TomTom Traffic Index


Covering 404 cities across 58 countries on 6 continents, our Traffic Index ranks urban congestion worldwide and provides free access to city-by-city information. This year, we include emissions data for 4 European cities. You, along with drivers, city planners, carmakers and policy makers, can use the index to help tackle traffic-related challenges. Now in its 11th year, you will find even more insights from both real-time and historical data.

The TomTom Traffic Index is one more way we are helping create a safer, cleaner, congestion-free world, together with our partners and global community of drivers.

Our methodology

The numbers

Wondering how we determine the rankings, and what exactly the percentages mean?

A 53% congestion level in Bangkok, for example, means that a 30-minute trip will take 53% more time than it would during Bangkok’s baseline uncongested conditions.

You can turn this 53% into travel time through simple calculations.
First: 0.53 x 30 mins = 15.9 mins extra average travel time.
Second: 30 mins + 15.9 mins = 45.9 mins total average travel time.

We calculate the baseline per city by analyzing free-flow travel times of all vehicles on the entire road network – recorded 24/7, 365 days a year. This information allows us to also calculate, for example, how much extra time a driver will spend in traffic during rush hour in Bangkok.

We perform calculations for all hours of each day, so you can see congestion levels at any time in any city, including morning and evening peak hours.

The data

With TomTom, your data is never sold and is always anonymized – meaning we break the link between your identity and the data we receive. Our Traffic Index data comes from our growing community of more than 600 million drivers, who use TomTom tech in navigation devices, in-dash systems and smartphones around the world.

The colors

The color-coded figures you see are based on measurements from TomTom's historical traffic database. To ensure our statistics best represent the driving experience on the road, we measure individual road segments as well as entire road networks. Then we weight busier, more important roads to calculate an accurate overall congestion level.

You’ll see four different colors indicating congestion levels in cities.

> 50%
25% - 50%
15% - 25%
< 15%

Emissions data

TomTom emissions estimation

Vehicle Traffic leads to emission of both GHG (greenhouse gases like CO2) and pollutants (like NOx, PM, CO). The quantity of emitted gases depends on multiple factors, like the total traffic volume, the driving speed, the steadiness of the traffic flow (accelerations and decelerations) and the regional vehicle fleet composition (share of different drive and vehicle types, like gasoline, diesel, hybrid or EV; and motorcycles, cars, vans, buses and trucks, respectively).

With the new emissions report, TomTom estimates the emissions and energy consumption due to vehicle traffic for cities. TomTom traffic data provides detailed information of traffic patterns with a 1Hz resolution and a share of up to 40% of traffic in city centers, allowing us to quantify the contribution to emissions resulting from acceleration maneuvers, speeds and road specifications. We combine our traffic data with our map data (slopes, road classes), an estimation of the vehicle fleet based on NEMO fleet module1 and the emission and consumption model PHEM2to estimate emissions.

Using PHEM, we estimate emissions for each FCD trace for a given vehicle and emission class. To assess the total emission for a city, TomTom estimates the total number of driven trips using traffic counts from loop data and the current vehicle fleet mix on the road

1 NEMO fleet module is a fleet estimation model from the Graz University of Technology (TU Graz) which uses the regional vehicle registration numbers to estimate the share of kilometers driven. 

2 PHEM (Passenger car and Heavy-duty Emission Model) is an instantaneous emission model based on equations of vehicle longitudinal dynamics and engine emission maps, which has been developed by TU Graz. PHEM calculates the energy required to perform the driving maneuvers (speeds, accelerations) observed in our data and estimates the resulting emissions. PHEM is calibrated with real world data: the TU-Graz has measured emissions from a vast range of vehicles representative of the EU vehicle fleet to establish emission maps.


We thank Martin Treiber (University of Technology Dresden, Institute for Econometrics and Statistics particularly in Transportation) for his great scientific advice and many fruitful discussions.

We further thank Martin Dippold and Stefan Hausberger (ITNA, Graz University of Technology) for scientific advice and great technical support for PHEM and NEMO-fleet.


A traffic situation in which travel times are not impacted by congestion. Typically occurs at night but can happen any time of day.
Extra travel time compared to a one-hour period during free flow conditions.
Extra travel time as compared to a one-hour period during free flow conditions, multiplied by 230 working days per year.
Extra travel time during rush hours compared to a 30-minute period during free flow conditions.
Extra travel time during rush hours as compared to a one-hour period during free flow conditions, multiplied by 230 working days per year.
The busiest one-hour period in the morning/evening as defined per city based on actual traffic measurements.
This table shows the congestion level for each hour of each day of the week, on average across the entire year, for each city.
Day with the congestion level that is at least two times lower than the congestion level on the respective day in 2019.
Day with the congestion level that is at least two times higher than the congestion level on the respective day in 2019.
Urban area boundaries
We defined our own urban areas, using the same methodology for all cities indexed around the world. We did it ourselves to increase accuracy, as municipal and statistical boundaries are not internationally uniform in their size and coverage of urban areas.

The current extra travel time drivers are experiencing on average. It is calculated using TomTom’s real-time traffic information, as opposed to our historical data used elsewhere throughout the Traffic Index.


The current number of traffic jams (and their total length), based on TomTom’s real-time traffic information.


An estimation of the vehicle kilometers of mopeds, motorcycles, cars, vans, buses, trucks and lories of different drive types and EU-emission classes.


The cost of congestion is defined as extra emissions due to inefficient traffic. It is the difference between the optimal emission and suboptimal emission caused by traffic delays. Note that the optimal emission is estimated regionally and does not reflect a global optimum.


Size of natural forest regeneration to offset 1 year of traffic emissions. Based on: How much CO2 does a tree absorb?

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