Traffic data can be an invaluable resource for analysing and tackling the critical issue of air quality. Yet, raw traffic data by itself is not sufficient, and requires tools and technologies for assessing, in a data-driven fashion, the impact of traffic on air quality indicators. We have developed a cutting-edge tool capable of estimating real-time vehicle traffic emissions from raw traffic data with remarkable accuracy. This tool can be seamlessly integrated into the OpenDataHub platform, giving users easy access to a comprehensive view of traffic data and of the environmental impact of transportation. Our demonstration of the tool uses traffic data from the Brenner motorway, giving you a look at how the COPERT model (the European standard for vehicle emission calculation) can effectively be used to estimate the levels of primary pollutants and greenhouse gases on the basis of vehicle fleet composition, speed, and vehicular flow. We will also inspire you with examples and ideas on how this tool can be used by both operators and developers to create value-added services and to enable innovative solutions for decision makers and individual citizens.

1. Gianluca Antonacci
CISMA srl
NOI Techpark, Bolzano
gianluca.antonacci@cisma.it
FROM RAW TRAFFIC DATA TO
AIR POLLUTANT EMISSIONS
AN OPEN DATA APPROACH AND
DEMONSTRATOR
OPEN DATA HUB DAY
Bolzano
19-05-2023

2. Pollutants:
● NOx
● CO
● VOC
Air quality - local
WHY MONITORING TRAFFIC EMISSIONS?
Provide better understanding about mobility choices and transportation policies environmental
impacts
Develop more effective policies and regulations to reduce emissions and carbon footprint from
transportation.
Greenhouse gases emissions:
● CO2
Climate change - global
● PM10
● PM2.5

3. REAL-TIME EMISSION ESTIMATION TOOL
Pull traffic data from ODH as input data for the emission model
Pollution connector
COPERT algorithm
Push back emission data to ODH
Emission model
An application for real-time emission estimation, integrated with OpenDataHub
Returns emissions from traffic data

4. POLLUTION CONNECTOR
● Scheduler: scheduling the periodic task to update the
pollution data.
● Pollution Computation Task: handling the
computation of a new batch of measures.
Downloading the traffic data, computing the pollution
measuring and uploading them to OpenDataHub.
● TrafficODHConnector: downloading and parsing
traffic data from ODH.
● PollutionODHConnector: handling the upload and
the download of the Pollution Measures from ODH.
● PollutionComputationModel: comuting the new
pollution data from the traffic data.

5. POLLUTION CONNECTOR
1. Scheduler starts the execution of a new Pollution Computation Task.
2. Download of the list of available TrafficSensor stations.
3. Download of the latest pollution data available for each station, lane,
and class of vehicle.
4. Identify the new traffic data to download.
5. The TrafficODHConnector downloads the new batch of traffic data
using the starting point identified in the previous step.
6. The PollutionComputationModel computes the new pollution
measures and returns them to the main task.
7. Finally, the main task uploads the new PollutionMeasures to the ODH
using the PollutionODHConnector.

6. EMISSION MODEL
COPERT ALGORITHM
COmputer Programme to calculate
Emissions from Road Traffic
www.emisia.com
● REAL-TIME:
○ transits (10 minutes total)
○ speed (10 minutes average)
● SEMI-STATIC:
○ fleet composition
○ COPERT coefficients
○ Road geometry
INPUT
● Emissions data [g/km]
○ NOx
○ CO
○ VOC
○ CO2
○ PM
OUTPUT
Compute emissions for each vehicle category and each measurement station.

7. EMISSION MODEL
COPERT ALGORITHM
Subdivision of vehicle in type
of vehicle according to the
fleet composition
Classification in EURO class
and fuel
Emission Factors
Total Emissions
Emission Factor [g/km/vehicle]:
pollutant released by a single vehicle in one kilometre.
Where:
● A, B, C, D, E, F, G coefficients depending on the
pollutant, vehicle class, fuel, EURO class and road
slope.
● v: vehicle speed
Total Emissions by vehicle category [g/km]:
Emission Factor * Number of Vehicles

8. A22-BRENNER HIGHWAY CASE STUDY
OpenDataHub provides traffic data from multiple traffic stations along the A22 Brenner highway.
The emission estimation tool is based on data from a set of 20 selected traffic stations.

9. A22-BRENNER HIGHWAY CASE STUDY
Altri dati o statistiche per mostrare che
informazioni si possono estrarre e che abbiano un
impatto maggiore rispetto ad una semplice serie
temporale di emissioni?
Penso ad esempio:
● totale giornaliero emissioni di CO2 di tutta
l’autostrada…
● contributo del traffico pesante sul totale (NOx,
CO2)....

10. A22-BRENNER HIGHWAY CASE STUDY
Altri dati o statistiche per mostrare che
informazioni si possono estrarre e che abbiano un
impatto maggiore rispetto ad una semplice serie
temporale di emissioni?
Penso ad esempio:
● totale giornaliero emissioni di CO2 di tutta
l’autostrada…
● contributo del traffico pesante sul totale (NOx,
CO2)....

11. A22-BRENNER HIGHWAY CASE STUDY
Altri dati o statistiche per mostrare che
informazioni si possono estrarre e che abbiano un
impatto maggiore rispetto ad una semplice serie
temporale di emissioni?
Penso ad esempio:
● totale giornaliero emissioni di CO2 di tutta
l’autostrada…
● contributo del traffico pesante sul totale (NOx,
CO2)....

12. ACCESS DATA THROUGH API
OPEN DATA HUB SWAGGER DOCUMENTATION:
https://swagger.opendatahub.com/?url=https://mobility.api.opendatahub.com/v2/apispec
curl -X 'GET'
'https://mobility.api.opendatahub.com/v2/flat%2Cnode/TrafficSensor/LIGHT_VEHICLE-CO2-emiss
ions/2023-05-01/2023-05-10?limit=200&offset=0&shownull=false&distinct=true&timezone=UTC'
-H 'accept: application/json'
library(bzar)
type <- ‘Traffic’
station <- ‘A22:679:1’
variable <- ‘LIGHT_VEHICLES-CO2-emissions’
start <- '2023-05-01'
end <- '2023-05-10'
data <- bzar.get_data(type, station, variable, start, end, 1, user, password)
R
bash
The A22 data is currently only available to authorized collaborators. If you are interested in accessing this data, please contact help@opendatahub.com.

13. WHAT ABOUT OTHER CONTEXTS?
Replicability in other contexts: what do I need?
traffic station(s) – at least vehicles count and speed
acquisition and data storage system – from traffic station to database
software interface – e.g. API
Applicability in urban contexts. Pay attention to:
Fleet composition: identifying the actual composition of the local fleet
may be not so easy.
Unchannelized flow in urban hilly context: what slope value is better
representative? Remarkable if heavy traffic is significant.

14. WHAT’S NEXT?
What can you do with emission data?
● Estimate carbon footprint of the whole road axis and/or a single vehicle category (e.g. heavy traffic).
● Assess the pollutants/carbon reduction over the years caused by fleet modernization/electrification or traffic
and transportation policies.
● Manage traffic speed based on emission values and patterns for air quality purposes.
● Get input data for air quality modeling and data analysis.
● …

15. Thank
you!
…or you can contact me at:
gianluca.antonacci@cisma.it
Any questions or ideas
are welcome!