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Tsanakas, N. (2019). Emission estimation based on traffic models and measurements. (Licentiate dissertation). Linköping: Linköping University Electronic Press
Open this publication in new window or tab >>Emission estimation based on traffic models and measurements
2019 (English)Licentiate thesis, monograph (Other academic)
Abstract [en]

Traffic congestion increases travel times, but also results in higher energy usage and vehicular emissions. To evaluate the impact of traffic emissions on environment and human health, the accurate estimation of their rates and location is required. Traffic emission models can be used for estimating emissions, providing emission factors in grams per vehicle and kilometre. Emission factors are defined for specific traffic situations, and traffic data is necessary in order to determine these traffic situations along a traffic network. The required traffic data, which consists of average speed and flow, can be obtained either from traffic models or sensor measurements.

In large urban areas, the collection of cross-sectional data from stationary sensors is a costefficient method of deriving traffic data for emission modelling. However, the traditional approaches of extrapolating this data in time and space may not accurately capture the variations of the traffic variables when congestion is high, affecting the emission estimation. Static transportation planning models, commonly used for the evaluation of infrastructure investments and policy changes, constitute an alternative efficient method of estimating the traffic data. Nevertheless, their static nature may result in an inaccurate estimation of dynamic traffic variables, such as the location of congestion, having a direct impact on emission estimation. Congestion is strongly correlated with increased emission rates, and since emissions have location specific effects, the location of congestion becomes a crucial aspect.

Therefore, the derivation of traffic data for emission modelling usually relies on the simplified, traditional approaches. The aim of this thesis is to identify, quantify and finally reduce the potential errors that these traditional approaches introduce in an emission estimation analysis. According to our main findings, traditional approaches may be sufficient for analysing pollutants with global effects such as CO2, or for large-scale emission modelling applications such as emission inventories. However, for more temporally and spatially sensitive applications, such as dispersion and exposure modelling, a more detailed approach is needed. In case of cross-sectional measurements, we suggest and evaluate the use of a more detailed, but computationally more expensive, data extrapolation approach. Additionally, considering the inabilities of static models, we propose and evaluate the post-processing of their results, by applying quasi-dynamic network loading.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2019. p. 131
Linköping Studies in Science and Technology. Licentiate Thesis, ISSN 0280-7971 ; 1835
National Category
Transport Systems and Logistics
urn:nbn:se:liu:diva-155771 (URN)10.3384/lic.diva-155771 (DOI)9789176850923 (ISBN)
2019-04-04, K3, Kåkenhus, Campus Norrköping, Norrköping, 13:15 (English)
Available from: 2019-03-26 Created: 2019-03-26 Last updated: 2019-04-24Bibliographically approved
Tsanakas, N., Ekström, J. & Olstam, J. (2017). Emission estimation based on cross-sectional traffic data. In: Prceedings of TAP 2017 22nd International Transportation and Air Pollution Conference: . Paper presented at 22nd International Transportation and Air Pollution Conference, Zürich, Switzerland, 15-16 November 2017 (pp. 1-15). EMPA
Open this publication in new window or tab >>Emission estimation based on cross-sectional traffic data
2017 (English)In: Prceedings of TAP 2017 22nd International Transportation and Air Pollution Conference, EMPA , 2017, p. 1-15Conference paper, Published paper (Refereed)
Abstract [en]

The continuous traffic growth has led to highly congested cities, with negative environmental effects, both related to air quality and climate change. According to the European Environment Agency, transportation remains a significant contributor to the total emissions of the main air pollutants, (EEA, 2016). Specifically, Nitrogen Oxides (NOx), Carbon Oxide (CO) and fine particulate matter (PM2.5) make up 32%, 23% and 8% of the total emissions, respectively. This vigorous impact of vehicular emissions to the urban environmental air quality, raises concerns over the impact of traffic on human health. Therefore, the effective implementation of emission reducing policies, such as traffic control measures or congestion pricing, becomes crucial for many European cities in order to meet the air quality standards and mitigate the human exposure to pollution. To quantify the environmental effects of these measures and demonstrate their effectiveness, a reliable estimation of pollutants concentrations through emission and dispersion modelling is needed....

Place, publisher, year, edition, pages
EMPA, 2017
National Category
Civil Engineering Transport Systems and Logistics
urn:nbn:se:liu:diva-147873 (URN)
22nd International Transportation and Air Pollution Conference, Zürich, Switzerland, 15-16 November 2017
Förbättrad prognos av energianvändning och emissioner vid styrmedelsanalys i vägtrafiken
Swedish Energy Agency, 38921-1
Available from: 2018-05-17 Created: 2018-05-17 Last updated: 2019-05-10Bibliographically approved

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