the remaining areas missing in the MERITS data (Greece, Corsica, Northern Ireland), we retrieved PDF timetable data from the railway operators' websites [20] and stored the information in GFTS tables. We georeferenced the corresponding station locations using EuroGeographics' EuroRegionalMap and OpenStreetMap. For each of these specific countries and regions, we used the schedules active on an ordinary weekday, but due to data availability issues, the actual day chosen was different in each of the additional datasets [21]. 6.5 ORIGIN/DESTINATION CALCULATIONS BETWEEN CITIES The assessment of accessibility of cities and greater cities relies upon origin/destination calculations through the entire rail network. While the available data in principle allow the calculation of travel time between all possible pairs of stations (hence also between all possible pairs of cities), a simplified approach was necessary due to limitations in the available IT infrastructure. 17. Analysed by Büro für Raumforschung, Raumplanung und Geoinformation (RRG). 18. This adjustment is intended to be valid for the duration of daylight saving time because some countries do not follow a daylight saving time change (Belarus, Russia). 19. Estonia: retrieved February Ireland: National Transport Authority, retrieved October 2013 from by-agency/nta.php 20. Greece: TRAINOSE retrieved August 2015; Corsica: Chemins de Fer de la Corse retrieved September 2015; Northern Ireland: Translink NI Railways retrieved June All reference dates are within the period

17 A WALK TO THE PARK? ASSESSING ACCESS TO GREEN AREAS IN EUROPE'S CITIES 15 First, we overlaid the station locations with the polygons of all cities and greater cities to establish the link between station codes and city/greater city codes. Second, for every city we reduced the number of relevant city destinations. Based on a preliminary analysis of the direct trips between cities, for every city we determined the maximum Euclidian distance that can be reached by an optimal direct rail trip of maximum 3 hours. Extrapolated to the distance covered by a theoretical rail trip of exactly 3 hours, this gave us a first indication of a reasonable search radius around each city. We extended this search radius by 25% and listed the cities inside this catchment area. This resulted in a list of origin/destination pairs of cities to be examined. For the origin/destination calculations, we used the open source platform OpenTripPlanner [22]. This platform allows trip calculations through a network based on GTFS timetables. From the overlay of stations with city areas, we created a clustered version of the GTFS timetables, whereby all stations located in the same city received the same station code. Consequently, OpenTripPlanner considered this station cluster as a single origin or destination, and the calculations provided us with optimal trips and travel times between cities, regardless of the specific origin or destination station inside each of these cities. Using this approach, we lost the details on the variety of specific connections between individual stations but substantially reduced the number of origin/destination calculations required. Within the time frame between 7:00 and 9:00, trips between all selected origin/destination pairs were calculated using each quarter of an hour as the preferred departure time. This meant that each of the O/D calculations was repeated 9 times and resulted in a variety of effective trip times and total travel times (including waiting time before departure). The individual origin/destination calculations provide us with the requested departure time, the effective train departure and arrival times and the number of transfers during the trip. We can summarise the calculations by origin/destination pair, calculating the average total travel time, the minimum effective trip time and the number of distinct trips found [23]. For the subsequent analysis, we filtered these results by taking only the connections where the minimum effective trip time is less than 3 hours. To each of the connections, we linked the population figure of the urban centre of the destination city. This population figure was then weighted according to the average total travel time from the origin, using an exponential function: Where: P * e -βt P = the population of the urban centre of the destination city T = the average total travel time between the two cities Β = the exponent for the inverse distance weighting = 0.5 weight hours Graph 2: Function weighting the destination population according to travel time Finally, the potential accessibility to other cities was the sum of the weighted populations of the destination cities. The results of the origin/destination calculations can also be used to assess travel speed between cities. Due to the clustering of stations inside each of the cities, we ignored the precise location of the start and endpoint of the trips linking the cities. For this reason, each of the cities was represented by its population-weighted centroid [24]. The distance between cities was determined by the geodesic distance between the centroid points. In the case of cities located close to each other, and especially when stations are located relatively far away from the city centroid, this approach distorts the speed estimates. In order to circumvent this problem, more refined origin/destinations are needed, between individual stations of each of the cities. Unfortunately, this requires an unmanageable number of calculations. Hence, we limited the calculations to connections between major stations of cities located maximum 100 km from each other. As "major stations", we selected stations located on the territory of a city/greater city, meeting at least one of the following conditions: The only station in the city Any station with more than 150 departures between 6:00 and 20:00 Any station with more than the city average number of departures between 6:00 and 20:00. For each of these stations, we requested an origin-destination calculation to all other main stations located in other cities or greater cities, with 7:30 as the preferred departure time. From these results, we selected the shortest trip time by pair of cities, while keeping the identifiers (stop_id) of the departure and arrival stations. This allowed us to calculate a more realistic speed estimate, by dividing the length of the connection between the stations by the duration of the optimal trip between two cities. These speed estimates can be further aggregated by country, and/or by distinguishing cross-border trips or trips inside a country. This is done by calculating the average speed of the trips between cities, weighted by the population of the urban centre of the destination city As the O/D calculation is repeated 9 times, the number of distinct trips varies between 0 and Calculated on the basis of the population distribution at the level of 1 km² grid cells

18 16 7 References Dijkstra, L. and Poelman, H., 2012, Cities in Europe, the new OECD-EC definition, European Commission, Brussels ( ec.europa.eu/regional_policy/sources/docgener/focus/2012_01_ city.pdf) Spiekermann, K., Wegener, M. e.a., 2015, Transport Accessibility at Regional/Local Scale and Patterns in Europe. Applied Research project 2013/1/10 final report volume 2, ESPON, Luxembourg ( Documents/Projects/AppliedResearch/TRACC/FR/TRACC_FR_ Volume2_ScientificReport.pdf)

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