Transit surveys based on on-board passenger interviews suffer from bias. Most commonly observed is the short trip bias: passengers travelling short distances are underrepresented in survey results. Biased data leads to an incorrect estimation of passenger demand can result in an inequitable allocation of revenues between transport operators. This paper examines how the short trip bias can be mitigated during the data ex-trapolation process. Four methods are examined: A simple extrapolation by boarding counts, three iterative proportional fitting models and an additional weighting concept are tested on simulated survey data. The simulative approach enables the evaluation of the examined methods concerning their effects in reducing short trip bias. A total of eight survey situations with selected parameters variated are simulated to allow conclusions about influencing factors. Results suggest that the most effective method is the weighting approach, followed by the iterative proportional fitting methods. Within the class of the iterative propor-tional fitting methods no significant difference is observed. Furthermore it is observed that the effectiveness of the weighting approach strongly relates to passenger numbers and selection rates. Furthermore an overview on topic related literature is given to examine practical approaches to reduce bias in survey data.:ABBILDUNGSVERZEICHNIS VII TABELLENVERZEICHNIS VII ABKÜRZUNGSVERZEICHNIS VIII SYMBOLVERZEICHNIS IX 1 EINLEITUNG 1 2 ANFORDERUNGEN AN VERKEHRSERHEBUNGEN 5 2.1 Einnahmeaufteilung im SPNV 5 2.2 Aufbau von Verkehrserhebungen 8 2.2.1 Zweistufige Stichprobenziehung 8 2.2.2 Felderhebung: Zählung und Befragung 10 2.2.3 Datenaufbereitung 11 2.2.4 Hochrechnung und Auswertung 11 2.3 Problem der Stichprobenverzerrung 14 2.3.1 Präzision und Genauigkeit 14 2.3.2 Untererfassung von Kurzstreckenfahrern 15 2.3.3 Weitere Verzerrungsursachen 16 3 LITERATURÜBERBLICK: ANSÄTZE ZUR PROBLEMVERMEIDUNG 19 3.1 Hochrechnungsverfahren 19 3.1.1 Iterative Randsummenverfahren (IPF) 19 3.1.2 Gewichtungsverfahren 21 3.2 Weitere Ansätze 24 3.2.1 Platzgruppenverfahren 24 3.2.2 Anpassung der Startlösung des Hochrechnungsverfahrens 25 3.2.3 Veränderung der Auswahlprozedur der Fahrgäste 27 3.2.4 Veränderung des Erhebungsdesigns 28 4 METHODIK 31 4.1 Auswahl der Verfahren 31 4.1.1 Einsteigerhochrechnung 31 4.1.2 Fratarverfahren 32 4.1.3 Durchschnittsfaktormethode 32 4.1.4 MULTI-Verfahren 33 4.1.5 Gewichtungsverfahren von Keppeler und Schulze 34 4.2 Simulationsdaten 36 4.2.1 Fahrtdaten Linie 1 37 4.2.2 Fahrtdaten Linie 2 38 4.2.3 Befragungsszenarien 39 4.3 Gestaltung der IPF-Verfahren 40 4.3.1 Weitere Randbedingungen 40 4.3.2 Abbruchkriterien 42 4.3.3 Bester Iterationsschritt 44 4.3.4 Aufstellung der Startmatrix 44 4.4 Anwendung des Gewichtungsverfahrens 45 5 ERGEBNISSE 49 5.1 Charakteristik der Befragungsstichproben 49 5.2 Aggregierte Ergebnisse 51 5.3 Einfluss der Befragungsquoten 53 5.4 Verteilung der Verkehrsleistung nach Tarif 54 5.5 Konvergenzverhalten 55 6 DISKUSSION 57 6.1 Ergebnisinterpretation 57 6.2 Einordnung und Schlussfolgerungen 59 7 FAZIT UND AUSBLICK 63 EHRENWÖRTLICHE ERKLÄRUNG XVII

Eine Vielzahl von Wissenschaftlern und Wissenschaftlerinnen befassen sich mit der Unfalltheorie und den darauf einflussnehmenden Faktoren, mit dem Ziel die Straßenverkehrssicherheit für alle Verkehrsteilnehmende zu verbessern. So wird auch in der vorliegenden Arbeit der Einfluss der Regionen (Unterschied zwischen Ost- und Westdeutschland) und des Verkehrskontextes (Bundesautobahnen, außerhalb und innerhalb von Ortschaften) auf die Häufigkeit von Straßenverkehrsunfällen, differenziert nach der Unfallschwere, untersucht. Nach einer eigenständigen Recherche der auf die jeweiligen Bundesländer und Straßentypen differenzierten Fahrleistungen, welche für das Jahr 2015 in Deutschland, erbracht worden sind, werden mit Hilfe von Regressionsmodellen Risikokennziffern für die differenzierten Häufigkeiten von Verunglückten (Getöteten, Schwerverletzten, Leichtverletzten) ermittelt. Diese wurden mit der Statistiksoftware R durchgeführt. Abschließend ist zu sagen, dass die Aussagen für Unfälle mit getöteten Personen nicht für Unfälle mit schweroder leichtverletzten Personen gelten und die Bundesautobahn nach den Risikokennziffern der sicherste Straßentyp ist. Schlussendlich werden die erhaltenen Kennziffern diskutiert.:INHALTSVERZEICHNIS I ABBILDUNGSVERZEICHNIS III TABELLENVERZEICHNIS IV FORMELVERZEICHNIS V ABKÜRZUNGSVERZEICHNIS VI ABSTRACT VIII 1 EINLEITUNG 1 2 DESKRIPTIVE ANALYSE DES DATENSATZES 3 2.1 RECHTSGRUNDLAGE 4 2.2 BEGRIFFSERKLÄRUNG 5 2.2.1 Unfälle 5 2.2.2 Beteiligte 6 2.2.3 Verunglückte 7 2.2.4 Unfallursachen 7 2.2.5 Unfallarten und Unfalltypen 8 2.2.5.1 Unfallarten 8 2.2.5.2 Unfalltypen 8 3. LITERATURÜBERBLICK 10 3.1 PALMERA-SUAREZ, LOPEZ-CUADRADO ET AL. 10 3.2 WANG, QUDDUS AND ISON 12 3.3 TAYLOR, LYNAM AND BARUYA14 4. VORGEHENSWEISE 17 4.1 ERMITTLUNG DER DIFFERENZIERTEN FAHRLEISTUNGEN 19 4.1.1 DTV-tägliche Verkehrsstärken 20 4.1.2 Hochrechnung auf das Jahr 2015 21 4.2 VERWENDETE PROGRAMME 24 5. DURCHFÜHRUNG 25 5.1 VORBETRACHTUNG 25 5.1.1 Erstellen der Datenmatrizen 25 5.1.2 Streudiagramm 27 5.1.3 Bestimmtheitsmaß 31 5.1.4 F-Test 31 II 5.2 REGRESSION: GETÖTETE AUF BUNDESAUTOBAHNEN 32 5.3 REGRESSION: GETÖTETE AUßERHALB VON ORTSCHAFTEN 38 5.4 REGRESSION: GETÖTETE INNERHALB VON ORTSCHAFTEN 40 5.5 REGRESSION: SCHWERVERLETZTE AUF BUNDESAUTOBAHNEN 42 5.6 REGRESSION: SCHWERVERLETZTE AUßERHALB VON ORTSCHAFTEN 45 5.7 REGRESSION: SCHWERVERLETZTE INNERHALB VON ORTSCHAFTEN 46 5.8 REGRESSION: LEICHTVERLETZTE AUF BUNDESAUTOBAHNEN 49 5.9 REGRESSION: LEICHTVERLETZTE AUßERHALB VON ORTSCHAFTEN 51 5.10 REGRESSION: LEICHTVERLETZTE INNERHALB VON ORTSCHAFTEN 54 6. ERGEBNISSE 56 7. DISKUSSION 58 7.1 GETÖTETE 59 7.2 SCHWERVERLETZTE 60 7.3 LEICHTVERLETZTE 61 8. SCHLUSSBETRACHTUNG 62 LITERATURVERZEICHNIS 65 ERKLÄRUNG ZUR URHEBERSCHAFT 68

Airport regulation regimes are under revision in many countries. The decision about the extent of airport regulation is based on an economic analysis of market power, which is done in two steps. The first step involves defining the relevant markets the airport is operating on. This in turn is based on an economic analysis of the particular circumstances of the airport, and must be compatible with competition law. The second step consists of the evaluation of the airport's competitive position in all identified markets. Due to industry particularities, many diverse issues must be taken into account in this process, such as questions of upstream and downstream market interaction, airport congestion, peak-load pricing, or offsetting bargaining power. Many of these questions have been theoretically analyzed in the industrial organization literature, but have only rarely been applied in practical competition analysis with respect to airports. This paper builds a bridge between the theoretical insights and their practical application to airport regulation policy. We derive the principles for a sound economic analysis of the market power of airports, given the time and data constraints encountered in practice by the regulatory authorities and other involved parties, and propose a general framework for the analysis of market power of airports. We focus on hub airports, as the analysis of these gateways covers all the relevant issues, including countervailing power of the hub operator.

Considering the effectiveness of the WTO and global governance in general as insufficient to overcome growing problems in governing a globalizing world, I pursue four objectives. By shedding light on the changes in the environment of the WTO and their implications for the working of the WTO, I first want to underpin the case for structural reforms. My second, and central, aim is to recommend a more effective structure for the WTO. Thirdly, I draw general lessons for global governance from the example of the WTO. And finally, I assess the adequacy of my innovative research design. The research design is characterized by a broad analytical framework that traces how regional integration among nation states and legalization of international institutions affect bargaining and enforcement of international agreements; additionally, it considers trends that affect the WTO. The interdisciplinary theoretical framework combines insights from the fields of international relations, international law, and international economics, and builds upon rationalist and constructivist perspectives.

Air traffic has an increasing influence on climate; therefore identifying mitigation options to reduce the climate impact of aviation becomes more and more important. Aviation influences climate through several climate agents, which show different dependencies on the magnitude and location of emission and the spatial and temporal impacts. Even counteracting effects can occur. Therefore, it is important to analyse all effects with high accuracy to identify mitigation potentials. However, the uncertainties in calculating the climate impact of aviation are partly large (up to a factor of about 2). In this study, we present a methodology, based on a Monte Carlo simulation of an updated non-linear climate-chemistry response model AirClim, to integrate above mentioned uncertainties in the climate assessment of mitigation options. Since mitigation options often represent small changes in emissions, we concentrate on a more generalised approach and use exemplarily different normalised global air traffic inventories to test the methodology. These inventories are identical in total emissions but differ in the spatial emission distribution. We show that using the Monte Carlo simulation and analysing relative differences between scenarios lead to a reliable assessment of mitigation potentials. In a use case we show that the presented methodology can be used to analyse even small differences between scenarios with mean flight altitude variations.

Knowledge workers (KW), as important individual agents who embody, exchange, create and exploit knowledge, contribute to regional competitiveness and growth. To attract and retain them in a region, it is necessary to have a better understanding of their fundamental spatially-related behaviors including residence, workplace, and commute choices. In this study, we depart from a perspective of knowledge typology (analytical-synthetic-symbolic knowledge base) to investigate the heterogeneity of knowledge workers' residence, workplace, and commute choices. The case study was conducted in the metropolitan region of Munich. Various types of data are integrated: structural statistical and individually-based web-survey data; individuals' actual choices and their assessment of importance for each criterion; positional and relational data. We find that symbolic Advanced-Producer-Services (APS) workers tend to reside in central areas and use public transport or active modes to commute. In contrast, synthetic high-tech workers are found in relatively peripheral areas and depend more on cars to reach their workplaces. The spatially-related choices of analytical high-tech and synthetic-APS workers are positioned in between symbolic APS-workers and synthetic high-tech workers. We reach three conclusions: Firstly, the features of the knowledge base are evident in the spatial choices of knowledge workers. Secondly, there is a consistency of characteristics between interrelated spaces surrounding residence, workplace, as well as along the commute path of knowledge workers. Lastly, while the influence of the knowledge base has to be weighed against socio-demographic factors, different groups of knowledge workers clearly display distinct choices of residential location and commute mode. These conclusions may provide insights for urban planners and policy-makers regarding the attraction and retention of knowledge workers.

Plug-in electric vehicles (PEV) can reduce greenhouse gas emissions. However, the utility of PEVs, as well as reduction of emissions is highly dependent on daily vehicle kilometres travelled (VKT). Further, the daily VKT by individual passenger cars vary strongly between days. A common method to analyse individual daily VKT is to fit distribution functions and to further analyse these fits. However, several distributions for individual daily VKT have been discussed in the literature without conclusive decision on the best distribution. Here we analyse three two-parameter distribution functions for the variation in daily VKT with four sets of travel data covering a total of 190,000 driving days and 9.5 million VKT. Specifically, we look at overall performance of the distributions on the data using four goodness of fit measures, as well as the consequence of choosing one distribution over the others for two common PEV applications: the days requiring adaptation for battery electric vehicles and the utility factor for plug-in hybrid electric vehicles. We find the Weibull distribution to fit most vehicles well but not all and at the same time yielding good predictions for PEV related attributes. Furthermore, the choice of distribution impacts PEV usage factors. Here, the Weibull distribution yields reliable estimates for electric vehicle applications whereas the log-normal distribution yields more conservative estimates for PEV usage factors. Our results help to guide the choice of distribution for a specific research question utilising driving data and provide a methodological advancement in the application of distribution functions to longitudinal driving data.

Potential users of plug-in electric vehicles often ask for public charging facilities before buying vehicles. Furthermore, the speed of public charging is often expected to be similar to conventional refueling. For this reason, research on and political interest in public charging focus more and more on fast charging options with higher power rates, yet estimates for future needs are rare. This paper tries to fill this gap by analyzing current charging behavior from a large charging data set from Sweden and Norway and take the findings to calibrate a queuing model for future fast charging infrastructure needs. We find that the ratio of battery electric vehicles to public fast charging points can be similar to other alternative fuels in the future (close to one fast charging point per 1000 vehicles for high power rates of 150 kW). In addition, the surplus on the electricity prices for payoff is only 0.05–0.15 €/kWh per charging point. However, charging infrastructure needs highly depend on battery sizes and power rates that are both likely to increase in the future.

We use regression analysis to study what motivates car pool managers to campaign for BEV procurement using primary data from 229 car pool managers including adopters and non-adopters of EVs. Key findings are that a personal interest in EVs due to technophilia increases the intention to start procurement initiatives for BEVs. These findings underpin the fact that the attitudes of single individuals can influence internal organizational decision processes and therefore play an important role in explaining BEV adoption in commercial fleets. Other factors that foster initiatives for BEV procurement are organizational innovativeness, and the expectation of environmental benefits and positive effects on employee motivation. The fear of mobility constraints and doubts about the reliability of BEVs counteract the intention to campaign for their procurement.

This paper studies the impacts on flight trajectories, such as lateral and vertical changes, when avoiding the formation of persistent contrails for transatlantic flights. A sophisticated Earth-System Model (EMAC) coupled with a flight routing submodel (AirTraf) and a contrail submodel (CONTRAIL) is used to optimize flight trajectories concerning the flight time and the flight distance through contrail forming regions (contrail distance). All the trajectories are calculated taking into account the effects of the actual and local meteorological parameters, e.g., wind, temperature, relative humidity, etc. A full-year simulation has been conducted based on a daily flight schedule of 103 transatlantic flights. The trade-off between the flight time and contrail distance shows a large daily variability, meaning for the same increase in flight time, the reduction in contrail distance varies from 20% to 80% depending on the daily meteorological situation. The results confirm that the overall changes in flight trajectories follow a seasonal cycle corresponding to the nature of the potential contrail coverage. In non-summer seasons, the southward and upward shifts of the trajectories are favorable to avoid the contrail formation. In summer, the northward and upward shifts are preferred. A partial mitigation strategy for up to 40% reduction in contrail distance can be achieved throughout all the seasons with a negligible increase in flight time (less than 2%), which represents a reasonable trade-off between flight time increase and contrail avoidance.