Establishing a Standardised National Data System for Evaluating Road Maintenance Emissions in Sweden
2024 (English)Independent thesis Advanced level (degree of Master (One Year)), 10 credits / 15 HE credits
Student thesis
Abstract [en]
A trustworthy and consistent data system is crucial for monitoring and reducing carbon emissions from road maintenance operations. Developing a national data reporting system requires technical support and a systemic plan involving multiple stakeholders to implement the standard. In Sweden, Trafikverket, the Swedish transport agency, recently initiated a project that proposed a solution based on the BEAst standard and outlined the current data collection methods for road maintenance. The BEAst standard is an agreed industry-driven information standard that promotes machine-readable information communication, effectively reduces costs, and increases efficiency by streamlining communication within the industry. This is to address the critical need for a trustworthy data system to monitor and reduce carbon emissions from road maintenance operations. Although the datasystem has high potential to identify the sources of carbon emissions and create mitigation measures by precisely gathering fuel use data throughout operations and maintenance activities. There are many challenges in integrating data from diverse sources into a consistent system revealed several obstacles, including differences in CO2 emissions reported by different systems, human factors affecting data quality,and limited access to cloud services. To address these challenges, this study proposes a new data reporting mechanism which requires a detailed specification of reporting parameters covering content, format, resolution, and reporting frequency using BEAst standards.
Place, publisher, year, edition, pages
2024.
Keywords [en]
Road maintenance, Emissions, Data system, BEAst standard, Data visualization, Data analysis, Simulation, Data standards, Sustainable transportation
National Category
Information Systems
Identifiers
URN: urn:nbn:se:du-48525OAI: oai:DiVA.org:du-48525DiVA, id: diva2:1858027
Subject / course
Microdata Analysis
2024-05-152024-05-15