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.

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http://dx.doi.org/10.3384/lic.diva-155771 under the license https://creativecommons.org/licenses/by-nc/4.0/
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Published on 01/01/2019

Volume 2019, 2019
DOI: 10.3384/lic.diva-155771
Licence: Other

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