TomTom traffic index
Covering 390 cities across 56 countries on 6 continents, our Traffic Index ranks cities around the world by their average travel times and provides free access to city-by-city information. This year, we’ve included the financial impact of rising fuel costs and congestion due to traffic, along with the fuel/kWh consumption and CO2 emissions when driving petrol, diesel or BEV cars across all 390 cities. Now in its 12th year, this edition of the TomTom Traffic Index provides more insights than ever before, helping fleet managers, city planners, carmakers, policymakers and drivers tackle traffic-related challenges, make informed mobility decisions – and steer towards a better future.
The TomTom Traffic Index is based on floating car data (FCD). TomTom collects this data from a variety of different sources to create traffic services for our clients and customers. In the Traffic Index, we use a representative sample of this data, spanning 543 billion km, to assess and show how traffic has evolved in cities around the globe over the course of 2022.
This year we introduced a new metric: travel time, through which we ranked cities by the average time it took to drive 10 km within them.
The travel time in different cities is a result of multiple factors, which can be grouped into a) quasi static factors (e.g., road infrastructure, such as street categories, capacities, and speed limits); and b) dynamic factors (e.g., traffic congestion and changes in flow). The static factors determine the optimal travel time (as shown on the city pages) in a city (or our free flow products), whereas the dynamic factors provide the traffic flow changes – and the sum of both give the travel time.
Consumption and emissions
The quantity of consumed fuel, kWh or emitted CO2 depends on multiple factors, such as the total traffic volume, the driving speed, driving style, the steadiness of the traffic flow (accelerations and decelerations) and the regional vehicle fleet composition (share of different drive and vehicle types, like petrol, diesel, hybrid or EV, and motorcycles, cars, vans, buses and trucks, respectively).
We show what an average vehicle consumes, emits or costs if you were to drive it in any city around the globe. To compare the traffic efficiency with respect to consumption and emission across different cities worldwide, we used the same class of vehicle (BEV, Diesel, and petrol combustion) across all cities (see details below).
The presented differences in consumption and emission of traffic originate from the traffic flow in the given city and not from varying fleet compositions.
What is the methodology to estimate consumption?
TomTom traffic data provides detailed information of traffic patterns (speeds, accelerations, travel times and km driven) with a 1Hz resolution and a share of up to 40% of traffic in city centers.
We combine our traffic data with our map data (slopes, road classes) and the emission and consumption model PHEM* to estimate consumptions and emissions per km.
Using PHEM, we estimate the consumption for more than 700 million km of floating car data (FCD) traces for petrol, diesel and battery electric vehicles. This data set was the basis for our ML model, which was trained and used to estimate consumption and emissions globally and on a total of 543 billion km of FCD data.
*Passenger car and Heavy-duty Emission Model – a simulation tool developed by the Graz University of Technology (TU Graz).
To compare traffic states' efficiency worldwide, we use average vehicles with different drive types, petrol and diesel, and EV.
The vehicle classes in PHEM represent the average vehicle on the European market. We use the EU6ab (petrol and diesel), and the BEV Gen 1 EV. These are comparable to average vehicles from each category.
We collect fuel prices (diesel and petrol) and charging prices from filling stations and chargers from 55 countries for 2021 and 2022. The prices are then aggregated to a daily country-wide value.
We collect charging prices from chargers across the US and Europe, and aggregate the prices for each country and day. The data is compared and complemented with EV charging prices from publicly available EV pricing literature (Lanz et al and eco-movement).
Note that there are other charging options than those considered in our analysis. For the comparison with internal combustion engine vehicles (ICE), we only take into account publicly accessible regular (AC) and fast (DC) charging into account. We do not take into account any home or subsidized charging.
- Metro area
- A circle covering the city and rural areas in close proximity. The circles were defined in the early years of the Traffic Index and with input from locals where possible. It measures traffic of the entire region. The latter often includes more highway km and shows faster traffic than the city center.
- City center (urban area)
- A circle with a radius of 5 km covering the busiest parts of the city. It measures the city traffic. This occasionally covers a few km of highway and shows the most congested areas of the cities. Larger cities have higher traffic volume and more congestion at their centers.
- Travel time for one kilometer
- It is the total travel time (sum over all traversals) divided by the total km driven (sum over all traversals). The travel time and km-driven are calculated for each directed road segment (DSEG) within the city circle (s.a.) on an hourly basis. Wethen sum overall DSEG times and km-driven and divide the total travel time by the total km driven.
- Optimal travel time
- Travel time during free flow conditions
- Cost of congestion
- The cost of congestion is the additional amount of time/fuel/CO2 used per km due to suboptimal traffic flow. It is derived by subtracting the city's measured optimal travel-time or fuel-use per km from the current value, cost = current – optimal. Note that the city values are aggregated from road segments by road use. This defines all costs of congestion – fuel, CO2, time and price per km.
- Time lost in traffic
- The time difference between the same trip in optimal conditions (free-flow travel times) and the current congested travel times.
- time lost per year
- The average time lost to waiting in traffic over the course of one year. We assume 230 working days (therefore 230 trips per year).
- time in rush hour - per day
- Calculated as the sum of travel time of a round trip (to and from) during morning rush hour (to) and evening rush hour (from). In this instance, we use the most common commuting time.
- time in rush hour - per year
- TThe time lost during rush-hour commutes accumulated over one year (230 working days – therefore 230 round trips per year).
- morning/evening rush
- The busiest hour in the morning/evening of each city, based on TomTom traffic flow measurements.
- weekly driving patterns by time of day
- A table that shows the average week. It is the weighted average over the whole year, covering each hour of the day.
- Travel time per 10km/6mi now
- The live (real-time) measurement of traffic in the city. We show the average time it would currently take to travel 10 km/6 miles on average. It is calculated using TomTom's real-time traffic information, as opposed to our historical data, which is based on a sample of the 2022 data. Here, we use the live flow feed products and our speed profile products, which are not limited to 2022 data.
- Usual travel time
- Typical travel time per 10km/6mi based on TomTom historic speed profiles.
- traffic jams now
- The current number of traffic jams (and their total length), based on TomTom’s real-time traffic information.
- tree comparison
- Based on: How much CO2 does a tree absorb?
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