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This paper proposes to create data-driven perspectives on airline ticketing data by defining groups of air travel itineraries independently of geographical or temporal attributes. We demonstrate the approach by analysing an extensive empirical data set featuring ticketing data from several carriers as collected by the Airlines Reporting Corporation. The analysis compares five cluster quality indicators to evaluate effects of pre-processing and clustering decisions. Benchmarking the results against a more traditional geographical grouping demonstrates the potential for data-driven analysis. Finally, we highlight ways in which the resulting cluster perspectives could support demand forecasting, performance evaluation, and analyst interventions. [ABSTRACT FROM AUTHOR]

This is the first study of airline travelers’ no-show and standby behavior based on passenger and directional outbound/inbound itinerary data. The paper describes passengers’ behavior based on estimation of a multinomial logit model for domestic US itineraries departing in March 2001 or March 2002. This enables us to explore behavioral differences based on passenger and itinerary characteristics as well as identify differences in rescheduling behavior occurring after September 11, 2001. Benefits of using passenger data to improve forecasting accuracy and support a broad range of managerial decisions are described. [Copyright &y& Elsevier]

This study models airline passengers' no-show and standby behaviour using passenger and out-bound and inbound itinerary information. It describes factors that influence no-show and standby behaviour in continental US markets using multinomial and nested logit models. The model accounts for early standby opportunities on other itineraries flown by the carrier of interest, ie it incorporates information about booking levels on the carrier's itineraries departing on the same day. Empirical and validation analyses highlight the importance of distinguishing between outbound and inbound itineraries. It concludes by describing a framework carriers can use to implement the no-show model. [ABSTRACT FROM AUTHOR]

• We executed the same survey of airline itinerary choice on Amazon Mechanical Turk (AMT) and on Qualtrics. • Prior air travel characteristics are similar for the two populations. • AMT respondents are more likely to be younger, be male, and have low incomes. • Airline preferences for connections, elapsed time, and departure time of day are similar. • AMT respondents under age 65 are more price-sensitive than Qualtrics respondents. Within the transportation community, there has been increasing interest in using online outsourcing platforms such as Amazon Mechanical Turk (AMT) to conduct surveys. To date, transportation researchers' use of AMT has been justified based on findings from studies in other fields. That is, to the best of our knowledge, there has been no study that has evaluated how the distribution of responses associated with each question and behavioral model estimated from AMT survey data compares to survey data collected from a traditional platform for a travel behavior application. This paper fills an important gap in the literature by examining (1) whether the distributions of responses from AMT and Qualtrics (a traditional market research firm) respondents are statistically equivalent, and (2) whether itinerary choice models estimated from these two surveys are statistically equivalent? Results show that AMT and Qualtrics respondents reported similar air trip characteristics and were drawn from a similar geographic distribution, but they exhibited distinct sociodemographic characteristics. After controlling for different age distributions in the two datasets, we found that airline itinerary choice models estimated from the AMT and Qualtrics survey data produced similar results, with the key difference related to price sensitivities. Our study provides preliminary evidence on the viability of using AMT and similar online outsourcing platforms for air travel behavior studies. [ABSTRACT FROM AUTHOR]

Airlines frequently use advance purchase ticket deadlines to segment consumers. Few empirical studies have investigated how individuals respond to advance purchase deadlines and price uncertainties induced by these deadlines. We model the number of searches (and purchases) for specific search and departure dates using an instrumental variable approach that corrects for price endogeneity. Results show that search and purchase behaviors vary by search day of week, days from departure, lowest offered fares, variation in lowest offered fares across competitors, and market distance. After controlling for the presence of web bots, we find that the number of consumer searches increases just prior to an advance purchase deadline. This increase can be explained by consumers switching their desired departure dates by one or two days to avoid higher fares that occur immediately after an advance purchase deadline has passed. This reallocation of demand has significant practical implications for the airline industry because the majority of revenue management and scheduling decision support systems currently do not incorporate these behaviors. [ABSTRACT FROM AUTHOR]