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SFScon22 - Gianluca Antonacci - Traffic management in a Smart City scenario.pdf

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SFScon22 - Gianluca Antonacci - Traffic management in a Smart City scenario.pdf

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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.

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.

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SFScon22 - Gianluca Antonacci - Traffic management in a Smart City scenario.pdf

  1. 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. 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. 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. 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. 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. 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. 7. Fleet composition
  8. 8. Real-time data-processing and storage: Procedural diagram
  9. 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. 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. 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. 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. 13. Emission / concentration time-series Emission (calculated, traffic-related) Air pollution (directly measured)
  14. 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. 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. 16. Thanks for your attention Gianluca Antonacci, PhD CISMA Srl, NOI techpark, Bolzano gianluca.antonacci@cisma.it SFScon, Bolzano 11-12/11/2022

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