Abstract
Despite the deployment of more efficient vehicle technologies, global CO2 emissions related to transportation have increased by 250 % between 1970 and 2010 due to a rise of vehicle ownership, traffic volumes and congestion. CO2 is the most common of the anthropogenic greenhouse gas emissions and is a main contributor to climate change. Fine-scaled information on the spatial and temporal distribution of traffic-related CO2 emissions can support decision making processes with regard to emission mitigation measures. For the purpose of providing such information, commonly traffic emission models are applied. However, such models are often restricted in their spatiotemporal resolution due to a lack of adequate input data. A potential data source could be provided by the extended floating car data (xFCD) approach, where vehicle operation parameters like fuel consumption are read out on-trip via the vehicle’s onboard diagnostic system and get correlated with vehicle positions and timestamps at short recording intervals. In this work, the potential of fuel consumption recordings from xFCD for quantifying traffic-related CO2 emissions is evaluated. For this, an extensive database of GNSS-trajectories from vehicles (FCD) and xFCD fuel consumption measurements were recorded in the city of Salzburg, Austria. Using this input data, a set of averaged driving patterns for road segments, 15-min intervals and weekdays was derived. A similarity measurement algorithm was performed on these patterns, so that the most representative vehicle speed profile with fuel consumption recordings could be identified. The results indicate that the elaborated methodology can be applied for calculating representative, plausible and consistent CO2 emission factors from xFCD fuel consumption recordings with high spatial and temporal resolution. This shows the potential of the systematic usage of xFCD for the purpose of estimating traffic related CO2 emissions.
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Pucher, G. (2017). Deriving Traffic-Related CO2 Emission Factors with High Spatiotemporal Resolution from Extended Floating Car Data. In: Ivan, I., Singleton, A., Horák, J., Inspektor, T. (eds) The Rise of Big Spatial Data. Lecture Notes in Geoinformation and Cartography. Springer, Cham. https://doi.org/10.1007/978-3-319-45123-7_5
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