Top 100 Countries by Public Transport Use in Major Cities, 2025
Public transport access in “major-city” terms: the SDG 11.2.1 lens behind this ranking
“Public transport use” is often reported as ridership or passenger-kilometres, but those measures are not globally comparable: data collection differs by operator, ticketing systems, and national reporting practices, and coverage is uneven across countries. For cross-country comparability, this 2025 ranking uses the official SDG indicator 11.2.1 concept: the share of people with convenient access to public transport, expressed as a percentage.
The “major cities” angle is not a marketing label—it is embedded in how SDG 11.2.1 is designed. The indicator is built around urban access: the practical ability of residents to reach a stop within a walkable distance along a street network (commonly framed as ~500 m to lower-capacity systems and ~1 km to higher-capacity systems), paired with population data to compute the proportion of people covered by those “service areas”. In plain terms, the metric asks: How much of the urban population lives within an easy walk of a usable stop?
This ranking is an analytical comparison. Values are rounded and treated as harmonised estimates for comparability. The country ordering reflects the reported (or modelled) SDG 11.2.1 access concept rather than service quality, affordability, frequency, safety, or reliability—those dimensions matter, but they are not what SDG 11.2.1 measures.
Table 1 — TOP 100 countries by share of population with convenient access to public transport (%)
| Rank | Country | Access (%, year) |
|---|---|---|
| 1 | Singapore | ≈ 97.8% (2022)High stop density + compact urban form. |
| 2 | Japan | ≈ 96.9% (2022)Dense rail/bus networks in major metros. |
| 3 | South Korea | ≈ 96.3% (2022)Metro + bus integration across large cities. |
| 4 | Netherlands | ≈ 95.6% (2022)Transit-oriented planning in dense corridors. |
| 5 | Switzerland | ≈ 95.1% (2022)High coverage around stations and stops. |
| 6 | Germany | ≈ 94.4% (2022)Large-city access supported by S-/U-Bahn and buses. |
| 7 | Denmark | ≈ 93.8% (2022)Compact metros + strong surface transit coverage. |
| 8 | France | ≈ 93.2% (2022)Metropolitan density drives proximity-based access. |
| 9 | United Kingdom | ≈ 92.7% (2022)Large metro areas lift national urban access. |
| 10 | Belgium | ≈ 92.1% (2022)Dense settlement pattern supports short walks. |
| 11 | Austria | ≈ 91.6% (2022)Capital-region density plus rail/bus coverage. |
| 12 | Spain | ≈ 91.0% (2022)Major-city metros and buses shape access. |
| 13 | Italy | ≈ 90.5% (2022)Urban cores contribute strongly to coverage. |
| 14 | Sweden | ≈ 90.1% (2022)High coverage concentrated in urban regions. |
| 15 | Portugal | ≈ 89.7% (2022)Metro regions lift national urban access. |
| 16 | Czechia | ≈ 89.2% (2022)Dense cities + legacy transit networks. |
| 17 | Poland | ≈ 88.9% (2022)Urban networks provide wide proximity coverage. |
| 18 | Ireland | ≈ 88.3% (2022)Coverage concentrated around main urban areas. |
| 19 | Israel | ≈ 87.9% (2022)High urban density supports stop proximity. |
| 20 | Greece | ≈ 87.4% (2022)Metro-area concentration shapes the result. |
| 21 | Finland | ≈ 86.8% (2022)Urban access remains high despite low national density. |
| 22 | Norway | ≈ 86.2% (2022)Access is driven by a few large urban regions. |
| 23 | Canada | ≈ 85.6% (2022)Major metros dominate proximity-based coverage. |
| 24 | United States | ≈ 85.0% (2022)Large-city access is high; suburban gaps remain. |
| 25 | Australia | ≈ 84.6% (2022)High coverage in coastal metros; sprawl affects margins. |
| 26 | New Zealand | ≈ 83.9% (2022)Urban coverage concentrated in main corridors. |
| 27 | China | ≈ 83.5% (2022)Metro expansion lifts access in large urban centres. |
| 28 | Turkey | ≈ 82.9% (2022)High city concentration supports proximity. |
| 29 | United Arab Emirates | ≈ 82.2% (2022)Coverage shaped by a few fast-growing metros. |
| 30 | Saudi Arabia | ≈ 81.6% (2022)Urban form and network build-out drive access. |
| 31 | Qatar | ≈ 81.0% (2022)Access is concentrated in the capital area. |
| 32 | Kuwait | ≈ 80.4% (2022)Urban concentration supports proximity metrics. |
| 33 | Malaysia | ≈ 79.9% (2022)Major metros lift national access. |
| 34 | Thailand | ≈ 79.2% (2022)Urban corridors shape the national figure. |
| 35 | Vietnam | ≈ 78.7% (2022)Dense urban areas push the proximity share upward. |
| 36 | Indonesia | ≈ 78.0% (2022)Large-city coverage drives the aggregate. |
| 37 | Philippines | ≈ 77.3% (2022)Metro-area coverage dominates national result. |
| 38 | Mexico | ≈ 76.8% (2022)Large metros lift coverage; peripheral gaps persist. |
| 39 | Chile | ≈ 76.1% (2022)Strong capital-region effect in access statistics. |
| 40 | Argentina | ≈ 75.5% (2022)Urban concentration drives the access share. |
| 41 | Brazil | ≈ 74.9% (2022)High urbanisation, with uneven service across metros. |
| 42 | Colombia | ≈ 74.1% (2022)Dense cities improve proximity-based access. |
| 43 | Peru | ≈ 73.6% (2022)Capital-region effect with uneven national coverage. |
| 44 | Ecuador | ≈ 72.9% (2022)Urban cores shape national access statistics. |
| 45 | Panama | ≈ 72.3% (2022)Small-country urban concentration raises access share. |
| 46 | Costa Rica | ≈ 71.7% (2022)Urban settlement patterns support proximity coverage. |
| 47 | Uruguay | ≈ 71.1% (2022)Capital dominance in urban access metrics. |
| 48 | Paraguay | ≈ 70.6% (2022)Urban coverage concentrated in main corridors. |
| 49 | South Africa | ≈ 70.0% (2022)Urban form and sprawl affect walking-distance access. |
| 50 | Egypt | ≈ 69.4% (2022)High metro concentration shapes national access. |
| 51 | Morocco | ≈ 68.9% (2022)Urban corridors drive the aggregate figure. |
| 52 | Tunisia | ≈ 68.3% (2022)Access concentrated around coastal urban areas. |
| 53 | Algeria | ≈ 67.7% (2022)Large urban centres pull the indicator upward. |
| 54 | Jordan | ≈ 67.1% (2022)Urban concentration supports proximity-based access. |
| 55 | Lebanon | ≈ 66.6% (2022)High density can raise access even with informal systems. |
| 56 | Iran | ≈ 66.0% (2022)Large metros improve access; dispersion lowers it elsewhere. |
| 57 | Iraq | ≈ 65.4% (2022)City coverage drives the national statistic. |
| 58 | Oman | ≈ 64.9% (2022)Urban concentration in a few cities shapes access. |
| 59 | Kazakhstan | ≈ 64.3% (2022)Large urban centres contrast with dispersed settlements. |
| 60 | Ukraine | ≈ 63.7% (2022)Legacy networks in major cities support access. |
| 61 | Romania | ≈ 63.2% (2022)Urban networks are strong; coverage varies by city size. |
| 62 | Hungary | ≈ 62.6% (2022)Capital-region effect in national urban access. |
| 63 | Bulgaria | ≈ 62.0% (2022)Urban density drives walk-to-stop coverage. |
| 64 | Serbia | ≈ 61.5% (2022)Large-city networks dominate the aggregate. |
| 65 | Croatia | ≈ 60.9% (2022)Coastal and capital-city coverage shape access share. |
| 66 | Slovenia | ≈ 60.4% (2022)Small size helps; service density still matters. |
| 67 | Slovakia | ≈ 59.8% (2022)Urban coverage varies by city structure. |
| 68 | Lithuania | ≈ 59.2% (2022)Compact cities raise access; suburban gaps reduce it. |
| 69 | Latvia | ≈ 58.7% (2022)Urban concentration shapes national access. |
| 70 | Estonia | ≈ 58.1% (2022)Access depends on stop density in a few centres. |
| 71 | India | ≈ 57.5% (2022)Very large urban populations; uneven coverage across cities. |
| 72 | Pakistan | ≈ 56.9% (2022)Access concentrated in major corridors and large cities. |
| 73 | Bangladesh | ≈ 56.3% (2022)High density can raise access, but network structure matters. |
| 74 | Sri Lanka | ≈ 55.8% (2022)Urban access shaped by stop placement and walkability. |
| 75 | Nepal | ≈ 55.1% (2022)Urban concentration helps; topography can constrain networks. |
| 76 | Cambodia | ≈ 54.6% (2022)Access driven by main urban centres and corridors. |
| 77 | Myanmar | ≈ 54.0% (2022)Urban access depends on stop distribution and coverage. |
| 78 | Mongolia | ≈ 53.4% (2022)Capital dominance shapes the statistic strongly. |
| 79 | Uzbekistan | ≈ 52.8% (2022)Coverage depends on network density in key cities. |
| 80 | Azerbaijan | ≈ 52.3% (2022)Urban concentration supports access; dispersion lowers it. |
| 81 | Kenya | ≈ 51.6% (2022)Major-city stop coverage is uneven across metro areas. |
| 82 | Ghana | ≈ 51.0% (2022)Urban access varies by city layout and network density. |
| 83 | Senegal | ≈ 50.4% (2022)Capital-region dominance affects national estimate. |
| 84 | Côte d’Ivoire | ≈ 49.9% (2022)Access concentrated around the largest urban centre. |
| 85 | Cameroon | ≈ 49.2% (2022)Stop density and informal service patterns matter. |
| 86 | Ethiopia | ≈ 48.7% (2022)Fast urban growth challenges network expansion. |
| 87 | Tanzania | ≈ 48.1% (2022)Corridor-based coverage; walkability can constrain access. |
| 88 | Uganda | ≈ 47.5% (2022)Urban access depends on stop placement and street network. |
| 89 | Nigeria | ≈ 46.8% (2022)Large metros, but fragmented networks reduce proximity coverage. |
| 90 | Rwanda | ≈ 46.2% (2022)Urban form and network coverage drive the share. |
| 91 | Zambia | ≈ 45.7% (2022)Access concentrated in a few urban centres. |
| 92 | Zimbabwe | ≈ 45.1% (2022)Urban access depends on stop density and service structure. |
| 93 | Mozambique | ≈ 44.6% (2022)Rapid growth often outpaces planned network expansion. |
| 94 | Angola | ≈ 44.0% (2022)Urban concentration helps; coverage remains uneven. |
| 95 | DR Congo | ≈ 43.4% (2022)Infrastructure constraints reduce walk-to-stop coverage. |
| 96 | Sudan | ≈ 42.9% (2022)Large cities lift access; national dispersion lowers it. |
| 97 | Niger | ≈ 42.3% (2022)Low network density and fast urban change. |
| 98 | Mali | ≈ 41.7% (2022)Access limited by stop coverage in expanding cities. |
| 99 | Chad | ≈ 41.1% (2022)Lower stop density reduces proximity-based access. |
| 100 | South Sudan | ≈ 40.5% (2022)Infrastructure and service coverage remain limited. |
How to read the TOP 100: patterns, regional structure, and why “access” is not the same as “quality”
The TOP 100 table is not a scoreboard for “best public transport systems” in a broad sense. SDG 11.2.1 is a proximity-and-coverage measure: it captures whether people can reach a stop within a walkable distance along a street network. That design makes cross-country comparison possible, but it also creates predictable structural patterns. Countries with compact urban form and dense stop networks will tend to rise in the ranking even if service frequency or affordability vary widely.
Three macro-patterns typically explain most of the spread in results. First is urban density and street-network permeability: a fine-grained street grid makes “500 metres” reachable for more people than cul-de-sacs and wide arterial barriers. Second is stop density and network coverage: adding stops increases measured access quickly, especially in peripheral neighbourhoods where coverage is discontinuous. Third is urban footprint and sprawl: when metropolitan areas spread out faster than transit expands, proximity-based access lags even with large absolute ridership.
A useful mental model is to treat SDG 11.2.1 as a coverage threshold metric. It answers whether public transport is present and reachable for a large share of the population. It does not directly measure whether the service is frequent, safe, affordable, barrier-free, or time-competitive with cars.
The “major cities” framing matters because national access is usually driven by a handful of metropolitan regions. In countries where urban population is highly concentrated, improvements in one or two metro areas can shift the national estimate. In countries with multiple mid-sized cities, the national value reflects a more distributed set of urban systems—often producing more stable but slower-moving change.
Table 2 — Regional summary (typical central tendency and coverage)
| Region | Median access (%) | Coverage (countries; year window) |
|---|---|---|
| Europe & North America | ≈ 85% | ≈ 45 countries; 2018–2022 |
| East & South-East Asia | ≈ 78% | ≈ 14 countries; 2017–2022 |
| Latin America & Caribbean | ≈ 72% | ≈ 22 countries; 2017–2022 |
| Middle East & North Africa | ≈ 66% | ≈ 15 countries; 2016–2022 |
| South Asia | ≈ 57% | ≈ 6 countries; 2016–2022 |
| Sub-Saharan Africa | ≈ 49% | ≈ 30 countries; 2015–2022 |
| Oceania | ≈ 84% | ≈ 2 countries; 2018–2022 |
Table 3 — Biggest “access gaps” versus regional median (percentage points)
A practical way to identify lagging contexts is to compare each country’s access share to a regional median. A large negative gap does not automatically mean “poor transport”—it often signals a combination of fast urban expansion, limited stop coverage in peripheral areas, and urban layouts that make walking distances longer in practice.
| Country | Regional median (%) | Gap (pp) |
|---|---|---|
| South Sudan | ≈ 49% | ≈ −8.5 |
| Chad | ≈ 49% | ≈ −7.9 |
| Mali | ≈ 49% | ≈ −7.3 |
| Niger | ≈ 49% | ≈ −6.7 |
| DR Congo | ≈ 49% | ≈ −5.6 |
| Angola | ≈ 49% | ≈ −5.0 |
| Mozambique | ≈ 49% | ≈ −4.4 |
| Zimbabwe | ≈ 49% | ≈ −3.9 |
| Zambia | ≈ 49% | ≈ −3.3 |
| Nigeria | ≈ 49% | ≈ −2.2 |
FAQ (short, practical clarifications)
Is this ranking about public transport quality or only proximity?
Primarily proximity and coverage. SDG 11.2.1 estimates whether people can reach a stop within a walkable distance along a street network. It does not directly measure frequency, fare affordability, safety, comfort, accessibility for persons with disabilities, or travel time competitiveness.
Why can two countries with similar metros have different access shares?
Small differences in stop density, street connectivity, and the shape of urban expansion can produce sizable differences in “within 500 m / 1 km” coverage. In many cities, the decisive factor is the peripheral ring: coverage is usually near-complete in historic cores but drops rapidly in low-density edges.
Why do some countries show limited movement over time?
Access shares change slowly when networks expand at roughly the same pace as urban footprints. Large shifts usually require either major stop-network densification in underserved districts or a structural change in urban growth patterns that reduces distances to stops.
What this ranking implies for cities and national policy
Because SDG 11.2.1 is anchored in walkable access, the policy meaning of a high or low score is unusually concrete: it points to the spatial relationship between homes (and daily destinations) and the public transport network. In high-scoring contexts, a large share of the urban population lives close enough to a stop that public transport is a plausible default option. In lower-scoring contexts, public transport may still carry many riders in absolute terms, but it is less “reachable by design” for a broad share of residents.
Interpreting country differences requires separating two forces. One is the built environment (density, block size, street connectivity, and the pace of suburban expansion). The other is the network footprint (stop spacing, route coverage, and whether growth areas receive service early or late). The SDG 11.2.1 framework is particularly sensitive to the edges of urban areas: small changes in stop coverage in peripheral neighbourhoods can shift the access share materially. That makes the indicator useful for tracking whether a city is “keeping up” as it grows.
Policy takeaway (key implications)
- Coverage gaps are often more decisive than flagship projects. A single high-capacity line can be transformative locally, but national access shares typically rise when many neighbourhoods gain a walkable stop—especially in fast-growing peripheries.
- Urban form and transport planning are inseparable. If housing expansion happens in low-density layouts with poor street connectivity, “last 500 metres” access becomes structurally harder even with the same service budget.
- Equity is embedded in proximity metrics. When low-income areas sit farther from formal stops, measured access declines and daily mobility costs rise; improving coverage can reduce time burdens and widen access to jobs and services.
- Stability matters: access should not fall as cities grow. For rapidly expanding urban areas, “holding the line” (keeping the access share stable) can already represent a major logistical achievement.
- Interpret with complementary indicators. SDG 11.2.1 should ideally be paired with safety, affordability, reliability, and disability access measures when evaluating overall transport performance.
Finally, the headline percentage is best used as a directional tool for comparing coverage, not as a definitive judgment on lived experience. Two cities can have the same access share and radically different travel times if one has dedicated lanes, strong interchanges, and high frequency, while the other relies on slow mixed-traffic services. That is why SDG 11.2.1 is most valuable when it triggers a specific diagnostic question: Where are the uncovered neighbourhoods, and why is the walk-to-stop network incomplete?
Primary data sources and technical notes
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UN SDG Goal 11 (official indicator framing)
Overview of Goal 11 targets and the official definition of indicator 11.2.1.
https://sdgs.un.org/goals/goal11 -
UN Statistics Division — Global metadata for SDG 11.2.1 (SP_TRN_PUBL)
Methodology and concepts behind “convenient access” (walkable distance, service areas, population overlay) and reporting approach.
https://unstats.un.org/sdgs/metadata/files/Metadata-11-02-01.pdf -
UNEP — SDG Indicator 11.2.1 (data availability and disaggregation context)
Notes on availability patterns and the rationale for disaggregation in policy contexts.
https://sdgs.unep.org/article/sdg-indicator-1121 -
NASA SEDAC / CIESIN — SDG 11.2.1 Urban Access to Public Transport (2023 Release)
An openly accessible, global dataset providing urban-centre estimates across 178 countries (temporal extent 2015–2022), suitable for comparative analysis.
https://www.earthdata.nasa.gov/es/data/catalog/sedac-ciesin-sedac-sdgi-uapt-2023-2023.00 -
UN-Habitat — SDG 11.2 progress context
Background on interpreting the indicator in the broader SDG 11 monitoring framework and urban mobility planning.
https://unhabitat.org/sites/default/files/2021/10/a_progress_report_on_sdg_11.2.pdf
