Development of Approaches to Updated Database on the Vehicle Fleet of Various Countries for Assessing Gross Greenhouse Gas Emissions

• Published in: 2023 Intelligent Technologies and Electronic Devices in Vehicle and Road Transport Complex (TIRVED) pp. 1 - 5
• Main Authors: Trofimenko, Yu. V., Yakubovich, A. N., Lytov, V. M.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 15.11.2023
• Subjects: Air pollution; classification and attributive features; COPERT 5; data recovery; elements of artificial intelligence; greenhouse gases; model groups; Organizations; relational database; Relational databases; road transport; Roads; Standards organizations; Time series analysis; Uncertainty
• DOI: 10.1109/TIRVED58506.2023.10332670

The vehicular emission modelling software COPERT is extensively used in generating emission levels for National Emissions Inventory in Europe and in some other countries internationally. This software has a wide functionality and a flexible approach to the calculation of greenhouse gas emissions. Thus, COPERT presents more than 373 vehicle categories (groups), each of them differs in classification and attributive characteristics: category, fuel type, Euro emission standard, load capacity and engine capacity. Meanwhile, the practical use of this program is constrained by the need to collect a large volume of structural, technical and operational, road, climatic and other significant indicators used in COPERT as input data. Correctly taken into account the detailing of the fleet reduces uncertainties in the assessment of total gross emissions, which is especially important when assessing the statistical uniformity of time series of emissions. To do this, it is necessary to perform statistical processing and cluster analysis of available databases to form a relational database of all single vehicles. Within the framework of this work, such a relational database is demonstrated, formed on the basis of the Rosstat database (form 1-BDD) and the analytical agency "Autostat". This work is devoted to the development of algorithms and methods for correcting information (noisy data) when they are included in a relational database for all single vehicles that were in the car fleet in the period from 2010 to 2022 (about 60 million units every year), and the implementation of their clustering into COPERT calculated model groups using artificial intelligence elements. The results of this work may be relevant for a number of countries with a lack of representativeness of data in state vehicle accounting systems and seeking to account for greenhouse gas emissions by tier 3 according to the Intergovernmental Panel on Climate Change guidelines. Data flow management, availability of advanced storage systems, organization of information processing taking into account the recovery of missing data - all these key elements are necessary to confirm and verify the input parameters used to calculate greenhouse gas emissions in the COPERT program at the national level.