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The annual Transport Practitioners’ Meeting brings together a diverse mix of research and practical experience, highlighting innovation from local authorities, consultants, operators and other transport providers.
At this year’s event, held on 15–16 July, Padam Mobility’s Max McDonald and independent researcher Beate Kubitz presented their paper exploring how public transport provision can be compared across national borders.
Comparing public transport provision between countries is not straightforward. Different institutional structures, service models and data systems can make like-for-like comparisons difficult.
The paper examines how timetable data published in General Transit Feed Specification – or GTFS – format can provide a common basis for comparison. GTFS data makes it possible to visualise public transport provision geographically, including the location of stops and the frequency of services.
However, simply counting stops does not necessarily provide an accurate picture of the level of service available. The researchers therefore also counted how frequently buses call at each stop, creating an overall service-frequency metric for the network within a defined area.
The method was applied to Swindon in the UK and Orléans in France.
The comparison found that Swindon had more bus stops per square kilometre. However, those stops were served less frequently each day than the stops in Orléans. This illustrates why stop density alone is not sufficient to assess the strength or accessibility of a public transport network.
In Orléans, the fixed-route network is also complemented by virtual stops used by Demand-Responsive Transport. When these virtual DRT stops are included, the overall density of public transport access points across the metropolitan area increases further.
The findings demonstrate how standardised timetable data can reveal differences that might otherwise remain hidden. They also raise a broader question: should transport provision be assessed according to the number of physical stops, the frequency of services, the geographical coverage of the network or a combination of all three?
Read the full Case Study here
The presentation prompted several questions about the selection of the two areas, the integration of DRT in Orléans and the underlying data.
Swindon was selected because it shares several characteristics with Orléans. The two areas have broadly comparable population sizes and are located at a similar distance from their respective capitals, giving both the potential to form part of a long-distance commuting zone.
There are also some similarities in their economic profiles, including a well-developed industrial base. These characteristics provided a useful foundation for comparing the organisation and availability of public transport.
The DRT service is integrated into the Orléans metropolitan area’s wider mobility offer. Passengers can use the local MaaS application to plan journeys and purchase tickets covering both fixed-route public transport and DRT services.
Keolis operates the services as part of a single integrated transport network procured by Orléans Métropole. This makes it possible to present DRT as part of the overall public transport offer rather than as a separate or standalone service.
NaPTAN is the UK’s national system for identifying public transport access points. However, this study needed a data format that could be applied consistently across different countries.
GTFS specifies a common format for public transport information, including geographical data such as the latitude and longitude of stops. Because GTFS is used internationally, it provides a suitable basis for comparing networks across national borders.
The comparison between Swindon and Orléans shows that measuring transport provision requires more than counting stops on a map. Service frequency, geographical coverage and the presence of flexible transport options all affect the level of mobility that a network can provide.
As public transport increasingly combines fixed routes with flexible and on-demand services, datasets and evaluation methods will also need to reflect this changing reality. GTFS data offers a valuable starting point for making these differences visible and creating more meaningful comparisons between transport networks.

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