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Emission estimation based on traffic models and measurements
Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0003-2767-9415
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
Series
Linköping Studies in Science and Technology. Licentiate Thesis, ISSN 0280-7971 ; 1835
National Category
Transport Systems and Logistics
Identifiers
URN: urn:nbn:se:liu:diva-155771DOI: 10.3384/lic.diva-155771ISBN: 9789176850923 (print)OAI: oai:DiVA.org:liu-155771DiVA, id: diva2:1299107
Presentation
2019-04-04, K3, Kåkenhus, Campus Norrköping, Norrköping, 13:15 (English)
Opponent
Supervisors
Available from: 2019-03-26 Created: 2019-03-26 Last updated: 2019-04-24Bibliographically approved

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Emission estimation based on traffic models and measurements(5276 kB)193 downloads
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Tsanakas, Nikolaos

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Citation style
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