The TomTom Traffic Index is based on floating car data (FCD). TomTom collects this data from various sources to create traffic services for our clients and customers. In the Traffic Index, we use a representative sample of this data, spanning 737 billion km, to assess and show how traffic evolved in cities around the globe throughout 2024.

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.

How is a city defined?

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.

City centers are defined by selecting the densest areas that capture 20% of all trips within the city-connected area.

Metropolitan areas encompass the trip-dense regions that account for 80% of all trips within the city-connected areas.

The selection process is carried out systematically by adding regions with the highest traffic intensity until the specified thresholds (20% for city centers and 80% for metropolitan areas) are reached.

Why this approach?

Utilizing TomTom's global Floating Car Data (FCD) coverage, we have developed a traffic-based framework for defining city centers and metropolitan areas. This method standardizes our definitions, ensuring they reflect actual movement patterns of real people and maintain consistent logic across cities worldwide. How is the congestion level calculated?

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 fluid 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.

Why doesn't the most congested city also have the slowest average speed?

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.

New for this year: Vehicle Volumes

This year we have added vehicle/traffic volume insights for U.S. cities. TomTom specializes in Floating Car Data (FCD, vehicle probe data). We observe and gather data from up to 1 in 4 vehicles on European and North American roads in daylight hours. Data is collected from four key sources: connected cars, mobile navigation applications, on-dashboard navigation devices (satnavs) and managed fleet telematics devices (blackboxes). This provides the basis for our live and historic traffic speed estimations and is a powerful tool for making a precise estimate of total traffic volumes.