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Status quo und developments of long distance mobility (ways >100 km) ; Source: Bieland 2015 based on IFMO 2014, pp. 16 – 28.
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Traffic performance of leisure traffic has increased over the last years. Leisure traffic can be distinguished into common and uncommon leisure traffic. Common leisure traffic includes regular and routinised mobility whereas uncommon leisure traffic, for example, is represented by day trips or (short-time) vacations. The performance of uncommon lei...
Contexts in source publication
Context 1
... an environment friendly traffic management, a shift in transport performance to environmental alliance is necessary. Figure 1 illustrates the current transport performance as well as its future development in different segments of long-distance mobility. Long-distance mobility covers trips with a length of at least 100 km. ...
Context 2
... 11% of the visitors would choose a simple and cheap hotel accommodation. The majority of respondents, however, prefer higher-priced all- inclusive offers, such as the KONUS Guest Card (Hochschwarzwald Tourismus GmbH 2015). Accordingly, 61% would most likely opt for a hotel offering an all-inclusive card without electromobile transport offers. ...
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... According to the theory of planned behavior, intentions affect mode choice, although other important predictors are traveler habits and past behavior (Lanzini & Khan, 2017). Thus, it is interesting that analyzing habits in relation to holiday travel behavior allowed Bieland et al. (2016) to find that repeated use of public transport makes it more likely that it will be used during short holidays. A similar observation was made earlier by Nordfjaern et al. (2015) regarding leisure travel. ...
The article presents insights into holiday travel and its determinants in Poland. The purpose of the study was to analyze Polish citizens’ modal split and its determinants. Raw data from a pilot survey conducted in 2015 were used as the source material. To identify the determinants of travel mode choice for holiday trips, a multilevel multinomial logit model was utilized. This approach made it possible to include the hierarchical structure of the data, in which respondents are clustered within municipalities. The results reveal that apart from the decision-maker’s socioeconomic characteristics and household attributes, trip characteristics significantly determine Polish citizens’ choice of holiday travel mode. Moreover, the inclusion of municipality-level predictors substantially improved the accuracy of the model. The analysis revealed that the severity of the environmental consequences of motorized transport perceived by respondents also significantly influences their travel mode choice for holiday trips.
... On the demand-side, research has investigated the influence of sociodemographic features, travel motivations, inconvenience & restrictions, habits and situational factors of the trip, including required equipment transport (Gross & Grimm, 2018;Gühnemann et al., 2021;Juschten & Hössinger, 2021;. Some studies also point out the difficulties related to the organization and planning of PT-based trips, especially regarding the lack of information and skills to plan PT-based recreation trips (Bieland et al., 2016;Bursa et al., 2022a;Mattioli et al., 2016). However, information needs are often addressed superficially with no distinction between the availability of information and the ability to process it correctly (Le-Klähn et al., 2014;Zamparini et al., 2022). ...
... If a person does not travel by car, he or she will in most cases have no access to a car at his or her holiday destination and will be forced to move around by public transport or on foot. This was shown by several studies, such as Gutiérrez and Miravet (2016), Lew and McKercher (2006), Gross and Grimm (2018), and Bieland et al. (2016). The mode choice for the journey can be seen as analogous to mode availability variables in conventional travel demand models. ...
... The influence of the mode for arriving at a holiday destination on the intradestination travel behavior has been clearly proven by several studies, as already described. Differences in travel behavior between day and overnight visitors have also been shown in previous studies (Bieland et al., 2016) and is also measurable with our survey data. Two of these groups were chosen for the modeling of the intra-destination travel behavior. ...
With growing city tourism, there is an increasing need for urban travel demand models to consider traffic generated by visitors. Existing research has concentrated on socio-demographic and journey-related factors to determine what influences the mode choice of tourists. In contrast, revealed preference data, such as travel time, is almost never considered. In this article, we present the results of discrete choice modeling of city tourists' mode choice based on revealed preference data from a survey we conducted in Kassel, Germany. We used multinomial logit models and determined the model parameters using maximum likelihood estimations. Surprisingly , travel time played a smaller role in mode choice than assumed from previously established understanding about everyday mobility. In the final model, travel time was only significant for the alternative of walking. Also, most other sociodemographic and journey-related variables showed no significant influence. The final model reproduced the mode choice, but the goodness of fit was lower than expected from other research. We conclude that modeling the travel behavior of tourists is more complex than everyday mobility. An alternative approach that we suggest would be to model trip chains rather than single trips.
... If a person does not travel by car, he or she will in most cases have no access to a car at his or her holiday destination and will be forced to move around by public transport or on foot. This was shown by several studies, such as Gutiérrez and Miravet (2016)], Lew and McKercher (2006), Gross and Grimm (2018), and Bieland et al. (2016). The mode choice for the journey can be seen as analogous to mode availability variables in conventional travel demand models. ...
With growing city tourism, there is an increasing need for urban travel demand models to consider traffic generated by visitors. Existing research has concentrated on socio-demographic and journey-related factors to determine what influences the mode choice of tourists. In contrast, revealed preference data, like travel time, is almost never considered. In this article, we present the results of discrete choice modeling of city tourists’ mode choice based on revealed preference data from a survey we conducted in Kassel, Germany. We used multinomial logit models and determined the model parameters using maximum likelihood estimations. Surprisingly, travel time played a smaller role in mode choice than understood from previously established knowledge about everyday mobility. In the final model, travel time was only significant for the alternative walking. Also, most other sociodemographic and journey-related variables showed no significant influence. The final model reproduced the mode choice, but the goodness of fit was lower than expected from other research. We conclude that modeling the travel behavior of tourists is more complex than everyday mobility. An alternative approach that we suggest would be to model trip chains rather than single trips.
... Knowledge of the tourism market and tourist behavior is usually obtained from traditional data sources, such as statistical yearbooks (Shanghai Bureau of Statistics, 2020) and surveys (Rudjanakanoknad and Rattanasuwan, 2011;Huang et al., 2011;Coban, 2012;Bieland et al., 2016) and Internet data sources, including online trip diaries and geotagged social media data (Kotus et al., 2015;Sun et al., 2018;Wu et al., 2018;Khan et al., 2020;Mou et al., 2020). Traditional methods include analyses of tourism facility statistics, market trends, and tourist preferences. ...
... Surveys were conducted to understand tourist demands and preferences. Due to limitations of the sample size and cost, the respondents were typically limited to a small research area, such as tour bus lines (Rudjanakanoknad and Rattanasuwan, 2011), transportation hubs (Huang et al., 2011), hotels (Coban, 2012), and destinations (Bieland et al., 2016;De Cantis et al., 2016). ...
Mobile phone data provide a more complete and accurate description of tourism transportation demand than traditional and Internet data sources. In this paper, a framework is proposed to determine spatial correlations between tourism destinations, rest places, and transportation hubs based on mobile phone data. Firstly, nine rules for identifying visitors based on four spatial and temporal features are established. Then, the spatial correlations are analyzed from three aspects. A case study of Shanghai is carried out to verify the proposed methodology, and the addition of a tour bus network based on the evaluation of transportation accessibility is discussed. It is concluded that tourists tend to rest near next-day destinations and choose transportation hubs in the city center. The rest places that are sightseeing destinations, amusement parks, and convention centers exhibit polycentric characteristics. The research framework and results of this study are useful for tourism transportation planning.
Introduction. One of the key problems in ensuring the quality of recreation for the population is the provision of transport services that meet the transport needs of vacationers. The study of the transport needs of the population of resort cities during the holiday season is caused by the need to assess changing passenger flows as a result of a multiple increase due to vacationers in accordance with their place of attraction and possible adjustments to the routes of regular passenger transportation by road. This determines the relevance of the topic of this article. The purpose of the article is to develop a methodology for determining the needs for transport services for the population and vacationers in cities with high resort potential during the busiest periods, which makes it possible to design transportation processes along regular routes.
Materials and methods. General scientific methods of analysis and synthesis, the provisions of probability theory and mathematical statistics, mathematical modeling, as well as the provisions of the technology of transport processes were used as the main research methods. The work carried out the zoning of the city into transport areas with a center of gravity at stopping points of urban passenger transport. Calculations of potential transport needs were carried out on the basis of a survey of vacationers and field observations, which were processed using standard Microsoft Excel and Statistika software.
Results. The main result of the work is a methodology for determining the potential transport needs of the population and vacationers in cities with high tourism potential for the development and adjustment of routes for regular passenger transportation by road during the holiday season, which represents the scientific novelty of the research. The technique contains a mathematical model and algorithm.
Discussion and conclusion. The use of this methodology will enable the organizer of transportation in resort cities to optimize the organization of regular passenger transportation by road during peak seasons.
With the growing relevance of long-distance travel and the resulting climate impacts, the understanding of long-distance travel next to everyday travel becomes relevant. In particular in urban areas, people often compensate short distances and the use of environmentally friendly means of transport in everyday life with a higher amount of long-distance travel. The question arises how to characterize the behavior of urban people considering both everyday and long-distance travel behavior. Of interest is, whether there are discrepancies or similarities between the both kinds of travel, especially regarding mode choice. The demand for a car may not only result from daily mobility needs but from the extension of everyday life with long-distance travel. With the authors' paper, the authors present a typology of distinct travel types, that considers characteristics of everyday travel and long-distance travel as well as attitudes. By using data from a survey in Munich (Germany), the authors analyzed the relevance of long-distance journeys with short durations, such as weekend trips, as an extension of everyday life. For the segmentation, characteristics of everyday travel, long-distance travel and attitudes towards the car and public transit were simultaneously included in a cluster analysis. Seven traveler types were identified and compared to each other. The results show that traveler types exist that are very similar in everyday travel behavior but show completely different characteristics in terms of long-distance travel volumes and mode choice. Furthermore, the authors also see that for some traveler types, the car exclusively plays a role for trips that extend everyday life.
