Estimating CO2 Emissions from IoT Traffic Flow Sensors and Reconstruction
1.1. Literature Review
1.2. Paper Aim and Structure
- Allows the CO2 produced by vehicles in a city to be estimated, which is a relevant contribution of CO2 produced in cities, directly measuring the impact of vehicle population on the production of CO2, thus increasing the precision in measuring CO2. The advantage of identifying the function from traffic flow to CO2 is that it can be used to estimate the CO2 in city areas based on knowing the traffic flow with a certain precision, and thus the total CO2 production of the city, which currently can only be coarsely guessed by using a very limited number of CO2 sensors, whereas most cities have hundreds of traffic flow sensors.
- Is based on (i) the identification of the relationships from the measured traffic flow and determining the emissions factors, taking into account different traffic behaviours, from fluid traffic to “stop-and-go” conditions (congested and uncongested traffic situations); (ii) the changes in the emissions factor at different periods of the year; (iii) an approach to statistical validation by means of the CO2 measurements taken from specific sensors and traffic flow data, allowing for an assessment of the precision of the indirect estimation of CO2 on the basis of traffic flow.
- Provides a solution for computing CO2 emissions locally in the city and not only for specific vehicles or globally at city level, as mentioned above in the literature review.
2. Data Description and Related Problems
- Vehicular traffic flow: number of vehicles crossing the supervised location during a given period of time (which is usually referred to in terms of hours, that is, #cars/h);
- Vehicular average speed: average speed of the vehicles crossing the supervised location (measured in km/h);
- Vehicular density: number of vehicles in terms of road occupancy (measured in #cars/km).
- Travel time: average time that vehicles take to transit the supervised area (reported in s).
3. Traffic Flow Data Analysis
- Measured vehicular traffic flow, denoted by at a given timestamp , is normalized with respect to the number of lanes, denoted by , of the road of the location. Thus, the normalized vehicular traffic flow at a given timestamp , denoted by , is given by ;
- Measured travel time, denoted by at a given timestamp , is normalized with respect to the minimum travel time, denoted by , occurring in the absence of congestion in the sensor location. can be defined as the travel time needed to cross the area at the speed limit of the observed segment. Thus, the normalized travel time at a given timestamp , denoted by , is given by .
4. Traffic Flow Modalities: Congested and Uncongested
5. Traffic Flow Reconstruction into CO2 Sensor Locations
6. Computing CO2 Emissions Factors from Traffic Flow Data and Modalities
6.1. Time Alignment of Traffic Flow and Measured CO2 Data
6.2. Pollutant Data Type
6.3. From Traffic Flow Data to CO2
- is the amount of in a volumetric section of the road segment at a given time t, where:
- is the measurement of CO2 from the sensor in in the time interval (these values are measured by the CO2 sensors);
- is the area in which the sensor collects the values in and it is estimated on the road segments close to the CO2 sensor location. More precisely, we have:
- is the road length corresponding to the amount of m or Km performed by the vehicles in that specific area of the CO2 sensor, supposing that the vehicles change neither road nor behaviour in the segment.
- Contribution coming from vehicles/cars moving in uncongested conditions:
- is the traffic count in uncongested conditions in terms of #cars in the time interval. This can be measured on the basis of traffic sensors and/or estimated as solutions of the above-presented LWR PDE via the traffic reconstruction model (Section 5);
- is an emissions factor to be determined, which is the amount of gCO2/km per car in uncongested conditions.
- Contribution coming from vehicles/cars moving in congested conditions:
- is the traffic count in congested conditions in terms of #cars in the time interval. This can be measured on the basis of traffic sensors and/or estimated as solutions of the above-presented LWR PDE via the traffic reconstruction model);
- is an emissions factor to be determined, which is the amount of gCO2/km per car in congested conditions.
- If , then and ;
- If , then and .
- is the traffic density (#cars/km) as observed on the traffic sensors and/or estimated as solutions of the above-presented LWR PDE via traffic flow reconstruction;
- is the number of road lanes;
- and are the average vehicular speeds in z-th location in the cases of uncongested and congested situations, respectively, if the specific velocity cannot be measured or reconstructed. For example, is the vehicular speed in the case of a traffic congestion situation, and we assume that the value of varies from 0 to 5 km/h in accordance with the road characteristics at the z-th location.
6.4. Assessing Seasonal Changes of Estimations: Experimental Results
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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|Mean Flow Rate||100||250||400||550||700||850||950|
|Traffic Flow Sensors||Inflection Point|
(T-TH OBS) on
|Traffic Flow (#Cars/h)||Traffic Density (#Cars/Km)|
|All city sensors||438||215.8||255||7.24|
|S1 (near SMART27)||479||366||366.88||7.12|
|S2 (near SMART28)||536||186.14||216||6.35|
|S3 (near SMART29)||487||234.24||240||4.21|
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Bilotta, S.; Nesi, P. Estimating CO2 Emissions from IoT Traffic Flow Sensors and Reconstruction. Sensors 2022, 22, 3382. https://doi.org/10.3390/s22093382
Bilotta S, Nesi P. Estimating CO2 Emissions from IoT Traffic Flow Sensors and Reconstruction. Sensors. 2022; 22(9):3382. https://doi.org/10.3390/s22093382Chicago/Turabian Style
Bilotta, Stefano, and Paolo Nesi. 2022. "Estimating CO2 Emissions from IoT Traffic Flow Sensors and Reconstruction" Sensors 22, no. 9: 3382. https://doi.org/10.3390/s22093382