Smart Cities are urban environments in which, the stream of data coming from very different observational networks is managed and integrated in order to efficiently improve public services and the life quality of the citizens. Focusing on smart mobility, we present as proof of concept the case study of Crema (in collaboration with SIMET – Gruppo ENERCOM), where a coupled traffic and air quality measurement system was developed to real-time monitoring the traffic composition (vehicles categories) and its related emission of pollutants. The developed tool represents a data-driven decision-making support that can be used for urban planning, traffic optimization and pollution control.

1. Traffic management in
a Smart City scenario
Exploiting real time data to improve
urban planning and air quality
SFScon, Bolzano 11-12/11/2022
Gianluca Antonacci
CISMA Srl c/o NOI Techpark, Bolzano
gianluca.antonacci@cisma.it
In collaboration with:
SIMET – (ENERCOM group)

2. Smart Cities
●
Improving public services and life quality with data
management
Focusing on smart mobility
●
Real-time traffic and air-quality monitoring system
coupled with real-time pollutant emission estimation.
●
Data-driven decision-making support for urban planning,
traffic optimization and pollution control.
Introduction

3. The presented work is a proof of concept of a «smart city»
application, exploiting existing sensors and data streams,
concatenating them and inserting additional features where
necessary.
●
The starting point: traffic and air quality low cost sensors
installed in a city
●
The problem: how do we fully exploit gathered data and
estimate the local contribution of traffic on air pollution?
●
The P.o.C.: putting together data streams and adding small
pieces of software we can create value added information
Smart city platform – a proof of concept
+ + =

4. ●
Case study site: Crema, Lombardia, Italy
●
Two-lane high-traffic carriageway in
commercial area (~15000 veh/day)
●
Traffic sensors (radar) on each lane
●
Air quality sensor (PM10)
Case study description - Crema

5. 1)Monitoring traffic data and air pollution
2)Estimating traffic emissions in real-time based on traffic
data:
1)NOx
2)PM10
3)CO2
3) Coupling traffic, emissions and air pollution time series –
estimate contribution of vehicular emission to overall
pollution
4)Implementing of a traffic and emissions database potentially
useful as a support for sustainable mobility planning.
Case study target

6. 1) HW: Traffic sensors (number of vehicles every 5 minutes
on each lane)
2) HW: Air quality sensors (PM10)
3) Dataset: Vehicle fleet composition
●
vehicle class (buses, cars, motorcycle, …)
●
EURO class, Fuel (gasoline / diesel)
4) SW: COPERT (www.emisia.com) emission computation
5) SW: R & SQLite → statistics, storage & API
Tools (HW & data & SW)

7. Fleet composition

8. Real-time data-processing and storage:
Procedural diagram

9. Traffic sensor
24 GHz radar motion detectors
●
Vehicle detection: up to 30 m,
backward and forward directions.
●
Integrated signal processing ,efficient
interference suppression, vibration
suppression.
Air quality sensor
Laser scattering
●
Concentration of PM1 PM2.5, PM10
Source: Wi4B TAI sensors User Manual
Data are available
through API in json
format every 5
minutes
Real time traffic measurement

10. EF=
A v2
+Bv+C+D/v
Ev
2
+F v+G
COPERT algorithm
(COmputer Programme to calculate Emissions from Road Traffic – www.emisia.com)
Emission Factor: pollutant released
by a single vehicle in one kilometre
[g/km].
where:
●
A, B, C, D, E, F, G: coefficients
depending on the pollutant, vehicle
class, fuel and EURO class.
●
v: vehicle speed
Total Emissions by vehicle
category:
Emission Factor * Number of Vehicles
Emission calculation

11. Data are stored in a SQLite database, easily accessible in R
language
1)Raw data
●
Traffic (vehicles every 5 minutes)
●
Air quality (PM1, PM2.5, PM10 concentration)
2)Processed data
●
Traffic emissions (NOx, PM10, CO2 emissions [g/km] for
every vehicle category)
●
Daily statistics on traffic, air quality and emissions (max,
min, mean, std.dev., daily sum)
Time series of traffic, air quality and emissions are
coupled.
Database storage

12. Exposing API with plumbeR
HOW-TO web API using R
→ bitbucket.org/giaaan/rapi/
R code to expose data trough
API
Requirements:
●
RSQLite to request data
from database
●
Lubridate to manage date
and time
●
PlumbeR to create a web
API

13. Emission / concentration time-series
Emission
(calculated,
traffic-related)
Air pollution
(directly
measured)

14. REPLICATION
●
Low replication effort.
●
Ease of implementation over any measurement system
that exposes compatible traffic and air quality data through
API.
SCALABILITY
●
Easy multiple nodes management within the same location.
●
Unique fleet composition for different nodes in the same
location.
●
For other locations, different fleet composition must be
evaluated.
Replication & scalability

15. 1) A real-time system coupling traffic and pollution data has been
developed
2) It provides both historical data collection and real-time data.
●
Historical data → long-term Data-driven support for urban planning
●
Real-time data → short-term traffic and pollution management
3) It is often not necessary to build stand-alone solutions from the ground
up (with high costs), but it is cost-effective and time-saving to make the
most of existing features to create value-added results
4) In our opinion, a modern smart city application should have the
characteristics of modularity and easy exploitation of information
Conclusion

16. Thanks for your attention
Gianluca Antonacci, PhD
CISMA Srl, NOI techpark, Bolzano
gianluca.antonacci@cisma.it
SFScon, Bolzano 11-12/11/2022