Top 100 Cities by Traffic Congestion in 2026

Top 10 slowest cities by average travel time per 10 km

The top of the list shows two different congestion stories. Barranquilla, Kolkata, Bengaluru, Pune, Lima, Davao City and Trujillo point to fast-growing urban systems where demand, road capacity and public transport alternatives are not aligned. London, Kyoto and Dublin show a second pattern: even wealthy, service-heavy cities can become slow when old street networks, tourism, delivery demand and peak commuting compete for limited space.

Methodology

The ranking uses average travel time for a 10 km urban drive as the main comparison layer. This metric is useful because it converts congestion into a plain time cost: instead of asking readers to interpret an abstract index, it shows how long a relatively short city trip takes under observed traffic conditions. The 2026 page date reflects the publication frame, while the values come from the TomTom Traffic Index 2025 release based on observed 2024 road-speed and travel-time patterns. Data date: 2024 observed traffic patterns.

Cities are treated as urban areas or metropolitan road networks according to the source boundary definitions. That distinction matters because a city proper, a wider metro area and a functional commuting region can produce different rankings. The table therefore compares cities as published in the traffic-data frame rather than redefining borders independently. Names are harmonized for readability, country labels are standardized, and values are kept in mm:ss format in the table. Decimal minutes are shown only as a display option and for charts.

Annual time lost is used as a secondary exposure check, not as the main ranking column. A city can have a very slow 10 km trip but a lower yearly burden if peak congestion is concentrated into fewer hours; another city can have a slightly faster 10 km trip but a larger total burden if congestion lasts longer across the day and workweek. This is why the article separates travel-time ranking from the scatter chart.

The key limitation is comparability. App-based and navigation-based traffic datasets are powerful, but they do not capture every trip mode equally. Public transport, walking and cycling may reduce the lived burden for residents even when road travel looks slow. Weather, road works, tourism, special events, freight movement and temporary policy changes can also affect annual readings. For that reason, the ranking is best interpreted as a strong mobility signal rather than a full audit of every city’s transport system.

Key insights from the ranking

• Congestion is not only a megacity problem. Megacities are prominent, but cities such as Barranquilla, Trujillo, Davao City and Dublin show that smaller or mid-sized urban systems can also become highly constrained.
• South Asia and Latin America are heavily exposed near the top. Rapid vehicle growth, dense corridors, uneven public transport coverage and limited network redundancy can turn relatively short trips into long delays.
• Rich cities are not immune. London, Dublin, Paris, New York and Sydney show that high incomes and mature infrastructure do not automatically solve road-space scarcity.
• Congestion is a reliability problem, not only a speed problem. Once trips become unpredictable, households and firms add buffers, deliveries become harder to schedule, and the real cost spreads beyond fuel and driver time.
• Urban form matters. Historic cores, river crossings, bridge bottlenecks, narrow corridors, dispersed suburbs and weak jobs-housing balance can all raise congestion even before population size is considered.

What this means for readers

For commuters, the ranking helps translate a city’s mobility problem into daily time. A 10 km commute that regularly takes 30 minutes or more changes work-life balance, school drop-off planning, appointment reliability and housing choices. A cheaper apartment farther from work may become expensive once the lost time is counted.

For businesses, congestion affects route planning, delivery windows, field-service scheduling, employee access and last-mile logistics. In cities where short trips are slow, firms may need more vehicles, more drivers or more buffer time to deliver the same level of service.

For migrants, students, remote workers and tourists, road congestion should be read together with public transport coverage. A city can rank poorly for car trips but still be manageable if metro, rail, bus priority, cycling and walking routes cover daily needs. The opposite can also happen: a city with moderate road speeds may feel difficult if alternatives to driving are weak.

Policy takeaways

• Reliability is often the fastest win. Better incident response, signal timing, bus priority and bottleneck management can improve daily outcomes before major construction projects are completed.
• Road widening alone rarely solves the problem. Extra capacity can quickly attract more trips if pricing, parking, land use and transit alternatives stay unchanged.
• Peak spreading matters. Flexible work hours, school-time coordination, freight delivery windows and demand management can reduce the worst daily spikes.
• Public transport must be competitive, not symbolic. Rail, metro, bus rapid transit and priority lanes reduce congestion only when they are frequent, reliable and connected to where people actually live and work.
• Land-use planning is transport policy. Shorter distances between housing, jobs, schools and services reduce the number of unavoidable car trips.

Sources

• TomTom Traffic Index. Primary source frame for average 10 km travel time and congestion indicators used in this table. https://www.tomtom.com/traffic-index/
• TomTom Traffic Index ranking page. Used for comparable travel-time framing and source-method context. https://www.tomtom.com/traffic-index/ranking/
• INRIX Global Traffic Scorecard. Used as an additional reference for the economic interpretation of hours lost, delay and congestion costs. https://inrix.com/scorecard/
• International Transport Forum / OECD. Used for broader urban mobility policy context, including demand management, public transport and transport-system resilience. https://www.itf-oecd.org/
• World Bank Urban Transport and Urban Development resources. Used for background on urban mobility, transport access and city infrastructure constraints. https://www.worldbank.org/en/topic/transport