Barriers of Urban Mobility

Gergő Pintér 1,3 and Balázs Lengyel 1,2,3

1 ANETI Lab, Corvinus University of Budapest
2 ANETI Lab, ELTE Center for Economic and Regional Sciences
3 Institute for Data Analytics and Information Systems, Corvinus University of Budapest

30 January 2026 / GeoInno/ Budapest

motivation

Nagykőrösi road, Budapest by vst via Mapillary CC BY-SA 4.0

amenities enter the equation

  • complex amenities foster social mixing (Juhász et al., 2023)
    • attract people with diverse background
    • complex as in economic complexity
  • workplaces
  • crossroads

motivation

A wildlife overpass built over Highway 38 in Israel by Hagai Agmon-Snir | CC BY-SA 4.0

mobile positioning data

  • collected from various, unspecified smartphone apps
    • timestamp, user ID, location
    • GPS-based location
  • pings are clustered into stops (Juhász et al., 2023)
    • using Infostop algorithm (Aslak & Alessandretti, 2020)
    • where some time was spent

building a network

the blocks are considered nodes
connected if a user had consecutive stops in two blocks within a day

we’ve published the networks along with the paper (Pintér, 2025)

community detection

  • using the network built from the stops
  • Louvain community detection is applied
    • with different resolution values
    • executed 10 times for each resolution
Louvain communities (resolution 2.5)

Louvain community detection - resolution 2.5

infrastructural barriers: primary and secondary (dotted) roads
administrative boundaries: districts and neighborhood (dotted)

district 15

barrier crossing ratio

BCRγbarrier=1n∑mCB∑mCB×CCγ BCR_{\gamma}^{barrier} = \dfrac{1}{n} \frac{ \sum_{m} \text{{CB}} }{ \sum_{m} \text{{CB}} \times \text{{CC}}_{\gamma} }

  • m is the number of mobility edges
  • γ\gamma is the resolution
  • n is the number of iterations at resolution γ\gamma

by barrier types:

  • district
  • neighborhood
  • primary roads
  • secondary
  • railways
  • river

BCR by barrier types

same trip, same blocks, different communities
  • small resolution -> large communities -> fewer community crossing
  • however, the barrier crossing remains the same, so BCR gets smaller

classify users based on home location

trips within Budapest are considered

but the classification is not restricted to Budapest

decomposing barrier crossing ratio

decomposing barrier crossing ratio

back to the amenities

complex amenity portfolio:
diverse and ubiquitous amenities

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

back to the amenities

complex amenity portfolio:
diverse and ubiquitous amenities

takeaway

Not just the physical barriers, but the administrative boundaries also limit urban mobility.

The hierarchy of urban barriers are reflected by the communities of the mobility network.

Urban barriers have significantly different effects by people’s home location and the amenity complexity of destinations.

thanks for the attention!

Gergő Pintér, gergo.pinter @ uni-corvinus.hu, @pintergreg

this presentation is available online: pintergreg.github.io/geoinno2026

already published in
Cities 167, 106322 (2025)

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

references

Aslak, U., & Alessandretti, L. (2020). Infostop: Scalable stop-location detection in multi-user mobility data. https://arxiv.org/abs/2003.14370
Janosov, M., Szigeti, P., & Jablonszky, G. (2021). A tömegközlekedés a vártnál kevésbé sínyli meg a Lánchíd lezárását, a gyalogosok annál inkább). https://qubit.hu/2021/07/07/a-tomegkozlekedes-a-vartnal-kevesbe-sinyli-meg-a-lanchid-lezarasat-a-gyalogosok-annal-inkabb
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.
Pintér, G. (2024). Revealing urban area from mobile positioning data. Scientific Reports, 14(1), 30948.
Pintér, G. (2025). Code and data for quantifying barriers of urban mobility (Version v0.1.0). Zenodo. https://doi.org/10.5281/zenodo.14869889
Yabe, T., Tsubouchi, K., Shimizu, T., Sekimoto, Y., Sezaki, K., Moro, E., & Pentland, A. (2024). YJMob100K: City-scale and longitudinal dataset of anonymized human mobility trajectories. Scientific Data, 11(1), 397.

closing Liberty Bridge

Budapest downtown
only downtown
city level

this result aligns with (Janosov et al., 2021)

Nagoya metropolitan area

municipality boundaries wards (dotted)
higher order roads

open data (YJMob100K): (Yabe et al., 2024) | geopositioning the data: (Pintér, 2024)

BCR × Nagoya

stop distribution of agglomeration dwellers