| 1. |
- Tsanakas, Nikolaos, 1987-, et al.
(författare)
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Emission estimation based on cross-sectional traffic data
- 2017
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Ingår i: Prceedings of TAP 2017 22nd International Transportation and Air Pollution Conference. - : EMPA. ; , s. 1-15
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Konferensbidrag (refereegranskat)abstract
- 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....
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| 2. |
- Tsanakas, Nikolaos, et al.
(författare)
-
Emission estimation based on cross-sectional traffic data
- 2017
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Ingår i: Proceedings of the 22nd International Transportation and Air Pollution Conference, 2017.
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Konferensbidrag (refereegranskat)abstract
- The paper outlines as follows: Section 2 consists a literature review on methods for emission estimations based on sensors measurements. Section 3 provides a description of the methodology of estimating emissions from cross-sectional data by either using AADT estimation techniques or more sophisticated traffic estimators. Section 4 presents the case study that is a part of the E4 motorway in Stockholm, and provides details about the data collection. The results are presented in Section 5 and finally Section 6 concludes the study and discusses future work.
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| 3. |
- Tsanakas, Nikolaos, et al.
(författare)
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Reduction of errors when estimating emissions based on static traffic model outputs
- 2017
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Ingår i: Transportation Research Procedia. - : Elsevier. - 2352-1465. ; 22, s. 440-449, s. 440-449
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Tidskriftsartikel (refereegranskat)abstract
- The rapid growth of traffic congestion has led to an increased level of emissions and energy consumption in urban areas. Well designed infrastructure and traffic controllers along with more efficient vehicles and policy measures are required to mitigate congestion and thus reduce transport emissions. In order to evaluate how changes in the traffic system affect energy use and emissions, traffic analysis tools are used together with emission models. In large urban areas emission models mainly rely on aggregated outputs from traffic models, such as the average link speed and flow. Static traffic models are commonly used to generate inputs for emission models, since they can efficiently be applied to larger areas with relatively low computational cost. However, in some cases their underlying assumptions can lead to inaccurate predictions of the traffic conditions and hence to unreliable emission estimates. The aim of this paper is to investigate and quantify the errors that static modeling introduces in emission estimation and subsequently considering the source of those errors, to suggest and evaluate possible solutions. The long analysis periods that are commonly used in static models, as well as the static models' inability to describe dynamic traffic flow phenomena can lead up to 40 % underestimation of the estimated emissions. In order to better estimate the total emissions, we propose the development of a post processing technique based on a quasi-dynamic approach, attempting to capture more of the excess emissions created by the temporal and spatial variations of traffic conditions
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