Mesoscopic Emission Estimation for Road Networks
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Solution Overview
Problem
Current methods for estimating vehicle emissions on a road network lack precision and scalability, particularly at fine temporal and spatial scales, due to the limitations of macroscopic models and high computational demands of microscopic models, and fail to account for local infrastructure and driving styles.
Innovation Solution
A method using a mesoscopic model that incorporates vehicle flow rates, driving styles, and thermal states, combined with machine learning, to estimate pollutant emissions on a road network, allowing for precise calculations with limited computing resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If macroscopic models based on emission factors are used to estimate pollutant emissions, then the method is simple and computationally efficient, but the precision of emissions estimation deteriorates at fine temporal and spatial scales
Solution Approach 1:
The patent applies local quality by differentiating emissions estimation between homogeneous and heterogeneous traffic conditions. Macroscopic models are used for homogeneous sections where single emission factors suffice, while microscopic models are applied to heterogeneous sections requiring detailed vehicle dynamics analysis. This selective application optimizes both computational efficiency and precision according to local traffic characteristics.
Solution Approach 2:
The road network is segmented into multiple sections based on traffic flow characteristics, topography, and infrastructure features. Each section is classified as homogeneous or heterogeneous, allowing appropriate model selection. This segmentation enables the system to maintain high computational efficiency for majority sections while achieving high precision for critical heterogeneous sections.
2Measurement precision
If microscopic models are used to provide instantaneous emission rates based on vehicle dynamics, then the precision of emissions estimation improves, but the computational time and resource requirements increase significantly
Solution Approach 1:
The patent implements a dynamic model selection approach where the computational model is adapted based on real-time traffic conditions. During homogeneous flow conditions, computationally efficient macroscopic models are used. During heterogeneous conditions with frequent acceleration, deceleration, and idling, microscopic models are activated to capture transient emissions behavior, thus optimizing the balance between precision and computational time.
Solution Approach 2:
The system changes computational parameters by adjusting the level of model detail based on traffic condition parameters. When traffic conditions indicate homogeneity (steady flow, uniform speed), the system uses simplified emission factors. When conditions indicate heterogeneity (variable speed, frequent stops), the system transitions to detailed microscopic modeling with full vehicle dynamics parameters, thereby adapting computational resources to actual needs.
3Ease of operation
If a single emission factor is used for a road section, then the method is simple to implement, but the ability to represent contrasting emission levels in different traffic conditions deteriorates
Solution Approach 1:
The patent creates a universal emissions estimation framework that can handle both simple homogeneous cases and complex heterogeneous cases. The system first assesses traffic condition homogeneity and automatically selects the appropriate modeling approach, making the system universally applicable to all road sections regardless of traffic complexity, while maintaining ease of operation through automated model selection.
Solution Approach 2:
The system applies partial action by using simplified emission factors for the majority of homogeneous road sections where full microscopic modeling would be excessive. For heterogeneous sections where single emission factors are insufficient, the system applies the more detailed microscopic modeling approach. This partial application of complex methods only where necessary maintains implementation simplicity while improving adaptability for contrasting conditions.
Data Source
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AI summary
The present invention relates to a method for determining the quantity of pollutants (Q) emitted by at least one vehicle on a section of a road network, in which an average speed (VIT) of the vehicles on the sections of the road network is acquired, and a flow rate model (MDV) is used to determine the flow rate of vehicles on the sections of the road network, and a unitary pollutant emission model (MUE) is used to determine a quantity of pollutant emissions from a single vehicle. Finally, for all vehicles in the road network, an aggregated quantity (Q) of the pollutants emitted is determined using the determined flow rate (Db), the determined unitary quantity (Qu), as well as the distribution of driving styles (DSC) and the thermal state of the vehicles (DTH).