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.

In most levels of vehicle automation, drivers will not be merely occupants or passengers of automated vehicles. Especially in lower levels of automation, where the driver is still required to serve as a fallback level (SAE L3) or even as a supervisor (SAE L2), there is a need to communicate relevant system states (e.g., that the automated driving system works reliably or that there is a need for manual intervention) via the Human-Machine Interface (HMI). However, there are currently no agreed-upon guidelines that apply specifically to HMIs for automated driving. In this paper, we summarize design recommendations for visual-auditory and visual-vibrotactile HMIs derived from empirical research, applicable standards and design guidelines pertaining to in-vehicle interfaces. On this basis, we derive an initial set of principles and criteria for guiding the development and design of automated vehicle HMIs. A heuristic evaluation methodology consisting of an itemized checklist evaluation that can be used to verify that basic HMI requirements formulated in the guidelines are met is also presented. The heuristic evaluation involves an inspection of the HMI during typical use cases, judging their compliance with the proposed guidelines and documentation of identified instances of non-compliance. Taken together, the combination of the proposed guidelines and the heuristic evaluation methodology form the basis for both design and validation recommendations of automated vehicle HMIs, that can serve the industry in the important evolution of automation within vehicles.

The dynamics of demand responsive ride pooling (DRRP) systems is considered in a mean-field framework. The relevant dimensionless quantities determining the performance and viability of the system are identified. In the presence of an already established dominant market participant with comparable service quality (like, e.g., the private car), the mutual interaction of the actors (i.e., the customers sharing rides) by virtue of the route assignment algorithm gives rise to a discontinuous transition between two strongly different modes of operation. One of them represents the typical (unfavorable) performance of current ride pooling systems, while the other represents a new mode of operation in which virtually all customers use DRRP.