Public transport in the “15-minute city”

Máté Mizsák1, Zsófia Zádor2, Bence Kovács1, Gergő Pintér3, Balázs Lengyel1,3

1 ELTE Center for Economic and Regional Sciences
2 Northeastern University London
3 Corvinus University of Budapest

29 January 2026 / GeoInno / Budapest

motivation

  • 15-minute city is concept in urban planning
  • essential services should be available within a 15-minute walking distance
    • (Moreno et al., 2021)
  • although, this model can risk segregating poorer neighborhoods
    • (Abbiasov et al., 2022)
  • some amenities can foster social mixing
    • (Juhász et al., 2023)

what about public transport in the “15-minute city”?

15-minute by walking

  • 15-minute city usually considers walking distances
  • bicylce, other micromobility

research question

How does the integration of public transportation
within an extended 15-minute city framework
influence social mixing and access to amenities?

General Transit Feed Specification (GTFS)

  • a standard format for public transportation schedules, and associated geographic information
  • BKK also provides GTFS as open data
  • available for many cities
    • we use it for Budapest, Helsinki, and Madrid
    • to build a transportation network
directions by Google Maps

building a transportation network

Fővám square
Fővám square – one logical stop

routing model

limitation: static (3 min) transfer time

comparing walking and multimodal 15-minute accessible areas

  • 15 minute by walking (purple) or
  • 10 minute public transport and
    5 minute walking (blue)
    • because people usually don’t want to go to a bus/tram/etc stop

shape of the public transport accessibility areas

Fővám square: ellipticity = 0.06
509th street: ellipticity = 0.61
stops colored by the ellipticity of their accessibility areas

ellipticity of the public transport accessibility polygons

Budapest
Helsinki
Madrid

let’s focus on the amenities

  • does public transport provide access to more amenities?
  • we use amenities/POIs from OpenStreetMap
    • sorted into 9 essential amenity categories (Abbiasov et al., 2022)
Δ in Amenities = Multimodal - Walking

public transport supports amenity diversity

elliptic shape decreases access to amenities

SES: all low high

Does public transport provide
more opportunities for social mixing?

does public transport provide more opportunities for social mixing?

the larger accessibility area provide opportunity to meet people
with more diverse background

What is the Gini coefficient?

Perfect Equality (0)
For social-mixing, perfect equality means that only similar people of the same socio-economic background are in a location
Perfect Inequality (1)
For social-mixing, perfect inequality means the possibility for people from different socio-economic backgrounds to meet
Δ in Gini = Multimodal - Walking

social mixing measured by inequality

Budapest

  • Gini measured by: residential house prices
  • experienced Gini
    • from a TPDD
    • income in each decile
    • number of people in low, mid and high income categories

Helsinki

  • Gini measured by:
    • income in each income decile
    • number of people in low, mid and high income categories in each census tract

Madrid

  • Gini provided on the census tract level by the Instituto Nacional de Estadística

public transport provides potential
for social-mixing

SES: all low high

controlling for distance from the city center

complex amenity portfolio:
diverse and ubiquitous amenities

based on “Amenity complexity and urban locations of socio-economic mixing” (Juhász et al., 2023)

controlling for distance from the city center

ellipticity is not always a bad thing it helps on the outskirts

takeaway

  • including public transport and a multimodal transport into the 15-minute city concept can help reduce the experienced segregation
    • compared to the 15-minute walking area
  • urban peripheries benefit from public transport for social-mixing,
    • even when public transport follows avenues for quick access to the center,
    • however this shape negatively impacts access to amenities of those living far from the center

thank you for the attention!

Máté Mizsák, Zsófia Zádor, Bence Kovács, Gergő Pintér, Balázs Lengyel

contact: gergo.pinter @ uni-corvinus.hu, @pintergreg

“Urban Mobility Data Mining and Big Data Analysis”
Special Issue in Urban Science (IF: 2.9, Q1)
submission deadline: 30 September 2026

references

Abbiasov, T., Heine, C., Glaeser, E. L., Ratti, C., Sabouri, S., Miranda, A. S., & Santi, P. (2022). The 15-minute city quantified using mobility data. National Bureau of Economic Research.
Juhász, S., Pintér, G., Kovács, Á. J., Borza, E., Mónus, G., Lőrincz, L., & Lengyel, B. (2023). Amenity complexity and urban locations of socio-economic mixing. EPJ Data Science, 12(1), 34.
Moreno, C., Allam, Z., Chabaud, D., Gall, C., & Pratlong, F. (2021). Introducing the “15-minute city”: Sustainability, resilience and place identity in future post-pandemic cities. Smart Cities, 4(1), 93–111.