The ‘hydrogen economy’ represents an energy system in which hydrogen and electricity are the dominant energy carriers for use in transportation applications. The ‘hydrogen economy’ minimizes the use of fossil fuels in order to lower the environmental impact of energy use associated with urban air pollution and climate change. An integrated energy system is required to deal with diverse and distributed energy generation technologies such a wind and solar which require energy storage to level energy availability and demand. A distributed ‘energy hub’ is considered a viable concept in envisioning the structure of an integrated energy system. An energy hub is a system which consists of energy input/output, conversion and storage technologies for multiple energy carriers, and would provide an interface between energy producers, consumers, and the transportation infrastructure. Considered in a decentralized network, these hubs would form the nodes of an integrated energy system or network. In this work, a model of a clean energy hub comprising of wind turbines, electrolyzers, hydrogen storage, a commercial building, and a fleet of plug-in fuel cell vehicles (PFCVs) was developed in MATLAB, with electricity and hydrogen used as the energy carriers. This model represents a hypothetical commercial facility which is powered by a renewable energy source and utilizes a zero-emissions fleet of light duty vehicles. The models developed herein capture the energy and cost interactions between the various energy components, and also calculate the CO2 emissions avoided through the implementation of hydrogen economy principles. Wherever possible, similar models were used to inform the development of the clean energy hub model. The purpose of the modelling was to investigate the interactions between a single energy hub and novel components such as a plug-in fuel cell vehicle fleet (PFCV). The final model reports four key results: price of hub electricity, price of hub hydrogen, total annual costs and CO2 emissions avoided. Three scenarios were analysed: minimizing price of hub electricity, minimizing total annual costs, and maximizing the CO2 emissions avoided. Since the clean energy hub could feasibly represent both a facility located within an urban area as well as a remote facility, two separate analyses were also conducted: an on-grid analysis (if the energy hub is close to transmission lines), and an off-grid analysis (representing the remote scenarios). The connection of the energy hub to the broader electricity grid was the most significant factor affecting the results collected. Grid electricity was found to be generally cheaper than electricity produced by wind turbines, and scenarios for minimizing costs heavily favoured the use grid electricity. However, wind turbines were found to avoid CO2 emissions over the use of grid electricity, and scenarios for maximizing emissions avoided heavily favoured wind turbine electricity. In one case, removing the grid connection resulted in the price of electricity from the energy hub increasing from $82/MWh to $300/MWh. The mean travel distance of the fleet was another important factor affecting the cost modelling of the energy hub. The hub’s performance was simulated over a range of mean travel distances (20km to 100km), and the results varied greatly within the range. This is because the mean travel distance directly affects the quantities of electricity and hydrogen consumed by the fleet, a large consumer of energy within the hub. Other factors, such as the output of the wind turbines, or the consumption of the commercial building, are largely fixed. A key sensitivity was discovered within this range; the results were ‘better’ (lower costs and higher emissions avoided) when the mean travel distance exceeded the electric travel range of the fleet. This effect was more noticeable in the on-grid analysis. This sensitivity is due to the underutilization of the hydrogen systems within the hub at lower mean travel distances. It was found that the greater the mean travel distance, the greater the utilization of the electrolyzers and storage tanks lowering the associated per km capital cost of these components. At lower mean travel distances the utilization of the electrolyzers ranged from 25% to 30%, whereas at higher mean travel distances it ranged from 97% to 99%. At higher utilization factors the price of hydrogen is reduced, since the cost recovery is spread over a larger quantity of hydrogen.

Responsible for one in eight tonnes of national CO₂ emissions, the passenger car sector is pivotal to delivering on UK climate change commitments to avoiding warming of more than 2°C. This thesis provides a clear and quantitative framing of emissions reduction at the sectoral level, by disaggregating global cumulative emissions budgets and pathways associated with a range of probabilities of exceeding 2°C. The relatively low level of abatement currently planned for the UK car sector, it is argued, needs to be significantly increased for the following reasons: (i) a scientific basis in cumulative emissions for sectoral mitigation makes carbon budgets, rather than end point targets (e.g. 2050), of the first importance; (ii) the currently high probability (63%) of exceeding 2°C underpinning the current UK carbon budgets is inconsistent with the UK government’s commitment to avoiding ‘dangerous climate change’; (iii) short-term emissions growth in industrialising countries considerably reduces remaining emissions space for industrialised countries; (iv) very limited scope exists for any large sector to cut emissions by less than the national mean rate of decarbonisation at higher rates of mitigation (around 10% p.a. by the 2020s). The consequences for emissions space in other sectors if international aviation and shipping mitigate less than the mean are quantified. For UK car sector emissions to remain consistent with a low probability of exceeding 2°C while observing these limitations, this analysis finds that planned sectoral mitigation over the coming decade needs to be increased fourfold. Means to address this expected abatement shortfall using readily available technology are investigated using a fleet emissions model to compare the effect on cumulative emissions of changes in a range of fleet parameters (including mean new car bulk emissions factors, vehicle age-proportionate annual distance travelled, and rates of fleet growth and turnover). Pushing existing car technology to the limit of expected short term efficiency gains is found to be insufficient to deliver a pathway with better than 56% probability of exceeding 2°C. Without reduction in aggregate demand for vehicle kilometres in the short term, lower probabilities of 2°C are placed beyond reach. The possibility of rapid step changes in levels of per capita car use is explored in qualitative interviews using narrative storyline scenarios. A range of coercive and voluntary interventions is considered in relation to their potential to overcome the structural and behavioural constraints to rapid transformation of personal travel.

Reliable forecasting models are needed to achieve the climate related goals in the face of increasing transport demand. Such models can predict the long-term behavioural response to policy interventions, including infrastructure investments, and thus provide valuable pre-dictions for decision makers. Contemporary forecasting models are mainly based on national travel surveys. Unfortunately, the response rates of such surveys have steadily declined, implying that the respondents become less representative of the whole population. A particular weakness is that it is likely that respondents with a high valuation of time are less willing to respond to surveys (because they have less time available for such), and therefore there is a high chance that they are underrepresented among the respondents. The valuation of time plays an important role for the cost benefit analyses of public policies including transport investments, and there is no reliable way of controlling for this uneven sampling of time preferences. Fortunately, there is simultaneously an increase in the number of signals sent between mobile phones and network antennae, and research has now reached the point where it is possible to determine not only the travel destination but also the travel mode based on mobile phone network antennae connections. The aim of this thesis is to investigate if and how mobile phone network data can be used to estimate transportation mode choice demand models that can be used for forecasting and planning. Key challenges with using this data source in the context of mode choice models are identified and met. The identified challenges include uncertainty in the choice variable, the difficulty to distinguish car and bus trips, and the lack of information about the trip purpose. In the first paper we propose three possible model formulations and analyse how the uncertainty in the choice outcome variable would play a role in the different model formulations. We also conclude that it is indeed possible to estimate mode choice demand models based on mobile phone network data, with good results in terms of behavioural interpretability and significance. In the second paper we estimate models using a nested logit structure to account for the difficulty in separating bus and car, and a latent class model specification to meet the challenge of having an unknown trip purpose. / <p><strong>Funding agencies:</strong> The research in this thesis has mainly been funded by the research projects DEMOPAN and DEMOPAN-2 within the research program Transportekonomi at The Swedish Transport Administration.</p>

The sale of electrically assisted bicycles (‘e-bikes’) is growing at a rapid rate across Europe. Whereas market data is available describing sales trends, there is limited understanding of the experience of early adopters of e-bike technology. This paper investigates the motives for e-bike purchase, rider experience and perceived impact on mobility, health and wellbeing through in-depth interviews with e-bike owners in the Netherlands and the UK. Findings revealed that the motive for purchasing e-bikes was often to allow maintenance of cycling against a backdrop of changing individual or household circumstances. E-bikes also provided new opportunities for people who would not otherwise consider conventional cycling. Perceptions of travel behaviour change revealed that e-biking was replacing conventional cycling but was also replacing journeys that would have been made by car. There was also a perception that e-biking has increased, or at least allowed participants to maintain, some form of physical activity and had benefitted personal wellbeing. Technological, social and environmental barriers to e-biking were identified. These included weight of bicycle, battery life, purchase price, social stigma and limitations of cycle infrastructure provision. Additional research is necessary to quantify actual levels of mode substitution and new journey generation among new e-bike owners and the impact of e-biking on promoting physical health and mental wellbeing.

Data about bicycle usage is an important input parameter for several purposes. They are used to describe changes towards more sustainable transport, and partly to say something about changes towards more active transport as opposed to passive modes oftransport. Importantly such data are used as the denominator when calculating crash risk: for cyclists. In Norway, as in most countries, these data are captured in several ways today. This is partly done by using data from the national travel behavior survey, partly using figures from stationazy or mobile bicycle counters, and partly using other methods such as manual counts, etc. The technological development has provided several new opportunities to register such travel, in the form of more advanced stationary counters, advanced algorithms that interprets signal data, video recording solutions and app-based measurement systems. At the sam.e time, we see that development in the transport sector also creates new challenges. In just a few years, electric scooters have radically changed the traffic picture in cities and towns in Norway. There is therefore a need for more knowledge about different forms of ways to measure bicycle and micro-mobility use, their strengths and weaknesses, and what kind of strategies the authorities should have to be equipped to meet future changes in the transport field, as exemplified by the recent intlux of e-scooters. The current paper aims to respond to these challenges by answering the following research questions: • What are the relative strengths and weaknesses of different data sources for measuring cycling and micromobility use? • How weil do the different sources function to capture micromobility and to differentiate between traditional cycling and micromobility? • How can the different data sources be used as input for calculating crash risk for various forms of soft mobility (i.e. cycling and micromobility)?

The present study examines a Mobility Management measure called “test traveller project”, which aims at increasing the public transport modal share by offering free public transport tickets to those who often use their car for their daily commuting and trips. The existing literature consists of a rather limited number of cases and their scope is usually limited because only some of the main elements that influence one’s travel behaviour are considered in each study. Furthermore, literature is not unanimous in concluding that this measure can reduce car use. This work studies more than 50 cases in Sweden, and by employing the Theory of Planned Behaviour the effects of test traveller projects have been examined in an empirical case in the Swedish municipality of Botkyrka, located in the Stockholm metropolitan area. The findings underline that a test traveller project, despite its limitations, may be a valid and relatively simple tool available to public bodies and public transport companies for enticing a segment of car drivers to switch to public transport where it is a valid alternative. In fact, according to the literature, the major results achievable are around 20% of new public transport users among test travellers, whereas in Sweden 20% has been achieved by the first upper quartile of the projects. In particular, the effectiveness of a test traveller project is greater when combined or conducted in parallel with other measures such as improvements in the public transport offer and/or changes in the transport system aiming at disadvantaging car use. The case study of Botkyrka has confirmed that attitudes are the major influencing factor when making the transport mode choice. Further, it has confirmed that environmental concerns and the time passed from one’s residential relocation also play an important role. Habits seem to be less important, thus adhering to that literature whose authors argue that an external event (such as moving home) makes people reflect upon and rethink their travel habits. The case study in Botkyrka has empirically demonstrated how the project participants correct their beliefs and perceptions about public transport, sometimes in a positive way and sometimes in a negative way. An interesting finding is the existence of a new category of people living in the suburbs. Literature indicates that, in the same suburban context, individuals with suburban land use preferences tend to use the car more that individuals with urban land use preferences. In the case study of Botkyrka clearly emerged as a majority among the test traveller project participants a category of individuals who have a suburban land use preference but at the same time would like to use public transportation instead of their car and have high environmental concerns.

Reliable forecasting models are needed to achieve the climate related goals in the face of increasing transport demand. Such models can predict the long-term behavioural response to policy interventions, including infrastructure investments, and thus provide valuable pre-dictions for decision makers. Contemporary forecasting models are mainly based on national travel surveys. Unfortunately, the response rates of such surveys have steadily declined, implying that the respondents become less representative of the whole population. A particular weakness is that it is likely that respondents with a high valuation of time are less willing to respond to surveys (because they have less time available for such), and therefore there is a high chance that they are underrepresented among the respondents. The valuation of time plays an important role for the cost benefit analyses of public policies including transport investments, and there is no reliable way of controlling for this uneven sampling of time preferences. Fortunately, there is simultaneously an increase in the number of signals sent between mobile phones and network antennae, and research has now reached the point where it is possible to determine not only the travel destination but also the travel mode based on mobile phone network antennae connections. The aim of this thesis is to investigate if and how mobile phone network data can be used to estimate transportation mode choice demand models that can be used for forecasting and planning. Key challenges with using this data source in the context of mode choice models are identified and met. The identified challenges include uncertainty in the choice variable, the difficulty to distinguish car and bus trips, and the lack of information about the trip purpose. In the first paper we propose three possible model formulations and analyse how the uncertainty in the choice outcome variable would play a role in the different model formulations. We also conclude that it is indeed possible to estimate mode choice demand models based on mobile phone network data, with good results in terms of behavioural interpretability and significance. In the second paper we estimate models using a nested logit structure to account for the difficulty in separating bus and car, and a latent class model specification to meet the challenge of having an unknown trip purpose. / <p><strong>Funding agencies:</strong> The research in this thesis has mainly been funded by the research projects DEMOPAN and DEMOPAN-2 within the research program Transportekonomi at The Swedish Transport Administration.</p>

The purpose of this study was to determine the travel behaviour of tourists to a ATKV resort, more specifically ATKV-Natalia. Numerous studies on travel behaviour has been done, but none of them indicated that it was focused on ATKV resorts. Travel behvaiour is concerned with the way in which tourists react towards specific inherent aspects. There are various internal and external factors which influence and determine travel behaviour. Travel motivations and reasons for travel are two of the most important factors influencing travel behaviour. Travel motivations can be defined as internal forces influencing a tourist and reasons for travel can be conceptualised as external forces influencing a tourist to travel. Another important factor influencing travel behaviour is the stage of the family life cycle in which the tourist finds him- or herself. Every stage has its own certain needs and wants. Thus, every stage implies different needs and wants in terms of traveling. The information of this study was gathered by means of a questionnaire. The questionnaire was distributed at ATKV-Natalia Resort during the weekend of 17-19 July 2009, by two. fieldworkers. A total of 159 questionnaires were distributed (one per family). Another 150 questionnaires were distributed by means of email. Respondents were identified by the existing database of ATKV which indicated the tourists who visited the resort during the 2009 December school holidays. Statistical analysis of the data was used to determine the findings of this study. For the first article, factor analyses were done in order to determine the travel motivations and reasons for travel of tourists to ATKV-Natalia. Correlation analysis was also done. in order to determine the correlations which exist between travel motivations and reasons for travel. In the second article, only the data of the respondents who indicated that they were married were used for statistical analysis. Cross tabulations were done in order to determine the most important similarities and differences which exist between tourists married with children and tourists married without children. / Thesis (M.Com. (Tourism))--North-West University, Potchefstroom Campus, 2010

The purpose of this study was to determine the travel behaviour of tourists to a ATKV resort, more specifically ATKV-Natalia. Numerous studies on travel behaviour has been done, but none of them indicated that it was focused on ATKV resorts. Travel behvaiour is concerned with the way in which tourists react towards specific inherent aspects. There are various internal and external factors which influence and determine travel behaviour. Travel motivations and reasons for travel are two of the most important factors influencing travel behaviour. Travel motivations can be defined as internal forces influencing a tourist and reasons for travel can be conceptualised as external forces influencing a tourist to travel. Another important factor influencing travel behaviour is the stage of the family life cycle in which the tourist finds him- or herself. Every stage has its own certain needs and wants. Thus, every stage implies different needs and wants in terms of traveling. The information of this study was gathered by means of a questionnaire. The questionnaire was distributed at ATKV-Natalia Resort during the weekend of 17-19 July 2009, by two. fieldworkers. A total of 159 questionnaires were distributed (one per family). Another 150 questionnaires were distributed by means of email. Respondents were identified by the existing database of ATKV which indicated the tourists who visited the resort during the 2009 December school holidays. Statistical analysis of the data was used to determine the findings of this study. For the first article, factor analyses were done in order to determine the travel motivations and reasons for travel of tourists to ATKV-Natalia. Correlation analysis was also done. in order to determine the correlations which exist between travel motivations and reasons for travel. In the second article, only the data of the respondents who indicated that they were married were used for statistical analysis. Cross tabulations were done in order to determine the most important similarities and differences which exist between tourists married with children and tourists married without children. / Thesis (M.Com. (Tourism))--North-West University, Potchefstroom Campus, 2010

This diploma thesis studies the issue of travel behaviour in the systems with abolished fares of public transport, so called free fare public transport policy (FFPT), which aredominantly influencing the transport system in favour of one means of transport. The main aim of this paper is to figure out how the concept of FFPT is affecting the choice of means of transport and what is the influence of the FFPT on the dynamic in the given transport system. Besides conceptual framing of main theoretical knowledge regarding the issue of travel behaviour and development of transportation systems, the paper is using analysis of practical implementation of the FFPT concept and also, on the case of Frýdek- Místek, the case study of specifics system with implemented FFPT. The results indicate that introduction of the FFPT is effective way how to influence travel behaviour in the favour of public transport and to change conditions in the given transport system, which is then more variable. It's important to mention, FFPT concept does not represents universal tool how to influence development of transport systems while it is just one measure out of many others. Besides of focusing on specifics measurements it is important to systematically using different tools and its complementarity while development of...