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How do Europeans travel in Australia? Examining cultural convergence in travel behaviour

How do Europeans travel in Australia? Examining cultural convergence in travel behaviour

In the field of tourism research, there is evi- dence that studies still support the argument that national cultures form a distinctive base for distin- guishing travel behaviour, and researchers agree that those geographical segmentations are still fre- quently applied (Dolnicar and Kemp, 2009; Mos- cardo et al., 2000; Tkaczynski et al., 2009). According to Pearce (2005: 133), most tourism research argues for diverging and heterogeneous cultures: ‘An implicit conceptual approach under- lies much of the international market segmenta- tion research in tourism. The approach may be described as divergence and is characterized by the view that markets are culture-bound, often nationally distinct and are likely to stay that way’. Reisinger (2009: 22) also supports this argument for the distinctiveness of geographical borders of Europe; it is culturally fragmented, and countries preserve their culture: ‘European countries pay particular attention to their cultural identities, origins of artists, rituals, art works, buildings, and even whole landscapes’.
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Exploring the relationship between land-use system and travel behaviour - some first findings

Exploring the relationship between land-use system and travel behaviour - some first findings

1. D EFINING THE LAND - USE / TRANSPORT SYSTEM PROBLEM The spatial distribution of activities such as living, working, recreating or education, implies that people have to travel. Therefore, the land-use configuration is thought to be able to generate particular travel patterns. Consequently, the theoretical foundation for the impact of land-use on travel behaviour can be found in the theory of utilitarian travel demand (Lancaster, 1957). This theory postulates that the demand for travel does not derive its utility from the trip itself, but originates from the need to reach the locations where activities take place (van Wee, 2002). In other words, the greater part of our travel trips is derived (although a limited part is undirected travel - Mokhtarian & Salomon, 2001). On the other hand, changes in the transport system can alter location decisions of households and firms, resulting in a land-use change. For instance, an investment in transport infrastructure changes the accessibility of a region, which has an impact on housing values, economic development, … This two- way interaction between land-use and transport composes the land-use/transport system (LUTS). Wegener & Fürst (1999) entitle the LUTS as the ‘land-use transport feedback cycle’. The set of relationships implied by this term can be summarized as follows:
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Understanding urban travel behaviour by gender for efficient and equitable transport policies

Understanding urban travel behaviour by gender for efficient and equitable transport policies

Policy insights This study has shown that women transport users have more similar travel characteristics to other women in different cities than with men in their own cities, supporting the fact that gender plays a more substantial role in affecting travel behaviour than other factors, such as the built environment, existing transport services, or even age and income. For example, travel distance is found to be longer for men than women across all eight cities, where men also make more commuting than non-commuting trips, drive more (especially in developing cities) and travel more during conventional peak hours that reflect regular working hours. It is clear that in order to implement efficient and equitable transport policies, the differences in travel behaviour by gender have to be considered and addressed accordingly in both developing and developed cities. Although it is imperative that the gender mainstreaming of transport policies and programmes take place at every level to achieve true gender equity, four main policy insights have been identified in this study.
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Approaches to Create a Data Basis for Modelling of Long-Distance Travel Behaviour

Approaches to Create a Data Basis for Modelling of Long-Distance Travel Behaviour

The present paper introduces the complexity of the long-distance travel data landscape in Germany. By listing earlier studies it illustrates the diversity of published indication of annual long-distance trip frequencies and sets a range for it. It can be assumed that the true value for an annual long-distance trip frequency supposedly lies somewhere between 13 and 17. A more precise specification is not possible presently which reveals today’s huge uncertain- ties relating to the quantification of long-distance travel behaviour. It is shown that a consolidation approach using a combination of several data sources leads to higher long-distance trip frequencies as if they result from only one of the sources. Currently there is no micro-level data set that contains all aspects of long-distance travel comprehensively. However, several studies and sur- veys cover parts of long-distance travel in different forms of data availability. The huge challenge will be to create a combined data set that is free of over- lapping of the single travel segments to avoid overestimation of long-distance trip frequencies. A methodological framework to create such data set is pre- sented in this paper, while this idea is strongly reasoned by the here available data. Since overnight trips are considered separately in two important availa- ble micro-level data sets, this separation between overnight travel and day trips is adopted in the here presented approach. This must not hold for differ- ent conditions of data availability. In forthcoming work the methods of the sin- gle parts (data fusion, calibration, consolidation, and evaluation) have to be concretised and applied.
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Travel behaviour response to major transport system disruptions: Implications for smarter resilience planning

Travel behaviour response to major transport system disruptions: Implications for smarter resilience planning

Greg Marsden et al. – Travel Behaviour Response to Major Transport Disruptions acceptable it is to be late during these incidents or whether staff feel their employers support greater home working or flexing of their shift work during these periods. A final observation on the importance of a broader societal resilience approach is to note that this also allows for the long-term impacts of disruptions to be considered. As Graham and Thrift (2007: 5) note, recovery is the means by which society “produces learning, adaptation and improvisation.” If we only look at infrastructure then, once the infrastructure is repaired, the system returns to its previous level of service. However, people adapt and change their behaviours in response to disruptions (see the section on evidence on behavioural response) so, whilst the infrastructure may return to normal, some aspects of the underlying behaviours do not. Similarly, longer-term “expectations” may change. For some infrastructure it may become to be seen as normal that it is closed for several days in any particular winter (e.g. ferries or bridges due to high winds) and social adaptations are put in place to adapt to that (e.g. levels of stock holding in businesses). People may also now choose to live in places which are vulnerable to some transport disruption that they previously would not have if they know they can occasionally work from home to mitigate the consequences of relatively infrequent events. A key question for understanding the economic impacts of disruptive events is to what extent does repeated unreliability (and at what level) begin to make a difference to the attractiveness of places for inward investment? This again requires us to look beyond just the engineering outcomes.
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Potential influence of city pricing (downtown toll) 
for private transport in Germany on travel behaviour

Potential influence of city pricing (downtown toll) for private transport in Germany on travel behaviour

The study seeks to determine to what extent introduction of a city pricing system would constitute an effective method of diverting traffic with the objective of ensuring mobility. The focus is on possible changes in the travel behaviour of car users, espe- cially during work-related rush hour traffic. In particular, the intention is to determine the potential impact on traffic patterns of various toll models. Of interest in this regard are the factors which affect expected changes in travel behaviour. It was also ana- lyzed whether the subjective perception of traffic noise, traffic jams and poor air qual- ity has an impact on behaviour after introduction of a downtown toll. The study was also based on the assumption that expected behavioural changes correlate strongly with the relevant travel purpose.
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Social Networks and Cooperative Travel Behaviour

Social Networks and Cooperative Travel Behaviour

The interaction between social networks and travel behaviour is reciprocal (Larson et al., 2006): Social networks and travel patterns co-evolve in that, on the one hand, the social network induces travel, and on the other hand, travel opportunities enable the spread of the social network. The idea of co-evolution is incorporated in the simulation framework of Arentze and Timmermans (2008), where social connections are created and dissolved according to the individuals’ attributes and their travel patterns. Persons can interact if they meet at the same place at the same time, and persons can exchange information through the social network. While the complexity of Arentze’s and Timmermans model limits its applicability to small scenarios, Marchal and Nagel (2005) and Hackney and Marchal (2011) seize the same idea, yet in a large-scale context and with a underlying traffic flow model. Traditional models for location choice require the enumeration over all alternatives. The work of Marchal and Nagel (2005) emphasises that the inclusion of social networks as an information exchange channel offers the construction of plausible choice sets in reasonable computation times. In the simulation model of Hackney and Marchal (2011), social connections are created or reinforced if people meet at the same place in an overlapping time window and fade out over time if they are not reinforced. An added utility for joint activities compensates for longer trips and shorted activity durations, and moreover, it draws agents together in the same spatiotemporal patterns. In contrast to the latter two simulation models, the model of Ronald et al. (2012) explicitly models cooperative planning of activities, yet lacking an underlying traffic simulation. In Ronal et al.’s model, individuals negotiate about social activities from which they gather information about new activity locations and acquaintances. It is shown that persons with a lot of social contacts participate in more activities and that persons who are similar in their socio-demographics tend to socialise more often.
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Europeans travel behaviour in Istanbul and Turkey

Europeans travel behaviour in Istanbul and Turkey

EU Commission Report-2002, SSI 2000-2001 As it can be seen from Table 10, Turkey addresses only the Germans in the European market and only attracts them. Even though the European market has great importance for Turkey when we look at European travel rates out of EU countries it can be seen that Turkey has hardships attracting European travelers. Within travelers visiting Istanbul, Northern Europeans and Irish visitors are nearly non-existant. On the other hand the Greek, German and Italians prefer to visit the city destinations least among the EU travelers, but the German travelers visit Istanbul the most and the Italians visiting Istanbul is twice as much as all the Italians visiting Turkey. Furthermore it is seen that the Spanish and French has a greater interest for Istanbul than Turkey.
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Household Demographic Composition, Community Design, and Travel Behaviour: An Analysis of Motor Vehicle Use in Germany

Household Demographic Composition, Community Design, and Travel Behaviour: An Analysis of Motor Vehicle Use in Germany

complicated activity patterns and make more serve-passenger trips than men, they have unequal access to the car and conduct more of their travel by public transportation or by foot (Manning, 1978; Giuliano, 1979; Hanson and Hanson, 1980,1981; Hanson and Johnston, 1985; Bernard, Seguin, and Bussiere, 1996, Preißner, Hunecke, Sander and Backhaus, 2000, Heine, Mautz and Rosenbaum, 2001). Consensus on female subordination in car access, however, is far from universal, and empirical evidence varies widely over both time and geography. For example, while Hanson and Johnston (1985) point to evidence from a survey in Baltimore showing that women are far more reliant on public transportation for getting to work, Gordon, Kumar and Richardson (1989 ) find little difference between men and women in private automobile and public transport use. They point to statistics from the 1983 Nationwide Personal Transportation Survey (NPTS) showing that the proportion of women who drive to work (62.2%) in the U.S. is actually slightly higher than that of men (61.7%). Likewise, Rosenbloom (1996) presents statistics from the 1990 Public Use Micro Sample (PUMS) indicating that 89.5% of all female trips is by car compared with 89.1% for men, with both groups having increased car usage substantially over the previous decade. A more recent survey from the U.K. notes that despite a strong growth in license holding among women, only two thirds of female license holders are the main driver of the household car compared with four-fifths of male license holders. In another study from the U.K., Dargay and Hanly (2004) find no significant effect of gender in a probit model of the likelihood of using the car as a commute mode.
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An improved framework for large-scale multi-agent simulations of travel behaviour

An improved framework for large-scale multi-agent simulations of travel behaviour

We have shown that the system can relax to an uncongested state using the present times mutator module, however the relaxation takes many hundreds of iterations. We expect that a more goal-oriented module, which tries to return new activity time schedules that are better than old ones, would allow the system to relax faster. We are working on two versions of such module at different stages of development and capability. Both of them use genetic algorithms (GA) to ‘evolve’ activity schedules by mutating or mixing existing schedules. One of these contains a global (for all agents) mental map of the traffic network for esti- mating travel times between proposed activities, but only adjusts the durations of the activities, leaving patterns and locations alone. Unfortunately, this module has some bugs in the mental map that have not been worked out yet, so it gener- ates faulty schedules (Schneider, 2003; Raney et al., 2003). The second GA- based model has no known bugs but is not yet integrated into the framework (Charypar and Nagel, 2003).
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Mobility information services and its consequences for travel behaviour considering different user types

Mobility information services and its consequences for travel behaviour considering different user types

Cluster 2: age 46, good education, medium income, very high car usage, doesn’t like walking or PT (n=1039). Cluster 3: age 46, medium education, high income, very high car usage, doe[r]

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Trends in commuter and leisure travel in The Netherlands 1991-2001 - Mode choice and travel time

Trends in commuter and leisure travel in The Netherlands 1991-2001 - Mode choice and travel time

1. Introduction The last few decades have seen an increase in complexity in household structures and allocations of household responsibilities and working tasks. Women’s increasingly multifaceted role in the household has lead to increases in female mobility. While we know that work participation rates of women increased during the 1990s, we might expect a change in travel behaviour that reflects this shift (Beets, Liefbroer, & de Jong Gierveld, 1997; Harms, 2003; SCP, 1998). Research indicates that women tend to commute less (time and distance) than men (Harms, 2003; Hjorthol, 2000; MacDonald, 1999; Rouwendal, 1999)Grieco, Pickup, & Whipp, 1989). An important spatial trend that has occurred concurrent with the swell of female workforce participation is the increasing polycentrism of cities. Perhaps the changing gender composition of the workforce can partially explain this trend. Women, have
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National travel statistics – Travel in Europe

National travel statistics – Travel in Europe

In order to understand travel patterns and travel behaviour, it is important to collect data on travel patterns. As part of the OPTIMSIM project, an assessment has been carried out of existing methodologies for collecting travel data in European countries. This paper describes how different countries collect travel data and information on travel resource in their National Travel Surveys (NTS) and assesses if existing NTS can be used to make realistic comparisons between travel patterns and travel behaviour in European countries.
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Harmonisation of national travel surveys in Europe - OPTIMISM WP2: Harmonisation of national travel statistics in Europe

Harmonisation of national travel surveys in Europe - OPTIMISM WP2: Harmonisation of national travel statistics in Europe

Within Task 2.1 of the OPTIMISM project the NTS of several countries have been collected and analysed both to identify and describe the travel patterns that are taking place in those countries, but also to discover what data is actually collected in different countries and how survey methodologies differ across Europe for collection of travel data. As a result it has been possible to identify some general conditions of national travel behaviour in each country and to identify some trends in travel patterns. However, comparing travel patterns across countries using data from different NTS has been limited by the variety of methods that are used to collect data and by differences in the type and format of data that is collected. Therefore, it is necessary to carry out a gap analysis followed by a proposal for a harmonised methodology of data collection by NTS.
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Goals and Behaviour

Goals and Behaviour

The second point in my argument is also related to the assumption of a nomic tie, of the necessary conditionship of an event occurring in order that another event should or could occur. This is, I believe, erroneous in yet another way, namely, in assuming that the basic pattern of one action to one goal obtains. If this were so, we could obtain long and involved explanatory chains, but these would be lineal and would not account for the combined patterns of social life. In actual fact, the same action can often be seen as a means to two or more ends. Consider, for example, the behaviour of a worker in a car factory: it is oriented towards the goal of producing cars, but at the same time towards the goal of maintaining his family. This means that his actions can be seen as having two different settings, two different sets of limiting conditions. Both, however, can be separated only analytically. In actual fact, both settings form a part of the same social reality and combine to limit the worker’s actions in specific ways. He is expected to work with enough intensity to ensure that an adequate quota of cars will be produced, but also to ensure a sufficient amount of money for his family. He should work as much as possible, so as to gain as much money as possible, but not so much as to incur the disapproval of his co-workers, and not so much as to debilitate his other actions within the family (such as, for example, not to be so tired as to have to refuse to play football with his son on Sunday), and so on. The possibility of activities having multiple goals is another legitimate way of making the assumption of the intentionality of behaviour a methodological principle for the analysis of intersubjective reality along the whole continuum between a single activity and a broad social setting.
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Disaggregated car fleets in microscopic travel demand modelling

Disaggregated car fleets in microscopic travel demand modelling

Currently travel demand models are increasingly applied to evaluate emission measures in local areas, such as access zones for different engine types 14 . Exempting electric vehicles from parking fees and taxes are also investigated 8 , which has a major impact on mode choice and furthermore on the whole trip chain. Such car specific restrictions that have a significant impact on travel demand cannot be modeled adequately using a standard average car.

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The fuzziness of travel-to-work areas

The fuzziness of travel-to-work areas

Figure 2 (a-c), show memberships of wards in London separately as a residential, workplace and travel to work area. In general, as expected, there is a wide spread of wards which have some degree of connection with London in its surrounding areas. All wards located inside the first order contiguous TTWAs with London appear to have connections with London. The proportion of wards reduces when the second order contiguous TTWAs with London are examined. Only a small proportion of wards in third order or even fourth order contiguous TTWAs have meaningful linkage with London. The exceptions are Basingstoke, Reading in the west and Milton Keynes in the northwest which are third order contiguous TTWAs but have a significant proportion of wards connecting to London. London as a fuzzy residential area seems to be more compact than London as a fuzzy workplace area. This shows that reverse commuting from London
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Social dilemma behaviour

Social dilemma behaviour

“Fair” is a dummy for giving half of the money to the other participant, “Swiftness” is measured as 120 seconds minus the time used (top-coded at 120 seconds) to answer a three-item ques[r]

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The valuation of travel time variability

The valuation of travel time variability

Having established some conceptual models for travel time variability, we next discuss some broader issues. The first is the fact that travel time variability will often be costly to people other than the traveller, including other meeting participants. However, the standard models used to assign a cost to travel time variability take the perspective of a single agent and do not represent other people. The second issue is the role of information and expectations. What counts as random travel time variability depends on the information available to travellers and it is important that we take this into account. Very little is known, however, about how travellers actually form expectations of travel times. Third, the models that we use are based on neoclassical rationality assumptions, but these assumptions can be systematically violated, especially in stated preference experiments. The paper discusses what to do about this issue.
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Travel Bans in Europe: A Legal Appraisal

Travel Bans in Europe: A Legal Appraisal

Along similar lines, it remains unclear, how the Commission defines the exemptions laid down in the non-binding Communication , such as ‘imperative family reasons’ or ‘persons in need of international protection’, which semantically evades the question whether people coming from potentially safe countries can claim asylum. Moreover, the exemptions are presented in an enumerative manner without indicating whether the reference to ‘non-essential travel’ may be applied in individual cases even if the other criteria are not met – mirroring the ‘cogent reasons’ formula of the German federal police. The Communication was apparently written under utmost time constraints and should be specified as soon as possible.
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