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Independent travelers are those vacationers who have booked only a minimum of their transportation and accommodation arrangements prior to departure on the vacation. Independent travel is an important and growing sector of worldwide tourism. Choice of vacation itinerary for the independent vacation represents a complex series of decisions regarding purchase of multiple leisure and tourism services. This chapter builds and tests a model of independent traveler decision-making for choice of vacation itinerary. The research undertaken employs a two-phase, inductive–deductive case study design. In the deductive phase, the researcher interviewed 20 travel parties vacationing in New Zealand for the first time. The researcher interviewed respondents at both the beginning and the end of their New Zealand vacations. The study compares pre-vacation research and plans, and actual vacation behaviors, on a case-by-case basis. The study examines case study narratives and quantitative measures of crucial variables. The study tests two competing models of independent traveler decision-making, using a pattern-matching procedure. This embedded research design results in high multi-source, multi-method validity for the supported model. The model of the Independent Vacation as Evolving Itinerary suggests that much of the vacation itinerary experienced in independent travel is indeed unplanned, and that a desire to experience the unplanned is a key hedonic motive for independent travel. Rather than following a fixed itinerary, the itinerary of an independent vacation evolves as the vacation proceeds. The independent traveler takes advantage of serendipitous opportunities to experience a number of locations, attractions and activities that they had neither actively researched nor planned.

In this chapter, we support that pricing carbon emissions policies in aviation might have a lower than expected impact on aviation activity, if such policies are accompanied by investments in environmental technology and an adequate communication strategy by the airlines, which eliminates information asymmetries. Furthermore, we suggest that firms' environmental strategies might be an important advantage in competition. Using orthogonal design techniques, we developed a binary choice experiment, in which carbon emissions are presented to 338 German respondents among other choice-related attributes. Estimating several binary logit models, we are in a position to compute the air travelers' willingness to pay (WTP) for carbon emissions reductions. Findings suggest that passengers consider carbon emissions in their individual airline (itinerary) choice decision, if they receive the corresponding information. We also show that the passengers' WTP for carbon emissions reductions is in line with recent estimates on marginal damage cost. Furthermore, we find that WTP increases for passengers with high environmental sensitivity, whereas it decreases for frequent flyers. Investments in environmental technology and airline commitment for environmental-related firm policy positively influence the passengers' choices. It appears that the average passenger is more sensitive to environmental issues and is willing to pay more for environmental protection than industry officials or policymakers presume.

Mixed logit models can represent heterogeneity across individuals, in both observed and unobserved preferences, but require computationally expensive calculations to compute probabilities. A few methods for including error covariance heterogeneity in a closed form models have been proposed, and this paper adds to that collection, introducing a new form of a Network GEV model that sub-parameterizes the allocation values for the assignment of alternatives (and sub-nests) to nests. This change allows the incorporation of systematic (nonrandom) error covariance heterogeneity across individuals, while maintaining a closed form for the calculation of choice probabilities. Also explored is a latent class model of nested models, which can similarly express heterogeneity. The heterogeneous models are compared to a similar model with homogeneous covariance in a realistic scenario, and are shown to significantly outperform the homogeneous model, and the level of improvement is especially large in certain market segments. The results also suggest that the two heterogeneous models introduced herein may be functionally equivalent.

The growth of online data and services on the Web have have led to the Web become an indispensable tool for the tourist industry. It is not denied that various approaches bring benefits for visitors, in supporting their searching for tourist attractions, such as interesting places for the visit, eating or staying. However, like a coin has two sides, too much information would present a difficulty for people when planning their journeys. Generally, tourists usually have problems when trying to find satisfactory accommodation if references to nearby restaurants, sights or event locations are lacking. In addition, travelers suffer from the information overload when they look for information about potential destinations, events and related services. Providing relevant and up‐to‐date information for the tourists with different personal interests is still a challenging task for the tourist guide information systems. The purpose of this paper is to propose a semantic approach for searching tourist information and generating travel itinerary.

The paper focus on introducing an ontological model for representation of tourist resources as well as traveler's profile. Based on this model, smart user interfaces facilitating the semantic search have been implemented in the mobile travel guide application. In addition, the authors propose an algorithm for generating travel itinerary which combines semantic matching with ant colony optimization technique.

The Semantic Tourist informAtion Access and Recommending (STAAR) system, which has been implemented to promote the travel activity in Hanoi, reveals the advantages of the semantic approach in the development of smart application with ontology‐based, user‐friendly interface and high precision information search features. An experiment was conducted to show that the proposed algorithm generates travel itinerary relevant to both criteria of itinerary length and user interest.

This paper presents a novel algorithm for planning a travel itinerary that is optimized on the length as well as semantically matching the user interests.

This chapter estimates the demand for flights in an international air travel market using a unique dataset with detailed information not only on flight choices but also on contemporaneous prices and characteristics of all the alternative non-booked flights. The estimation strategy employs a simple discrete choice random utility model that we use to analyze how choices and its response to prices depend on the departing airport, the identity of the carrier, and the departure date and time. The results show that a 10% increase in prices in a 100-seat aircraft throughout a 100-period selling season decreases quantity demanded by 7.7 seats. We also find that the quantity demanded is more responsive to prices for Delta and American, during morning and evening flights and that the response to prices changes significantly over different departure dates.

This chapter explores the cultural interest and needs of Thai group tourists undertaking specific tours. The perspectives of a specialist Thai tour operator in choosing routes and conducting tours for these tourists in Europe provide the basis for the points identified. The special appeal of themes linked to Thai life is emphasized. The development of a group travel itinerary is documented as a pragmatic exercise in matching the expectations and the spending power of the participants with the business goals of the tour provider. The wider pan-Asian implications of the work include the need for a high level of personal care and the necessity to explain and interpret often little understood historical facts.

The connectivity provided by full-service network carriers under the umbrella of airline alliances is increasingly challenged by the services of Middle Eastern airlines via their own hubs, and the rise of new passenger strategies like self-connectivity. While these two developments can potentially benefit consumers with more services and lower fares, the rise of Middle East carriers has been met with opposition by EU and US airlines that call for increased protectionism. In addition, only a few airports in the world actively support self-connections. In this context, this study aims to investigate (1) the markets in which Middle East carriers exert a stronger dominance in terms of the number of passenger connections, (2) whether EU, US, or Asian hubs provide a competitive quality of connectivity in terms of travel time, and (3) whether a significant potential for self-connections is hidden at major airports worldwide. To that end, several datasets of passenger bookings (MIDT), airline schedules, and minimum connecting times between 2012 and 2015 are combined in a connections-building methodology that delivers six market-specific airport connectivity indicators for our benchmarking exercise. Our findings show that although European and some Asian hubs have lost traffic in global markets, they remain competitive from a quality perspective. US hubs have maintained their market share and competitive position. Finally, we identify the airports and airlines with the highest potential to provide self-connecting travel options, which can become an attractive new source of revenue for the parties involved.