# Harmonized Annual Averaged Traffic Data at Street Segment Level for European Cities

Xavier Bonnemaizon, Philippe Ciais, Chuanlong Zhou, Qinren Shi, Rohith Teja Mittakola, Clément Goldmann, Simon Ben Arous, Nicolas Megel, Steven J Davis

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Xavier Bonnemaizon, Philippe Ciais, Chuanlong Zhou, Qinren Shi, Rohith Teja Mittakola, et al. Harmonized Annual Averaged Traffic Data at Street Segment Level for European Cities. Scientific Data, 2025, 12 (1), pp.1365. 10.1038/s41597-025-05698-y. hal-05231916

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# Harmonized Annual Averaged Traffic Data at Street Segment Level for European Cities

Xavier Bonnemaizon<fnref n="1" />,²✉, Philippe Ciais<fnref n="1" />, Chuanlong Zhou<fnref n="1" />, Qinren Shi<fnref n="1" />, Rohith Teja Mittakola<fnref n="1" />, Clément Goldmann<fnref n="1" />, Simon Ben Arous², Nicolas Megel¹³& Steven J. Davis¹⁴

Traffic flow data in Europe are collected locally by city authorities using different systems and standards, making it difficult to compare cities or evaluate large-scale maps, such as those used for emission inventories. To address this gap, we compiled and harmonized publicly available traffic data for 36 European cities, linking geolocalized information to road segments, spanning years from 2015 to 2024 depending on data availability. Annual Average Daily (or Weekday) Traffic is provided, and supplementary variables (e.g., truck flow percentages and speed metrics) are included where available. The data are georeferenced, with geometries corresponding to each measurement location. The dataset was enriched with additional attributes through map matching of traffic measurement locations to OpenStreetMap. Code and methodology for transforming raw data into a uniform structure are documented in Python Jupyter Notebooks, ensuring transparency and reproducibility. This dataset in a unified format facilitates cross-city comparisons and supports applications in environmental science, including the estimation of greenhouse gas and pollutant emissions, as well as urban planning and road transport management.

## Background & Summary

Estimating Annual Average Daily Traffic flow (AADT, in number of vehicles per day) is important for urban planning and traffic management. Various approaches are employed to measure traffic<sup>1,2</sup>. While many local authorities rely on static sensors, such as radar or magnetic loops, temporary pneumatic or manual measurements remain common. Recently, the adoption of AI-enabled camera systems has gained growing interest, providing enhanced capabilities for accurately classifying vehicle types<sup>3</sup>. This is particularly valuable as urban traffic becomes increasingly diverse, with the growing popularity of bicycles and personal light electric vehicles<sup>4</sup>. Camera systems sensors also exhibit fewer data gaps compared to road-embedded sensors, which are often disrupted by construction work (e.g., cutting a magnetic loop), however the classification can be affected by night time or poor weather conditions. This highlights the diversity of traffic measurement methods and the challenge of reporting traffic data in a standardized manner.

Localized AADT statistics, combined with road section lengths and emission factors, enable the estimation of pollutant and greenhouse gas emissions from road transport<sup>5-8</sup>. Additional parameters, such as vehicle classification and fleet speeds, are often necessary to refine these inventories, as emission factors can vary significantly depending on these two variables (e.g., COPERT<sup>9</sup>, MOVES<sup>10</sup> models). However, AADT remains the primary driver of emissions at the street segment level. This is particularly evident in urban areas, where roads exhibit a vast range of traffic capacities: from low-traffic residential streets with only a few thousands of vehicles per day to major arterials and urban highways carrying 100,000 vehicles daily<sup>11</sup>.

Aligned with European open data policies, an increasing number of cities are launching open data portals, often containing release of historical traffic information. These portals offer notable advantages, such as higher update frequency compared to national statistics and the granularity and precision of localized datasets<sup>12</sup>. However, these datasets are provided in non-standardized city-specific formats, creating significant challenges for comparative analysis and cross-regional integration<sup>13,14</sup>. Disposing of harmonized data across different cities

<footnote n="1">¹Laboratoire des Sciences du Climat et de l'Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, Gif-sur-Yvette, France. ²Kayrros SAS, Paris, France. ³NEXQT SAS, Paris, France. ⁴Department of Earth System Science, Stanford University, Stanford, CA, USA. ✉e-mail: xavier.bonnemaizon@lsce.ipsl.fr</footnote>
<figure><img src="image_3.png" /><figcaption>**Fig. 1** Map of selected cities for this study. The flowchart describes the systematic process employed in order to generate our harmonized dataset of annual averaged daily traffic. Of the 58 cities investigated, traffic flow data was found for 53 (5 cities without data, marked with black crosses), and standardized data was obtained for 36 (blue dots). Cities with data that could not be standardized (red triangles) faced various challenges, such as real-time-only data availability (e.g., Nantes⁵⁷, Brussels⁵⁸, Bilbao⁵⁹), inaccessible downloads (e.g., Rome⁶⁰, Budapest⁶¹, Warsaw⁶²), the lack of geospatial coordinates for flow attribution (e.g., Athens⁶³, Nicosia⁶⁴), nationwide dataset with only major highways (Ljubljana⁶⁵, Tallinn⁶⁶, Riga⁶⁷), or static maps only (Prague⁶⁸, Munich⁶⁹).</figcaption></figure>

is necessary to build traffic models that can be applied at large scale. Training a model predominantly on data from a single city, or when one region or country dominates the dataset, can introduce biases when applied elsewhere⁷,⁸. Incorporating harmonized data from multiple cities and sources refines validation metrics and enables better assessment of a model's quality and adaptability across different urban contexts¹⁵.

Although efforts to collect multi-source traffic flow data have been undertaken by researchers¹⁶,¹⁷, AADT and ancillary data collections can quickly become outdated and are sometimes not directly downloadable. Additionally, reproducibility is burdensome, as previous studies often did not include URLs for accessing the original data, as well as the code used to process the datasets. The DATEX II¹⁸ data standard has been developed in Europe for traffic management information exchange but is primarily focused on motorways and real-time information. A GitHub repository from Graphhopper¹⁹ provides a collection of data sources. However, these sources vary in terms of variables and spatial coverage. Other initiatives, such as Telraam²⁰, collect crowdsourced traffic measurements, but suffer from heterogeneous spatial distribution and a lack of coverage for urban main highways.

In this study, we collected and harmonized AADT data from individual measurement sensors in 36 cities. In cases where only the Annual Average Weekday Traffic (AAWT, equivalent to AADT excluding weekends) is available, this distinction is documented. We provide detailed access information of the original data sources (URLs), and Python notebooks to facilitate data standardization and preparation for further analysis. A qualitative comparison of city-specific data is included to ensure robustness and reliability of the dataset. Additionally, we document cities where historical AADT values were unavailable but other valuable traffic statistics were provided. Finally, we discuss recommendations for these cities to improve and standardize their data formats for greater compatibility between open data platforms.

To our knowledge, this is the first study to present an extensive list of open data sources for urban mobility and harmonized traffic datasets across European cities. This open-source project offers a foundation that can be expanded through collaborative efforts to map and document all available sources of validation data for road transportation modeling in European cities and even beyond.

## Methods

The process of producing the dataset follows a systematic methodology to ensure harmonization across a diverse range of city-specific traffic data (flowchart Fig. 1). First, open data portals of European cities were identified and explored in search of datasets related to traffic volume monitoring. Our investigation covered the capitals of the EU-27 countries, along with Norway, Switzerland, the United Kingdom (UK), and additional major cities.
<table><caption>**Table 1.** List of the 36 cities with standardized data. The table includes the temporal coverage of the standardized data (* indicating that earlier raw data is available), the average number of sensors per year, the provided flow variable, and the technology used (IL: Inductive Loop; M: Manual; PT: Pneumatic Tube; C: Camera; Mo: Model; A: Automatic; n/a: Unknown). Source URLs for raw traffic count measurements are also provided. Additional details can be found on the GitHub repository.</caption><thead><tr><th>Country</th><th>City</th><th>Years available</th><th>Average number of sensors</th><th>Frequency of raw data</th><th>Mandatory variable</th><th>Measurement technology</th><th>Raw data source URL</th></tr></thead><tbody><tr><td>Austria</td><td>Vienna</td><td>2016–2024</td><td>68</td><td>monthly</td><td>Both</td><td>Static sensors</td><td>26</td></tr><tr><td>Czechia</td><td>Brno</td><td>2016–2023</td><td>589</td><td>yearly</td><td>AADT</td><td>n/a</td><td>27</td></tr><tr><td>Denmark</td><td>Copenhagen</td><td>2015–2024*</td><td>43</td><td>yearly</td><td>AADT</td><td>Temporary counts</td><td>28</td></tr><tr><td>Finland</td><td>Helsinki</td><td>2015–2023*</td><td>56</td><td>yearly</td><td>AADT</td><td>A</td><td>29</td></tr><tr><td rowspan="10">France</td><td>Bordeaux</td><td>2019–2023*</td><td>433</td><td>yearly</td><td>AAWT</td><td>IL</td><td>30</td></tr><tr><td>Grenoble</td><td>2022</td><td>113</td><td>yearly</td><td>AADT</td><td>IL</td><td>31</td></tr><tr><td>Lille</td><td>2015–2024*</td><td>759</td><td>yearly</td><td>AAWT</td><td>IL - PT</td><td>32</td></tr><tr><td>Lyon</td><td>2019–2023</td><td>341</td><td>yearly</td><td>AAWT</td><td>IL</td><td>33</td></tr><tr><td>Marseille</td><td>2020</td><td>10819</td><td>yearly</td><td>AADT</td><td>Mo</td><td>34</td></tr><tr><td>Montpellier</td><td>2015–2024*</td><td>1011</td><td>yearly</td><td>Both</td><td>M - PT - Other sensors</td><td>35</td></tr><tr><td>Paris</td><td>2023*</td><td>1887</td><td>hourly</td><td>Both</td><td>IL</td><td>36</td></tr><tr><td>Rennes</td><td>2015–2024*</td><td>348</td><td>yearly</td><td>AADT</td><td>Stationary and Temporary</td><td>37</td></tr><tr><td>Rouen</td><td>2015–2024*</td><td>209</td><td>yearly</td><td>Both</td><td>Temporary counts</td><td>38</td></tr><tr><td>Toulouse</td><td>2018–2023</td><td>897</td><td>yearly</td><td>AAWT</td><td>PT - C - Radar</td><td>39</td></tr><tr><td>Tours</td><td>2019–2022</td><td>256</td><td>yearly</td><td>AAWT</td><td>n/a</td><td>40</td></tr><tr><td rowspan="2">Germany</td><td>Berlin</td><td>2024*</td><td>212</td><td>hourly</td><td>Both</td><td>n/a</td><td>41</td></tr><tr><td>Hamburg</td><td>2015–2023*</td><td>305</td><td>yearly</td><td>Both</td><td>IL - M - Infrared</td><td>42</td></tr><tr><td>Ireland</td><td>Dublin</td><td>2023*</td><td>546</td><td>hourly</td><td>Both</td><td>IL</td><td>43</td></tr><tr><td>Italy</td><td>Milan</td><td>2024*</td><td>40</td><td>sub hourly</td><td>Both</td><td>C</td><td>44</td></tr><tr><td>Luxembourg</td><td>Luxembourg</td><td>2023*</td><td>183</td><td>daily</td><td>Both</td><td>Radar</td><td>45</td></tr><tr><td>Netherlands</td><td>Amsterdam</td><td>2019; 2025</td><td>4895</td><td>yearly</td><td>AADT</td><td>Mo</td><td>46</td></tr><tr><td>Norway</td><td>Oslo</td><td>2015–2023*</td><td>412</td><td>hourly</td><td>AADT</td><td>IL</td><td>47</td></tr><tr><td>Portugal</td><td>Lisbon</td><td>2022–2023*</td><td>8</td><td>hourly</td><td>AADT</td><td>n/a</td><td>48</td></tr><tr><td rowspan="3">Spain</td><td>Barcelona</td><td>2018–2024</td><td>9</td><td>yearly</td><td>AAWT</td><td>n/a</td><td>49</td></tr><tr><td>Madrid</td><td>2024*</td><td>4744</td><td>sub hourly</td><td>Both</td><td>IL - C</td><td>50</td></tr><tr><td>Valencia</td><td>2016–2022</td><td>32</td><td>monthly</td><td>AAWT</td><td>IL</td><td>51</td></tr><tr><td rowspan="2">Sweden</td><td>Malmo</td><td>2015–2024*</td><td>196</td><td>yearly</td><td>AADT</td><td>n/a</td><td>52</td></tr><tr><td>Stockholm</td><td>2015–2024*</td><td>1752</td><td>yearly</td><td>AADT</td><td>n/a</td><td>53</td></tr><tr><td rowspan="2">Switzerland</td><td>Geneva</td><td>2017; 2019*</td><td>216</td><td>yearly</td><td>Both</td><td>IL - PT</td><td>54</td></tr><tr><td>Zurich</td><td>2024*</td><td>205</td><td>hourly</td><td>Both</td><td>n/a</td><td>55</td></tr><tr><td rowspan="6">United Kingdom</td><td>Birmingham</td><td>2015–2023*</td><td>252</td><td>yearly</td><td>AADT</td><td>M - Mo - A</td><td rowspan="6">56</td></tr><tr><td>Cardiff</td><td>2015–2023*</td><td>97</td><td>yearly</td><td>AADT</td><td>M - Mo - A</td></tr><tr><td>Edinburgh</td><td>2015–2023*</td><td>108</td><td>yearly</td><td>AADT</td><td>M - Mo - A</td></tr><tr><td>Glasgow</td><td>2015–2023*</td><td>204</td><td>yearly</td><td>AADT</td><td>M - Mo - A</td></tr><tr><td>London</td><td>2015–2023*</td><td>2201</td><td>yearly</td><td>AADT</td><td>M - Mo - A</td></tr><tr><td>Manchester</td><td>2015–2023*</td><td>191</td><td>yearly</td><td>AADT</td><td>M - Mo - A</td></tr></tbody></table>

The scope of the dataset is expected to evolve as we encourage contributions from anyone wishing to supplement it with data from additional cities not currently included. Norway, UK and Luxembourg databases are nationwide, so other cities in these countries might be accessed following the same methodology. We searched for information in the local language to locate relevant datasets. Each dataset included is cited along with its source URL within Table 1 and the GitHub repository (see Data Records). The different licenses are reported in "cities_summary.csv".

A key selection criterion was the inclusion of geographical coordinates within the data, which were sometimes linked indirectly through additional files referencing a "sensor ID". We reconstructed the geographic coordinates of the corresponding road segments for Lisbon and Barcelona where few sensors were available and no or inaccurate geographical coordinates were provided. Most cities provided point-based or linestring geographical references, but exceptions included Malmo and Madrid, where orthogonal and polygons of directional arrows were employed. The geographic extent of road measurements around the city varies significantly, depending on the jurisdiction of the local authority that created the dataset. Some local datasets don't monitor major road segments as these might be managed by a higher-level entity (region, state). Future improvements could include the merging of several datasets from various sources managing different road importances. In cases where the
<table><caption>**Table 2.** List of mandatory features (**bold**) and optional ones included in each processed file. The availability of local dataset features depends on their presence in the original data (Supplementary Table S1). Additional OSM features were retrieved using map matching algorithms detailed in Methods and notably the file "mapMatching_OSM.py" on GitHub.</caption><thead><tr><th>Data source</th><th>Feature name</th><th>Description</th></tr></thead><tbody><tr><td rowspan="10">Local dataset</td><td>AADT orAAWT<br>float</td><td>Average Annual Daily (Weekday) Traffic (vehicles per day).</td></tr><tr><td>s85<br>float</td><td>85th percentile of observed speed (km/h).</td></tr><tr><td>savg<br>float</td><td>Average monitored speed (km/h).</td></tr><tr><td>TR<br>Example: TR_AADT</td><td>Trucks (variables related to trucks/heavy vehicles).</td></tr><tr><td>2W<br>Example: 2W_savg</td><td>2-Wheels (variables related to 2-wheels).</td></tr><tr><td>pct<br>Example: TR_pct_AAWT</td><td>Percentage (value as a percentage of AADT/AAWT).</td></tr><tr><td>geometry<br>Point,LineString</td><td>Geographic coordinates of the sensor location (Point) or the corresponding road segment (LineString) in the WGS84 projection.</td></tr><tr><td>raw_name<br>text</td><td>Name of the road segment.</td></tr><tr><td>raw_oneway<br>boolean</td><td>Whether or not the sensor monitors flow in one direction only. If this is not provided one may look at the city-level labeling in "cities_summary.csv".</td></tr><tr><td>raw_direction<br>text</td><td>Indication for the direction of the flow. This field can be an angle value or text indication (not translated). NaN if raw_oneway is False.</td></tr><tr><td rowspan="5">Road segment<br>informations</td><td>raw_lanes<br>float</td><td>The number of lanes of the monitored road segment.</td></tr><tr><td>raw_techno<br>text</td><td>The counting technique employed when this information is provided at the sensor level (not translated). Otherwise one may look at the city-level attribute Table 1.</td></tr><tr><td>osmid<br>float</td><td>The "road segment ID" that links the provided data to the OSM referential.</td></tr><tr><td>osm_name<br>text</td><td>Name of the matched road segment.</td></tr><tr><td>osm_type<br>text</td><td>The OSM "highway" classification type.</td></tr><tr><td rowspan="5">OpenStreetMap</td><td>osm_lanes<br>float</td><td>The number of lanes of the matched road segment. Many values could be missing.</td></tr><tr><td>osm_oneway<br>boolean</td><td>Whether or not the matched road segment has flow in one direction only. Retrieved from the presence of 'oneway' attribute or 'lanes:backward/forward' in OSM. Many values could be missing.</td></tr><tr><td>osm_maxspeed<br>float</td><td>The indicated speed limit on the road segment (km/h) retrieved from "maxspeed" in OSM.</td></tr><tr><td>osm_distance<br>float</td><td>The distance (meters) between the sensor location and the matched road segment (for point geometries only). This can serve as a quality indicator for the matching process.</td></tr></tbody></table>

raw dataset is nationwide, we clip it to corresponding functional urban areas²¹ (Norway, Luxembourg) or filter it using the local authority name (UK).

For 5 cities out of 58 investigated, no actual traffic flow data was found on open access (black crosses Fig. 1). Harmonization was not achievable for 17 cities (red triangles Fig. 1). Among other limitations, this is due to missing geographical coordinates (e.g., Athens, Nicosia), real-time-only data availability (e.g., Nantes, Brussels, Bilbao), inaccessible downloads (e.g., Rome, Budapest, Warsaw), nationwide dataset with only major highways (Ljubljana, Tallinn, Riga), or static maps only (Prague, Munich). In those cases, the data source URLs and some processing code are still available in the GitHub repository for users who may find them useful.

Data acquisition was followed by preprocessing and transformation into a standardized GeoJSON format. This step involved handling the unique data formats, field names, and encoding conventions employed by each city. AADT (or AAWT) values were calculated when not directly provided, typically by aggregating higher-frequency data, such as hourly or daily recordings (Table 1). For hourly cases, values are linearly interpolated for up to 4 consecutive missing records. Days with more than 4 consecutive missing records are excluded. Hourly traffic is then summed for each day, and the resulting daily totals are averaged to compute AADT (excluding weekends to compute AAWT). This last step is directly employed in the case of raw daily recordings. The corresponding process is presented in a dedicated file ("temporary_Agg.py") in the GitHub repository. Spatial aggregation to the road section had to be performed in the cases where the reported values are lane-level. Obvious outliers were identified and removed when necessary. Specifically, we retained values between 2 and 200,000 vehicles per day, which corresponds to approximately twice the order of magnitude above the typical daily flow on the Paris périphérique, one of the busiest roads in Europe.

Finally, map matching techniques were proposed and performed as an example on the OpenStreetMap²² (OSM) network (see Usage Notes). For each road segment, we include the results of our map matching algorithms into processed files. The retained features from OSM are presented in Table 2. For linestring geometries, matching can be achieved with great confidence by employing methods like Hidden Markov Models, such as
<table><caption>**Table 3.** Guidelines for an ideal local traffic flow measurement dataset.</caption><thead><tr><td>Category</td><td>Guidelines</td></tr></thead><tbody><tr><td>Metadata File</td><td>Should be available in English and describe the data collection process, including sensor technology (e.g., inductive loops, radar, cameras).</td></tr><tr><td>Variable Definitions</td><td>Clearly define all recorded variables, specifying data types (e.g., average vs. maximum values) and temporal resolution.</td></tr><tr><td>Measurement Scope</td><td>Specify what is being measured, such as whether all motor vehicles are included, bicycles are excluded, or public transport is accounted for.</td></tr><tr><td>Geographical Attribution</td><td>Provide a geographic referential linking traffic data to specific road sections geometries.</td></tr><tr><td>Preferred Geometry Format</td><td>Use oriented linestrings with unique IDs and descriptive attributes (e.g., road name, segment type).</td></tr><tr><td>Handling of Point Geometry Format</td><td>If point-based data is used, specify directionality through angular measures or categorical labels (e.g., "North") to enable easier conversion into linestrings.</td></tr></tbody></table>

<table><caption>**Table 4.** Content details of each city-level folder on the GitHub.</caption><thead><tr><th>Folder/File</th><th>Description</th></tr></thead><tbody><tr><td>raw/</td><td>Contains original data files as retrieved from city-specific open data portals. Files are in their native formats (e.g., CSV, JSON, GeoJSON, SHP) and reflect the diversity in data reporting standards. If raw data was too large for storage, it was either excluded or sampled. Notebook files assume access to the complete raw data, while fully processed data is included.</td></tr><tr><td>treated/</td><td>Stores standardized datasets in GeoJSON format with consistent fields. Each feature details are documented in Table 2. Files follow the naming convention {city_name}_{AADT/AAWT}_{year}.geojson.</td></tr><tr><td>Mandatory Attributes</td><td>Each dataset includes AADT (or AAWT, at least one required) and geometries (as Point or LineString).</td></tr><tr><td>Optional Attributes from the local dataset</td><td>If available in the raw data, additional flow metrics may include vehicle categories (e.g., heavy vehicles) and speed metrics (average or 85th percentile, in km/h). Other attributes related to the measurement (name, direction, technology) are included when available.</td></tr><tr><td>OSM Attributes</td><td>Map matching was performed for each road segment, and additional attributes are included where the process was successful (see Methods). If matching was not achieved, the corresponding values are marked as NaN.</td></tr><tr><td>Python notebooks ((city_name).ipynb)</td><td>Contains preprocessing steps for cleaning, harmonizing, and transforming raw data. Each notebook is specific to a city or a dataset and includes annotations explaining methods and assumptions.</td></tr></tbody></table>

those implemented in the PyTrack Python library²³. For Point wise geometries, the matching task is more complex, particularly near intersections where sensor placement may lead to ambiguity. In these cases, we used the nearest neighbor approach, prioritizing roads with greater traffic importance (according to the OSM attribute "highway"), as they are more likely to be monitored. The corresponding code is available in the GitHub repository in "map_matchin OSM.py" and can be adapted to match our dataset to any other geographic referential. Note that using PyTrack algorithms requires a referential with a graph structure.

The final processed data was structured to ensure compatibility and comparability across different cities, making it suitable for a range of analytical applications (see Usage Notes and Table 2). A significant challenge was the lack of consistent metadata, which complicates the interpretation of field meanings and the identification of sensor technologies used in data collection. In particular, a difficulty was determining whether a measurement represented the total traffic flow for a road section or only a single direction. When available, this distinction was recorded at the sensor level under the attribute "raw_oneway", while the monitored direction was stored in "raw_direction". However, as this information was often missing in the original data (Supplementary Figure S1 and Table S1), we provided our city-level interpretations in the "direction" attribute within the "assets/cities_summary.csv" file on GitHub. We advise using this field cautiously, as some cases may remain ambiguous.

To improve multisource raw traffic datasets, independent of the scope, frequency, or available features in individual cities, we propose in Table 3 some recommendations based on our experience.

## Data Records

The dataset, along with processing scripts and related documentation, is archived on Zenodo²⁴, and the most up-to-date version is maintained on a public GitHub repository (https://github.com/XavB64/traffic-volume-data-EU-cities). The repository is structured by country, further divided into city-specific folders. Within each city's folder, users can find three main components (Table 4): raw data along with its source URL, processed data, and notebooks detailing the transformations applied to convert the raw data into the standardized format. Each file stored in the treated/ directory is a GeoJSON corresponding to a specific city and year. These files contain geometries in the form of Points or LineStrings, with each record representing a road segment. They include at least one of the two traffic variables, AADT or AAWT, expressed in vehicles per day. When it is available, the local authority name of the road segment is also provided under the attribute "raw_name". The full list of mandatory and optional attributes for the processed files are listed in Table 2.

The harmonized dataset is the result of the concatenation of the final GeoJSON files and is stored in harmonized_traffic_data/. It covers 36 cities and includes approximately 120 000 data points across 35 000 sensors. The
<figure><img src="image_4.png" /><figcaption>**Fig. 2** Example of AADT maps for the 36 cities included in the dataset. The data corresponds to the most recent year available for each city (except Rouen and Copenhagen where the second to last year is used to show more sensors). Where AADT is unavailable, AAWT is shown instead. Marker sizes are adapted depending on their number to improve readability. Notably, discontinuous short line geometries were dramatically widened in order to make them visible (Lille, Tours, Lisbon). Base map: OpenStreetMap contributors, CARTO (Positron style).</figcaption></figure>

data spans from 2015 to 2024, varying by city based on data availability. One exception is the city of Amsterdam where the average daily traffic was also modeled for 2025. We showcase an example of AADT and AAWT maps in Fig. 2, illustrating the diversity of our dataset. While some raw files contain data prior 2015, earlier data was excluded to manage the repository size. However, users can use the provided notebooks to process and standardize this older data if needed. A complete summary of the dataset and details on each city is available in Table 1.

## Technical Validation

Validating road traffic flow measurements is challenging, as local authorities are often the only reliable source for such data. However, temporal trends can be assessed by comparing traffic flow statistics with independent Floating Car Data (FCD) obtained from vehicles equipped with geolocation devices. Previous studies¹¹,¹⁵ have conducted such analyses with a finer temporal resolution, using FCD to verify official traffic datasets or derived
<figure><img src="image_5.png" /><figcaption>**Fig. 3** Validation of our dataset with private FCD from NEXQT. (a) presents an example for Paris in 2023 of the AADT derived from FCD counting and open data sources. Road importance is derived from the FCD product. (b) illustrates the distribution of performance metrics across 31 cities, specifically in terms of the R² score (representing a direct linear regression between FCD and open data) and the Pearson correlation coefficient. The red bold line represents the median, the box represents the first and third quartiles, and whiskers extend from the box to the farthest data point lying within 1.5 times the inter-quartile range.</figcaption></figure>

models, offering insights into the consistency of reported measurements. We performed a validation experiment by comparing our dataset with independent FCD provided by the company NEXQT covering the years 2022, 2023 and 2024. AADT and AAWT values were derived from FCD vehicle counts on corresponding road segments and merged with our dataset. Figure 3 presents intra-city spatial correlations for 31 cities (Copenhagen, Brno, Vienna, Oslo and Helsinki were excluded due to the lack of FCD availability). For cities where FCD data was available but no traffic counts were present in our dataset between 2022 and 2024 (Marseille, Geneva, Amsterdam), we used the most recent year available. Results indicate that in over 75% of the cities, the R² score from a direct linear regression between the two sources exceeds 0.5, and the Pearson correlation coefficient (ρ) is greater than 0.7. Low correlations and R² scores are likely due to uncertainty in the geolocation data, particularly in road segments or cities with poor signal. This comparison is based on two independent sources, both with inherent limitations. These findings support the overall validity and internal consistency of the open traffic data presented in this study.

To further ensure the quality of our dataset, we present in Fig. 4 the distribution of AADT and AAWT for each city, allowing for direct comparisons. The data corresponds to the most recent year available for each city, except for Rouen and Copenhagen where the second to last year is used instead, as their latest year benefits from significantly less measurements. Additionally, the right panel provides more details based on the results of map matching with the OpenStreetMap (OSM) referential. We examine the distribution of road types within each city's road monitoring dataset to identify potential biases caused by the under- or overrepresentation of specific OSM "highway" classifications. An analysis of per-lane flow values is also provided in the Supplementary materials (Figure S2).

As expected, we observe for cities where both information are available that the average annual weekday traffic is slightly higher than the overall annual average, reflecting reduced traffic volumes on weekends. Both metrics exhibit a broad range, with most values falling between 5,000 and 30,000 vehicles per day. Variations in city flow distributions primarily arise from the under or overrepresentation of high-volume roads. For instance, the high traffic observed in Glasgow can be attributed to its dataset originating from a national database that primarily monitors main roads (80% of its sensors covering motorway, trunk or primary types). Conversely, Toulouse shows generally lower traffic values, due to the absence of major roads in its dataset. Dublin stands out as an exception, displaying high traffic volumes despite the absence of major road types. This may be due to sensor placement at intersections, potentially capturing traffic from multiple directions. Finally, Valencia displays notably higher traffic values than other cities. We observe that its sensors are mostly attributed to high OSM categories, although other cities are in the same situation and do not show such high traffic volume values. The per-lane analysis in Supplementary Figure S2 indicates values closer to those of other cities.

## Usage Notes

A key application of our dataset is conducting statistical comparisons between traffic model outputs and standardized AADT (or AAWT) data from local authorities. This can be achieved by converting modeled traffic values into annual average statistics. This provides a relevant approach to validate the magnitude of traffic estimated through other means.

To achieve that, it is first important to get a common key related to road segments between our validation dataset and the output of a model. This "road segment ID" is highly variable in references used by local or national databases, as well as private entities (e.g. TomTom, Here). Therefore, we proposed map-matching techniques between our different normalized datasets and OSM that is broadly accessible (see Methods).

It is worth noting that some ambiguity may exist regarding traffic directions. In datasets where this information is provided, we noted it in the attributes "raw_oneway" and "raw_direction" (Table 2). This is to be
<figure><img src="image_6.png" /><figcaption>**Fig. 4** Boxplots of Average Annual Daily (grey boxes) and Weekday (white boxes) Traffic (thousands of vehicles per sensor per day) for the 36 cities with standardized data. The data corresponds to the most recent year available for each city (except Rouen and Copenhagen where the second to last year is used). The number of road segments monitored is indicated on the right side of the left panel. Whiskers extend to the 15th and 85th percentiles, the box represents the first and third quartiles, and the bold line inside denotes the median. The right panel displays the distribution of OSM road importance, derived from our map-matching techniques. Major road types are grouped with their corresponding “_link” variants (though only a few were labeled as such). The “Others” category includes the following OSM labels: unclassified, residential, living_street, service, track, path, busway (only present in Marseille, Amsterdam, Toulouse), and escape (only present in Marseille). Monitoring locations with unsuccessful matching are also included under the category “No match”.</figcaption></figure>
compared with the attribute “osm_oneway”. For example, if a city reports traffic in both directions (“raw_oneway = False”) but the matched road segment is marked as “osm_oneway = True”, the corresponding AADT value could potentially be divided by 2 in order to estimate the flow on this specific OSM object. The OSM referential has its limitations, including missing values for certain features (see Supplementary Figure S1). Therefore, it may be beneficial to use a more comprehensive referential, such as a national database or private company ones.

We do not claim full coverage of each city's main road network (Supplementary Table S2). Although the number of traffic sensors is relatively low in some cities (Table 1), we believe that the dataset remains valuable for traffic volume modeling and validation. A previous study²⁵ demonstrated that even limited open sensor data can be effectively used to estimate traffic volumes on unmonitored road segments, as shown in the case of Berlin. This method was subsequently applied to calculate associated on-road emissions, despite the raw data from Berlin consisting of only a few hundred sensors.

## Code availability

The code used to convert raw data into a standardized format is archived on Zenodo²⁴, and the most up-to-date version is maintained on a public GitHub repository (https://github.com/XavB64/traffic-volume-data-EU-cities). The corresponding raw data is also available when storage constraints allow. Additionally, the code used to generate the figures of this study is also provided, except for Fig. 3 where private data was employed.

Received: 24 March 2025; Accepted: 25 July 2025;

Published online: 05 August 2025

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**Author contributions**

X.B. designed the approach, developed the scripts as well as the dataset, and wrote the manuscript. C.G., R.T.M., C.Z. and N.M. helped to design the repository and to find data sources. Q.S. and N.M. tested the relevancy of the dataset for specific use cases. P.C., C.Z., S.J.D. and S.B.A. supervised the project and all authors reviewed the manuscript.

**Competing interests**

The authors declare no competing interests.

**Additional information**

**Supplementary information** The online version contains supplementary material available at https://doi.org/10.1038/s41597-025-05698-y.

**Correspondence** and requests for materials should be addressed to X.B.

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