Road Traffic Dynamic Pollutant Emissions Estimation: From Macroscopic Road Information to Microscopic Environmental Impact
Abstract
:1. Introduction
- The proposed modeling framework is able to estimate time-varying and microscopic road traffic emissions by only using easily obtainable macroscopic topographic and traffic information on any given geographical area;
- A thorough model validation is able to highlight the model accuracy and the drastic emissions estimation error when compared to well-established macroscopic emissions models, such as COPERT [32]. In addition, our model is able to identify critical road links in terms of pollutant emissions at a very high spatial resolution. Such a precision is very valuable to understand what road segments should need careful investigation and perhaps infrastructure modification, as well as to feed atmospheric dispersion and air quality models with high-resolution emission sources.
2. Materials and Methods
2.1. Driving Behavior Model
2.1.1. Road Network Segmentation
2.1.2. Driving Behavior Categorization
- Very high congestion: The congestion level, which is the ratio between the traffic speed and the free-flow speed, is below a defined threshold.
- Traffic light: Traffic light presence in the middle road link of the triplet.
- No priority: Existence of stop or yield sign in the middle road link of the triplet. It is also the case if the destination road link has lower functional importance than the other downstream road links.
- Priority—Major and intermediate segments: The destination road link has more functional importance than the other downstream road links. The triplet is a motorway, a major road link, or a secondary road link with high volume traffic.
- Priority—Minor segments: The destination road link has more functional importance than the other downstream road links. The triplet is a minor road link with low volume traffic and low-speed limitation.
- High curvature: The triplet curvature is above a defined threshold.
- Low curvature: The triplet curvature is below a defined threshold. Then, three categories are defined based on their functional importance within the Transportation Network.
2.1.3. Clustering of Driving Styles
2.1.4. Driving Characteristics Extraction
2.1.5. Stochastic Speed Construction
2.2. Microscopic Emissions Model
2.2.1. Vehicle Model
2.2.2. Engine Fuel Consumption Model
- Maximum torque curve and air-path architecture are known for the engine;
- Generic law for the friction mean effective pressure (FMEP), as a function of engine speed;
- Constant gross indicated efficiency;
- Fuel air equivalence ratio equal to 1 in spark-ignition engines (except at high load where it increases linearly with load), and varying between two values for compression–ignition engines;
- The exhaust gas recirculation (EGR) fraction is known for each point of the engine map.
- The engine coolant temperature is modeled using a simple heat exchange model. This model takes into account the heat produced by the combustion which is assumed to be a function of the engine operating point and the ambient heat exchange. Cold start effect on fuel consumption is then modeled with a coefficient function of the coolant temperature.
2.2.3. Engine-Out Emissions Model
2.2.4. After-Treatment Model
2.3. Microscopic Traffic Emissions
3. Results
3.1. Driving Behavior Model Validation
3.2. Microscopic Emissions’ Model Validation
3.2.1. Impact of the Driving Behavior
3.2.2. Impact of the Trip
3.2.3. Impact of the Vehicle
3.3. Microscopic Traffic Emissions
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Acknowledgments
Conflicts of Interest
References
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Feature Description |
---|
Congestion level |
Traffic light |
Stop sign |
Yield sign |
Road network hierarchy |
Road link curvature |
Feature Description |
---|
Mean speed |
Percentage of null speed |
75% percentile of speed |
Minimum speed |
Sum of positive acceleration |
Sum of negative acceleration |
Difference between initial and final speed |
Feature | Description |
---|---|
Number of lanes in the road link | |
Origin road link traffic speed | |
Center road link traffic speed | |
Destination road link traffic speed | |
Nominal speed | |
road link length | |
Slope angle | |
Curvature angle | |
Number of adjacent origin road links | |
Number of adjacent destination road links |
Error | |||
---|---|---|---|
Mean absolute error | 2.22 km/h | 2.26 km/h | 2.32 km/h |
Relative error | 8 % | 8.5 % | 9 % |
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De Nunzio, G.; Laraki, M.; Thibault, L. Road Traffic Dynamic Pollutant Emissions Estimation: From Macroscopic Road Information to Microscopic Environmental Impact. Atmosphere 2021, 12, 53. https://doi.org/10.3390/atmos12010053
De Nunzio G, Laraki M, Thibault L. Road Traffic Dynamic Pollutant Emissions Estimation: From Macroscopic Road Information to Microscopic Environmental Impact. Atmosphere. 2021; 12(1):53. https://doi.org/10.3390/atmos12010053
Chicago/Turabian StyleDe Nunzio, Giovanni, Mohamed Laraki, and Laurent Thibault. 2021. "Road Traffic Dynamic Pollutant Emissions Estimation: From Macroscopic Road Information to Microscopic Environmental Impact" Atmosphere 12, no. 1: 53. https://doi.org/10.3390/atmos12010053
APA StyleDe Nunzio, G., Laraki, M., & Thibault, L. (2021). Road Traffic Dynamic Pollutant Emissions Estimation: From Macroscopic Road Information to Microscopic Environmental Impact. Atmosphere, 12(1), 53. https://doi.org/10.3390/atmos12010053