This study presents a model that explains the traffic fatality by exploring the Poisson regression model using two types of explanatory variables – referred to as internal and external factors. Internal factors contain variables closely linked to traffic safety, such as speed limits and belt usage (Strandroth et al., 2012), whereas external factors comprise a set of variables that the Swedish Transport Administration cannot control, such as the economy and demographic change (Wiklund et al., 2012). The purpose of the study is to evaluate the impact that internal and external factors have on the traffic fatality. This is done by modeling the traffic fatality using internal factors and then assessing the contribution of adding external factors in the regression model with a forward variable selection strategy. This study uses Swedish traffic fatality data as monthly statistics. The main characteristics of the data are that fatalities have generally decreased with time. Also, the data is characterized by a long term cyclical pattern as well as a yearly cyclical pattern. For the purpose of modeling the impact of internal factors, a model inspired by Brüde (1995) has been adopted, using the variable time as the only explanatory variable. It is concluded that internal factors can be used to significantly explain the general trend of the development of traffic fatalities. The variables chosen to represent external factors were economic development, traffic exposure, demographic development and seasonal trend. The study concludes that the variables economic development, traffic exposure and demographic development significantly contribute to explain the long term cyclical trends, indicating that traffic fatality is a complex multivariate system where no single variable can solely explain its dynamics. The external factor seasonal trend has the most impact of the examined external factors and explains the yearly cyclical pattern by itself. The model presented in this study shows high explanatory power and overall good fit to fatality data, making it a promising tool for statistical analysis of factors contributing to fatality. Especially for the Swedish Transport Administration, the impact of external factors can be evaluated statistically. This study leaves room for further research to assess the impact of additional external factors as well as evaluating the model’s predictive power, both of interest to the Swedish Transport Administration. / Denna studie presenterar en modell som förklarar dödsfall i trafiken genom tillämpning av Poissonregression där två typer av förklaringsvariabler använts – interna och externa faktorer. Interna faktorer innefattar variabler som är direkt knutna till trafiksäkerhet, såsom hastighetsbegränsningar och användande av säkerhetsbälte (Strandroth et al., 2012). Externa faktorer är variabler som Trafikverket inte kan kontrollera, såsom landets ekonomi och demografiska förändringar (Wiklund et al., 2012). Syftet med denna studie är att evaluera påverkan av interna och externa faktorer på dödsfall i vägtrafik. Detta görs genom att analysera hur väl interna faktorer förklarar dödsfall i vägtrafik och sedan undersöka förbättringen av att införa externa faktorer som förklaringsvariabler genom användande av en forward variable selection-strategi. Denna studie använder månatlig data över dödsfall i svensk trafik. Dessa data karaktäriseras av en nedgående trend. Dynamiken av dödsfall visar på ett långt cykliskt mönster samt ett kortare, årligt mönster. I syfte att modellera påverkan av interna faktorer har en modell inspirerad av Brüde (1995) tillämpats. Denna modell använder enbart variabeln tid som förklaringsvariabel. Studien konstaterar att interna faktorer kan användas för att signifikant beskriva en generell trend för utvecklingen av dödsfall i vägtrafik. Variablerna som har valts att representera externa faktorer är ekonomisk utveckling, trafikarbete, demografi samt en säsongstrend. Studien konstaterar att variablerna ekonomisk utveckling, trafikarbete och demografi beskriver det långa cykliska mönstret, vilket tyder på att dödsfall i vägtrafik är av komplex natur och kan inte beskrivas av en ensam variabel. Den externa faktorn säsongstrend förbättrar modellen mest av de externa faktorerna och kan ensam förklara det kortsiktiga cykliska mönstret. Den modell som presenteras i denna studie har hög förklaringsgrad och en överlag bra modellanpassning, vilket gör den till ett lovande verktyg för statistisk analys av faktorer bidragande till dödsfall i trafiken. Modellen är av särskilt intresse för Trafikverket då den tillåter statistisk utvärdering av externa faktorers påverkan. Denna studie lämnar utrymme för framtida forskning att utvärdera påverkan av ytterligare externa faktorer samt att evaluera modellens förmåga att prognostisera framtida antal dödsfall i vägtrafik, vilka båda är intresseområden för Trafikverket.

For most kind of analyses in the field of traffic planning, there is a need for origin--destination (OD) matrices, which specify the travel demands between the origin and destination nodes in the network. This thesis concerns the OD-matrix estimation problem, that is, the calculation of OD-matrices using observed link flows. Both time-independent and time-dependent models are considered, and we also study the placement of link flow detectors. Many methods have been suggested for OD-matrix estimation in time-independent models, which describe an average traffic situation. We assume a user equilibrium to hold for the link flows in the network and recognize a bilevel structure of the estimation problem. A descent heuristic is proposed, in which special attention is given to the issue of calculating the change of a link flow with respect to a change of the travel demand in a certain pair of origin and destination nodes. When a time-dimension is considered, the estimation problem becomes more complex. Besides the problem of distributing the travel demand onto routes, the flow propagation in time and space must also be handled. The time-dependent OD-matrix estimation problem is the subject for two studies. The first is a case study, where the conventional estimation technique is improved through introducing pre-adjustment schemes, which exploit the structure of the information contained in the OD-matrix and the link flow observations. In the second study, an algorithm for time-independent estimation is extended to the time-dependent case and tested for a network from Stockholm, Sweden. Finally, we study the underlying problem of finding those links where traffic flow observations are to be performed, in order to ensure the best possible quality of the estimated OD-matrix. There are different ways of quantifying a common goal to cover as much traffic as possible, and we create an experimental framework in which they can be evaluated. Presupposing that consistent flow observations from all the links in the network yields the best estimate of the OD-matrix, the lack of observations from some links results in a relaxation of the estimation problem, and a poorer estimate. We formulate the problem to place link flow detectors as to achieve the least relaxation with a limited number of detectors.

In order to improve the air quality in Uppsala, a proposition to introduce one of two new emission zones (EZ), starting in the year 2020, has been proposed. In what is called Environment Zone Class 2 (EZ2), only cars that meet emission class Euro 5 and higher are allowed and in Environment Zone Class 3 (EZ3), only electric, fuel cell and gas vehicles are allowed. The purpose of this thesis is to examine how EZ: s would affect the air quality, regarding nitrogen oxides (NOx) and particles (PMx), within the zone of the city of Uppsala. Using the traffic simulation software PTV Vissim and the emissions modeling software EnViver, four scenarios have been created, two representing today's fleet of vehicles and two examining a modified fleet. Scenario 1 examines an exclusion of all non EZ2 vehicles (Euro 4 and lower) within the zone and scenario 2 examines an EZ2 solely on the road Kungsgatan. Scenario 3 and 4 examine an EZ2 and EZ3 where all cars that do not currently meet the requirements for each EZ are replaced with ones that do. The results indicate that all proposals, except scenario 2, lead to a reduction of NOx and PM2 within the zone. Scenario 1 shows a decrease by 51% for NOx and 57% for PM10, scenario 3 shows a decrease by 17% and 24% respectively and scenario 4 shows a decrease by 66% and 43% respectively. For scenario 2 the emissions show an increase by 10% and 7% each within the zone.