The 15th edition of the TomTom Traffic Index is our most robust and complete analysis ever. The Index benchmarks cities on their congestion levels, travel times and speeds, based on worldwide trip data spanning more than 3.65 trillion km, providing access to traffic information city by city. TomTom Traffic Index insights help state, regional and federal governments, DoTs, city planners and decision makers tackle traffic challenges and develop future-shaping strategies.
The TomTom Traffic Index is built from anonymized GPS data and real driving speeds recorded across trillions of kilometers. It enables detailed comparisons across countries and cities, offering trusted insights for cities, governments, organizations and media outlets seeking to understand how mobility evolves and how to respond to it. Data was anonymously collected from drivers within larger metropolitan areas (“metro”) and central city areas (“city”) throughout the complete road network — including fast roads and highways crossing this area.
The travel time in each city is a result of multiple factors which can be grouped into:
A) quasi-static factors (e.g., road infrastructure, such as street categories, road sizes and capacities, or speed limits) or B) dynamic factors that influence traffic flow (e.g., traffic congestion, roadworks, bad weather, etc.).
The static factors determine the optimal travel time in a city (as shown on the city pages), whereas the dynamic factors provide a basis to interpret traffic-flow changes — the sum of both gives us the travel time.
As with last year, our methodology for defining city centers and metropolitan areas aims to more accurately reflect actual traffic conditions and facilitate standardized comparisons across different cities. The foundation of the new definitions is rooted in TomTom’s Origin-Destination (O/D) Analysis, which provides detailed insights into the volume of trips between geographical areas.
We leverage anonymized vehicle movement data to examine traffic flows within a metropolitan region. This area is overlaid with a comprehensive hexagonal grid covering 4,300 square kilometers, where each hexagon cell represents approximately 4.5 square kilometers.
We extract movement patterns between all hexagons in the form of an O/D matrix using the TomTom O/D API. This data aids in identifying city-connected areas, which are clusters of regions (hexagon cells) exhibiting significant traffic flow between them. High traffic flow among these regions indicates strong socio-economic ties.
The City Area represents the dense urban core where traffic is dictated by infrastructure constraints (intersections, narrow roads, pedestrians), resulting in chronically low average speeds. It typically aligns with the jurisdiction of the city government or municipal authority.
The Metropolitan (Metro) Area represents the broader economic region defined by commuting patterns, where traffic is dictated by peak-hour flows and highway bottlenecks. It can also include suburbs, satellite towns, and commuter zones that are economically and socially linked to the city.
Utilizing TomTom's global Floating Car Data (FCD) coverage, we have developed a traffic-based framework for defining city and metropolitan areas. This method standardizes our definitions, ensuring they reflect actual movement patterns of real people and maintain consistent logic across cities worldwide.
Congestion is calculated by collecting all the travel times recorded by TomTom during a given period of time in a given area and comparing them with the lowest travel times from when traffic is in a totally free-flowing state. Congestion is expressed as a percentage, which is representative of the increase in travel time due to excess traffic. For example, a congestion level of 40 mean that, on average, journey times across that area's road network were 40% greater than when traffic is free flowing.
The congestion level of a city is based on the dynamic factors that affect its traffic flow. As explained above, congestion is recognized as the difference between free-flow or optimal traffic conditions and actual travel time. Free-flow travel times are based on static factors in each city, making the score relevant to that city's infrastructure and environment. It does not take the same time to drive 10 km without traffic in Amsterdam, the Netherlands, as it does in New York, U.S.A., as they both have different speed limits, road layouts and infrastructure.
For the first time, all data from the cities included in the Traffic Index (and more) can now be accessed through the new TomTom Area Analytics tool, launched last month. Users can select any city or define a custom area and analyze traffic across specific days, months or years with detailed hourly breakdowns. From neighborhoods to entire countries, the tool quickly delivers detailed insights into congestion levels, travel speeds and free-flow conditions, helping users better understand how mobility evolves over time while providing one of the world’s most comprehensive views of road and traffic performance.
Increase in travel time due to excess traffic. For example, a congestion level of 40 means that, on average, journey times across that area's road network were 40% greater than when traffic is free flowing.
A measure of how far you can drive in each city within a 15-minute window. It shows the average distance that can be covered in 15 minutes of driving, reported in kilometers and miles. The calculation is based on speeds observed in our FCD, reflecting actual driving behavior across the road network, which we use to convert a 15-minute duration into the distance typically reachable.
This is the total travel time (sum of all traversals) divided by the total km driven (sum over all traversals). A traversal refers to an observed vehicle traveling over a road segment. The travel time and km-driven are calculated for each directed road segment (DSEG) within the city area (s.a.) on an hourly basis. We then sum overall DSEG times and km-driven, and divide the total travel time by the total km driven.
A measure of what percentage of a typical trip in a city is driven on highways (FRC0). It shows the average share of the total trip distance that is covered on FRC0 segments, expressed as a percentage. The metric is computed as the sum of all highway distances for all trips divided by the sum of all total trip distances for those trips, multiplied by 100 percent. This is done across all observed trips in the city. The calculation is based on our FCD, reflecting actual driving behavior across the road network.
A measure of typical driving speed on highways (FRC0) in each city. It shows the average speed observed on FRC0 segments, reported in km/h and mph. The metric is computed over the highway portions of observed trips within the city boundary as total FRC0 distance divided by total FRC0 travel time (a distance-weighted mean). Calculations are based on our FCD, reflecting actual driving behavior across the road network.
A measure of typical driving speed on non-highway roads in each city. It shows the average speed observed on road segments excluding highways (non‑FRC0), reported in km/h and mph. The metric is computed over the non‑highway portions of observed trips within the city boundary as total non‑FRC0 distance divided by total non‑FRC0 travel time (a distance‑weighted mean). Calculations are based on our FCD, reflecting actual driving behavior across the road network.
Average time spent driving a 6mi/10km trip twice a day at peak hours (working days). This is the time lost to rush hour commutes across the entire year. 230 working days equate to 230 round trips per year.
Based on traffic flow measurements, we calculate the busiest parts, morning and evening, of each day in each city.
This table, on each city page, shows how driving times, congestion levels and speed fluctuate based on the time of day as a weighted average over the whole year, for each hour of the day. The weighted average is based on distance driven, so a road that is busier has a higher weighting. This reflects how most drivers experience traffic.
The live (real-time) measure of how far you can drive in each city within a 15-minute window. It shows the average distance that can be covered in 15 minutes of driving, reported in kilometers or miles. It's calculated using TomTom's real-time traffic information, as opposed to historical data, which is based on a sample of 2025 data. We use the live Traffic Flow feed and our Speed Profile products, which are not limited to historic 2025 data.
The current number of traffic jams and their total length, based on TomTom's real-time traffic information.
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