This thesis describes the relation between the tourism industry and crisis from a consumer perspective. The impacts on consumers have been receiving little attention in earlier tourism research. This study contains an empirical research with a focus on the attitudes of travellers during the refugee crisis of 2015. The attitudes are seen as interesting for this study since they can affect the traveller before, during and after the trip. In this work the attitudes further are divided into four different sub-categories, which were detected to be important in order to describe how the attitudes of travellers can be affected from a crisis. The performed study shows that the attitudes can be affected from a crisis in different ways. It also shows that the refugee crisis of 2015 had impacts on travellers, that might vary from person to person and that they might not be aware of themselves.

Taiwanese Travel Behaviors and Intention of Minsu Inn Consumption Abstract Amid the lifestyle trend which increasingly emphasizes leisure and travel, the questions of ¡§what are most important underlying motivation of travel in Taiwanese consumers?¡¨ ¡§what are the characteristics in Taiwanese travelers during vacations?¡¨ and ¡§how much are their intention to try to stay in the currently popular mingsu inns?¡¨ are worthy studying. Focusing on these issues, the study has integrated theories and literature of travel behaviors, proposed a theoretical model for travel behavior study on Taiwanese people, and hereby developed a scale to assessing travel motivations of Taiwanese people and established a model for empirical study in intention of mingsu inns consumption. The mainly participants of the study include professionals and technicians in Taiwan. The sample size is 445. The main results are summarized as follows: (1) Five main factors underlying Taiwanese travel are refreshment, family fusion, knowledge extension, socialization, and self-improvement. (2) The main sources of travel information are family members, media coverage, agency promotion, friends and relatives, and non-profit organization. There is a statistically significant and positive correlation between knowledge extension and agency promotion information source. (3) Major considerations in vacation planning include board and room, transportations, activities, itinerary, Nature access, and shopping. No significant correlation was found between motivations of travel and consideration about itinerary and shopping (4) Many positive and significant correlation were found between any vacation motivations of travel and intention of minsu inns consumption. (5) Negative and significant correlation between age and length of overseas travel was found, while the relationship between age and travel length significantly positive.

This thesis reports on aspects of information exchange in an online network whose members share an interest in travel. The Thorn Tree resembles a real thorn tree on which travellers hang messages. Using social network analysis, the network resulting from members participating in online discussions considers the importance of structure and position in an exchange network to travel information exchanges on the Africa category. Different ways and frequency of participation result in communication patterns giving structure to an exchange network in which participation in a thread determines the presence of a tie between actors. Actors are placed in various relations to others; network analysis makes measurements such as levels of reciprocity, density and centrality possible. At the heart of this study lies an inquiry into the Internet’s impact on society, more so, human interaction in cyberspace where spaces, places and even communities are qualified as being “virtual”. Scholars have different views in this regard. Some commentators claim that the Internet has spawned unique forms of community. The term “virtual communities” suggests new kinds of social interaction, with revolutionary consequences for local and global communication. Online communication could be a substitute for the loss of “traditional” physical communities, or even the cause of their demise. Others, however, praise the Internet for spawning communities even in the physical world. More complex theoretical perspectives are indicative of a need to interrogate the very notions of community and contemporary social networks resultant from the many-to-many communication capabilities of computer-mediated communication (CMC). Apart from community formation on the Internet, the concept community has not been tested among travellers yet. Networks, the ties people form and the exchanges that take place as a result of such ties relate to social capital. The notion of social capital in a computer-mediated environment needs more intense academic scrutiny. Nevertheless, for travellers and destination areas alike, information exchanges can be beneficial. However, not all information exchanges on the Thorn Tree or on the Internet per se are necessarily beneficial since verification is not always possible in a cyber environment. Nevertheless, for travellers with a need for travel information in a sparse network characterised by weak ties, content analysis linked to a network analysis proves that weak ties are beneficial for spreading useful information. On the Africa category discussions are short while threads have very limited life spans. Of the total number of actors in this dataset (1 282), it was found that a few are particularly active, while many contribute a few messages to a limited number of threads and clearly do not interact regularly on the Thorn Tree. / Thesis (DPhil (Information Science))--University of Pretoria, 2007. / Information Science / unrestricted

Aprendizagem por reforço multiagente (do inglês, MARL) é uma tarefa desafiadora em que agentes buscam, concorrentemente, uma política capaz de maximizar sua utilidade. Aprender neste tipo de cenário é difícil porque os agentes devem se adaptar uns aos outros, tornando o objetivo um alvo em movimento. Consequentemente, não existem garantias de convergência para problemas de MARL em geral. Esta tese explora um problema em particular, denominado escolha de rotas (onde motoristas egoístas deve escolher rotas que minimizem seus custos de viagem), em busca de garantias de convergência. Em particular, esta tese busca garantir a convergência de algoritmos de MARL para o equilíbrio dos usuários (onde nenhum motorista consegue melhorar seu desempenho mudando de rota) e para o ótimo do sistema (onde o tempo médio de viagem é mínimo). O principal objetivo desta tese é mostrar que, no contexto de escolha de rotas, é possível garantir a convergência de algoritmos de MARL sob certas condições. Primeiramente, introduzimos uma algoritmo de aprendizagem por reforço baseado em minimização de arrependimento, o qual provamos ser capaz de convergir para o equilíbrio dos usuários Nosso algoritmo estima o arrependimento associado com as ações dos agentes e usa tal informação como sinal de reforço dos agentes. Além do mais, estabelecemos um limite superior no arrependimento dos agentes. Em seguida, estendemos o referido algoritmo para lidar com informações não-locais, fornecidas por um serviço de navegação. Ao usar tais informações, os agentes são capazes de estimar melhor o arrependimento de suas ações, o que melhora seu desempenho. Finalmente, de modo a mitigar os efeitos do egoísmo dos agentes, propomos ainda um método genérico de pedágios baseados em custos marginais, onde os agentes são cobrados proporcionalmente ao custo imposto por eles aos demais. Neste sentido, apresentamos ainda um algoritmo de aprendizagem por reforço baseado em pedágios que, provamos, converge para o ótimo do sistema e é mais justo que outros existentes na literatura. / Multiagent reinforcement learning (MARL) is a challenging task, where self-interested agents concurrently learn a policy that maximise their utilities. Learning here is difficult because agents must adapt to each other, which makes their objective a moving target. As a side effect, no convergence guarantees exist for the general MARL setting. This thesis exploits a particular MARL problem, namely route choice (where selfish drivers aim at choosing routes that minimise their travel costs), to deliver convergence guarantees. We are particularly interested in guaranteeing convergence to two fundamental solution concepts: the user equilibrium (UE, when no agent benefits from unilaterally changing its route) and the system optimum (SO, when average travel time is minimum). The main goal of this thesis is to show that, in the context of route choice, MARL can be guaranteed to converge to the UE as well as to the SO upon certain conditions. Firstly, we introduce a regret-minimising Q-learning algorithm, which we prove that converges to the UE. Our algorithm works by estimating the regret associated with agents’ actions and using such information as reinforcement signal for updating the corresponding Q-values. We also establish a bound on the agents’ regret. We then extend this algorithm to deal with non-local information provided by a navigation service. Using such information, agents can improve their regrets estimates, thus performing empirically better. Finally, in order to mitigate the effects of selfishness, we also present a generalised marginal-cost tolling scheme in which drivers are charged proportional to the cost imposed on others. We then devise a toll-based Q-learning algorithm, which we prove that converges to the SO and that is fairer than existing tolling schemes.

Aprendizagem por reforço multiagente (do inglês, MARL) é uma tarefa desafiadora em que agentes buscam, concorrentemente, uma política capaz de maximizar sua utilidade. Aprender neste tipo de cenário é difícil porque os agentes devem se adaptar uns aos outros, tornando o objetivo um alvo em movimento. Consequentemente, não existem garantias de convergência para problemas de MARL em geral. Esta tese explora um problema em particular, denominado escolha de rotas (onde motoristas egoístas deve escolher rotas que minimizem seus custos de viagem), em busca de garantias de convergência. Em particular, esta tese busca garantir a convergência de algoritmos de MARL para o equilíbrio dos usuários (onde nenhum motorista consegue melhorar seu desempenho mudando de rota) e para o ótimo do sistema (onde o tempo médio de viagem é mínimo). O principal objetivo desta tese é mostrar que, no contexto de escolha de rotas, é possível garantir a convergência de algoritmos de MARL sob certas condições. Primeiramente, introduzimos uma algoritmo de aprendizagem por reforço baseado em minimização de arrependimento, o qual provamos ser capaz de convergir para o equilíbrio dos usuários Nosso algoritmo estima o arrependimento associado com as ações dos agentes e usa tal informação como sinal de reforço dos agentes. Além do mais, estabelecemos um limite superior no arrependimento dos agentes. Em seguida, estendemos o referido algoritmo para lidar com informações não-locais, fornecidas por um serviço de navegação. Ao usar tais informações, os agentes são capazes de estimar melhor o arrependimento de suas ações, o que melhora seu desempenho. Finalmente, de modo a mitigar os efeitos do egoísmo dos agentes, propomos ainda um método genérico de pedágios baseados em custos marginais, onde os agentes são cobrados proporcionalmente ao custo imposto por eles aos demais. Neste sentido, apresentamos ainda um algoritmo de aprendizagem por reforço baseado em pedágios que, provamos, converge para o ótimo do sistema e é mais justo que outros existentes na literatura. / Multiagent reinforcement learning (MARL) is a challenging task, where self-interested agents concurrently learn a policy that maximise their utilities. Learning here is difficult because agents must adapt to each other, which makes their objective a moving target. As a side effect, no convergence guarantees exist for the general MARL setting. This thesis exploits a particular MARL problem, namely route choice (where selfish drivers aim at choosing routes that minimise their travel costs), to deliver convergence guarantees. We are particularly interested in guaranteeing convergence to two fundamental solution concepts: the user equilibrium (UE, when no agent benefits from unilaterally changing its route) and the system optimum (SO, when average travel time is minimum). The main goal of this thesis is to show that, in the context of route choice, MARL can be guaranteed to converge to the UE as well as to the SO upon certain conditions. Firstly, we introduce a regret-minimising Q-learning algorithm, which we prove that converges to the UE. Our algorithm works by estimating the regret associated with agents’ actions and using such information as reinforcement signal for updating the corresponding Q-values. We also establish a bound on the agents’ regret. We then extend this algorithm to deal with non-local information provided by a navigation service. Using such information, agents can improve their regrets estimates, thus performing empirically better. Finally, in order to mitigate the effects of selfishness, we also present a generalised marginal-cost tolling scheme in which drivers are charged proportional to the cost imposed on others. We then devise a toll-based Q-learning algorithm, which we prove that converges to the SO and that is fairer than existing tolling schemes.

Aprendizagem por reforço multiagente (do inglês, MARL) é uma tarefa desafiadora em que agentes buscam, concorrentemente, uma política capaz de maximizar sua utilidade. Aprender neste tipo de cenário é difícil porque os agentes devem se adaptar uns aos outros, tornando o objetivo um alvo em movimento. Consequentemente, não existem garantias de convergência para problemas de MARL em geral. Esta tese explora um problema em particular, denominado escolha de rotas (onde motoristas egoístas deve escolher rotas que minimizem seus custos de viagem), em busca de garantias de convergência. Em particular, esta tese busca garantir a convergência de algoritmos de MARL para o equilíbrio dos usuários (onde nenhum motorista consegue melhorar seu desempenho mudando de rota) e para o ótimo do sistema (onde o tempo médio de viagem é mínimo). O principal objetivo desta tese é mostrar que, no contexto de escolha de rotas, é possível garantir a convergência de algoritmos de MARL sob certas condições. Primeiramente, introduzimos uma algoritmo de aprendizagem por reforço baseado em minimização de arrependimento, o qual provamos ser capaz de convergir para o equilíbrio dos usuários Nosso algoritmo estima o arrependimento associado com as ações dos agentes e usa tal informação como sinal de reforço dos agentes. Além do mais, estabelecemos um limite superior no arrependimento dos agentes. Em seguida, estendemos o referido algoritmo para lidar com informações não-locais, fornecidas por um serviço de navegação. Ao usar tais informações, os agentes são capazes de estimar melhor o arrependimento de suas ações, o que melhora seu desempenho. Finalmente, de modo a mitigar os efeitos do egoísmo dos agentes, propomos ainda um método genérico de pedágios baseados em custos marginais, onde os agentes são cobrados proporcionalmente ao custo imposto por eles aos demais. Neste sentido, apresentamos ainda um algoritmo de aprendizagem por reforço baseado em pedágios que, provamos, converge para o ótimo do sistema e é mais justo que outros existentes na literatura. / Multiagent reinforcement learning (MARL) is a challenging task, where self-interested agents concurrently learn a policy that maximise their utilities. Learning here is difficult because agents must adapt to each other, which makes their objective a moving target. As a side effect, no convergence guarantees exist for the general MARL setting. This thesis exploits a particular MARL problem, namely route choice (where selfish drivers aim at choosing routes that minimise their travel costs), to deliver convergence guarantees. We are particularly interested in guaranteeing convergence to two fundamental solution concepts: the user equilibrium (UE, when no agent benefits from unilaterally changing its route) and the system optimum (SO, when average travel time is minimum). The main goal of this thesis is to show that, in the context of route choice, MARL can be guaranteed to converge to the UE as well as to the SO upon certain conditions. Firstly, we introduce a regret-minimising Q-learning algorithm, which we prove that converges to the UE. Our algorithm works by estimating the regret associated with agents’ actions and using such information as reinforcement signal for updating the corresponding Q-values. We also establish a bound on the agents’ regret. We then extend this algorithm to deal with non-local information provided by a navigation service. Using such information, agents can improve their regrets estimates, thus performing empirically better. Finally, in order to mitigate the effects of selfishness, we also present a generalised marginal-cost tolling scheme in which drivers are charged proportional to the cost imposed on others. We then devise a toll-based Q-learning algorithm, which we prove that converges to the SO and that is fairer than existing tolling schemes.

Adopting the perspective of the traveller, this thesis examines how travel plans are actually made in the everyday situation and how information is used and drawn on when planning the many, often unremarkable, trips of ordinary urban life. Ethnographical field studies in southern Stockholm, Sweden, employing a practice theory perspective showed that people use a vast mixture of different types of information when planning and making trips. Therefore, the concept of travel information should be defined broadly enough to encompass informal, formal, analogue and digital types of travel information. Then, and only then, is it possible to perceive the interactions between these and see their potential as a change factor for sustainable city travel. In relation to this, situations when travel information was not used, were also identified in the qualitative data and are acknowledged and discussed in the thesis. Travel planning is in everyday practice undertaken little by little, squeezed in between other activities of daily life. It is a process more extended in time, space and content than the limited search for information about the best way to get from A to B, assumed and facilitated in many existing travel information services. Travel planning is closely connected to the overall scheduling of activities (planning of life) and thus spans much larger time frames than the single trip. Planning travel also includes consideration of issues such as preparations needed (things to be done before departure/brought on the trip); managing vehicles and equipment in time and space (getting the bicycle/car home again or safely parked); the weather (current and forecast); social relations (the potential of different travel options for relationship building or conflict triggering); social norms (e.g. of 'good parenting' or 'proper behaviour on public transport'); and health issues (wanting exercise or inability to manage stairs/luggage). The qualitative data revealed that travel information is sometimes perceived and used as a security blanket, i.e. something to hold on to and give the traveller courage on the trip until they are safe at their destination. It also showed that travel planning, depending on life situation, can be experienced as either a house of cards, i.e. if anything changes the whole house collapses and has to be rebuilt to a different layout, or a prefab building, i.e. the same prefabricated pieces are joined together in more or less the same pattern every time. Recent decades have seen rapid growth in ICT services relating to transportation. Different types of travel information services have been proposed as a means to decrease the environmental impacts of transportation through effecting behavioural change. Taking the empirical 4 insights provided by this thesis into travel planning and travel information use in practice, it is clear that travel information services of tomorrow, if connected to other ICT systems in everyday life, could be designed in a way that broadens their horizon of assistance in supporting travel planning processes in everyday life. Although, as this thesis demonstrates, the role and potential of travel information services and ICT in the work of achieving sustainable mobility should be discussed from a perspective critical of overly linear perceptions and instrumental starting points. / Denna avhandling tar resenärens perspektiv, och undersöker från vardagslivets horisont hur resor egentligen planeras, och hur information används när de många, ofta obemärkta, vardagsresorna görs i staden. Etnografiska fältstudier i södra Stockholm, gjorda och analyserade med ett praktikteoriperspektiv, visar att människor använder sig av många olika typer av information för att planera och genomföra sina resor. Därför bör begreppet reseinformation definieras vitt nog att rymma både informella, formella, analoga och digitala typer av reseinformation. Först då är det möjligt att iaktta interaktioner mellan dessa, och också se informationens eventuella potential som förändringsfaktor för hållbart stadsresande. Även situationer när reseinformation inte används uppmärksammas och diskuteras. Reseplanering är i vardaglig praktik något som sker lite i taget, i mellanrummen mellan andra sysslor och aktiviteter. Det är en process mer förgrenad i tid, rum och innehåll än den avgränsade sökning efter information om bästa sättet att ta sig mellan A och B som många existerande IT-baserade reseplanerare verkar förutsätta och i dagsläget assisterar. Reseplanering är starkt knutet till den övergripande planeringen av aktiviteter i livet, och reseplanering har därför oftast ett vidare tidsspann än endast en resa. Planering av resor inkluderar även hänsyn till faktorer som nödvändiga förberedelser (saker att göra innan avfärd/ta med sig på resan); hantering av fordon och bagage i tid och rum (få hem cykeln/bilen igen eller få den säkert parkerad); vädret (nuvarande och prognostiserat); sociala relationer (potentialen för konflikter/positiv samvaro vid olika färdmedelsval); sociala normer (om gott föräldraskap eller hur man för sig på kollektiva färdmedel); och hälsoaspekter (vilja till motion eller oförmåga att gå i trappor). Fältarbetet gjorde tydligt att reseinformation ibland används som en snuttefilt – något att ty sig till och hålla i handen på okänt territorium tills du har kommit till målet för din resa. Reseplanering kan också, beroende på livssituation, antingen upplevas och liknas vid husbygge med prefabricerade element – samma standardiserade, välkända bitar sammanfogas på ungefär samma sätt varje gång; eller också ett korthus – om någonting ändras brakar hela huset ihop, och måste byggas upp igen med en helt annan design. Under de senaste decennierna har det skett en snabb utveckling av IT-lösningar på transportområdet. Olika typer av reseinformationstjänster har föreslagits som ett verktyg för att ändra resebeteenden och därmed minska transporters miljöpåverkan. Med utgångspunkt från studiens 6 empiriska insikter i hur reseplanering går till i praktiken, och hur reseinformation används blir det tydligt att morgondagens reseplanerartjänster, om de kopplades ihop med andra digitala system vi använder i vår vardag, skulle kunna utformas på ett sätt som utvidgar systemets "assistanshorisont" för att bättre passa ihop med reseplaneringspraktikerna i vår vardag. Potentialen och rollen för reseinformationstjänster och IT i arbetet med att minska transporters miljöpåverkan bör diskuteras från ett perspektiv som är kritiskt till alltför linjära och instrumentella utgångspunkter, vilket denna avhandling bidrar till. / <p>QC 20141030</p>