This thesis proposes a framework to design and analyze sustainability within complex urban transportation systems. Urban transit systems have large variability in temporal and spatial resolution, and are common in lifecycle analyses and sustainability studies. Unlike analyses with smaller scope or broader resolution, these systems are composed of numerous interacting layers, each intricate enough to be a complete system on its own. In addition, detailed interaction with the system environment is often not accounted for in lifecycle studies, despite its strong potential effects on the problem domain. To manage such complexity, this thesis suggests a methodology that focuses on integrating existing modeling constructs in a transparent manner, and capturing structural and functional relationships for efficient model reuse. The Systems Modeling Language (OMG SysML ) is used to formally implement the modeling framework. To demonstrate the method, it is applied to a large scale multi-modal transportation network. Analysis of key network parameters such as emissions output, well-to-wheel energy use, and system capacity are presented in a case study of the Atlanta, Georgia metropolitan area. Results of the case study highlight several areas that differ from more traditional lifecycle analysis research. External influences such as regional electricity generation are found to have extremely large effects on environmental impact of a regional mobility system. The model is used to evaluate various future scenarios and finds that existing policy measures for curbing energy use and emissions are insufficient for reducing impact in a growing urban region.

In this thesis the effects of two future greenhouse gas emissionreducing strategies in the passenger transport sector are investigated.Three factors were modelled for 2021-2055; The life cycle emissions offour vehicle types using a well-to-wheel life cycle analysis tool calledGREET, the growth curve of these vehicle types was analyzed andextrapolated to obtain total vehicle predictions and the mileage ofthese vehicles was extrapolated from existing governmental data. Theresulting scenarios show that in the short term E85 ex fuel vehicles arecapable of more avoided emissions, with EVs outperforming them inthe long term. However limitations in the prediction of vehicle mileageleaves the overtake point to be determined.

The Well-To-Wheels (WTW) methodology is widely used for policy making in the transportation sector. In this paper updated WTW calculations are provided, relying on 2013 statistic data, for the carbon intensity (CI) of the European electricity mix; detail is provided for electricity consumed in each EU Member State (MS). An interesting aspect presented is the calculation of the GHG content of electricity traded between Countries, affecting the carbon intensity of the electricity consumed at national level. The amount and CI of imported electricity is a key aspect: a Country importing electricity from another Country with a lower CI of electricity will lower, after the trade, its electricity CI, while importing electricity from a Country with a higher CI will raise the CI of the importing Country. In average, the CI of electricity used in EU at low voltage in 2013 was 447 gCO2eq/kWh, which is the 17% less compared to 2009. Then, some examples of calculation of GHG emissions from the use of electric vehicles (EVs) compared to internal combustion engine vehicles are provided. The use of EVs instead of gasoline vehicles can save (about 60% of) GHG in all or in most of the EU MSs, depending on the estimated consumption of EVs. Compared with diesel, EVs show average GHG savings of around 50% and not savings at all in some EU MS.