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Traffic Road Emission Estimation Through Visual Programming Algorithms and Building Information Models: A Case Study
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AI
DOI：10.1109/ACCESS.2021.3123565.png)
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Abstract
En 中文
Emissions from transportation have a severe impact on the current climate crisis. Therefore, the estimation of these pollutants requires precise measurements that integrate both traffic and vehicle fleet information within a specific country or area. However, the current estimation tools continue using vehicle fleet standards based on recommendations or local studies. A problem for the current estimation models arises due to the difficulty of centralizing the large number of vehicle statistics. This article has taken advantage of the capabilities of both visual programming tools and building information modeling (BIM) to centralize databases from different sources, generating a model that integrates current traffic data and vehicle fleet statistics. The proposed platform estimates emissions and the carbon footprint using TIER 1 emission factors recommended by the European Environmental Agency (EEA). This platform has been successfully applied to a case study to estimate the carbon footprint of the B-20 road in Barcelona, using current vehicle restriction scenarios. This case study presents a maximum difference of -2.72% compared with the estimations made by another similar report. This proposed platform more completely automates the communication among the equations and databases required to estimate traffic road emissions.
Keywords:
Estimation
Roads
Buildings
Carbon footprint
Software
Fuels
Urban areas
Building information modeling (BIM)
COPERT
emissions impact assessing
green house gases (GHG)
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W