Using Internet-based marketplaces to conduct surveys:an application to airline itinerary choice models

\u3cp\u3eWithin 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.\u3c/p\u3

Computational methods for estimating multinomial, nested, and cross-nested logit models that account for semi-aggregate data

We present a summary of important computational issues and opportunities that arise from the use of semi-aggregate data (where the explanatory data for choice scenarios are not necessarily unique for each decision-maker) in discrete choice models. These data are encountered with large transactional databases that have limited consumer information, a common feature in some transportation planning applications, such as airline itinerary choice modeling. We developed a freeware software package called Larch, written in Python and C++, to take advantage of these kind of data to greatly speed the estimation of discrete choice model parameters. Benchmarking experiments against Stata (a commonly used commercial package), Biogeme (a commonly used freeware package), and ALOGIT (a highly specialized commercial package for discrete choice modeling) based on an industry dataset for airline itinerary choice modeling applications shows that the size of the input estimation files are 50–100 times larger in Stata and Biogeme, respectively. Estimation times are also much faster in ALOGIT and Larch; e.g., for a small itinerary choice problem, a multinomial logit model estimated in ALOGIT or Larch converged in less than one second whereas the same model took almost 15 seconds in Stata and more than three minutes in Biogeme

A new twist on the gig economy: conducting surveys on Amazon Mechanical Turk

There is growing interest in using online outsourcing platforms that are part of the “gig economy” to conduct surveys for academic research. This interest has been driven in part by the belief that compared to traditional survey data collection methods, internet-based marketplaces such as Amazon Mechanical Turk (MTurk) enable one to collect survey data cheaper and faster from a larger, more diverse participant pool. However, many have questioned whether models based on survey data from these online marketplaces are similar to models based on survey data from more traditional platforms. To investigate this research question, we used MTurk and Qualtrics (a traditional market research firm) to survey air travelers. Our results showed that MTurk and Qualtrics respondents had distinct socio-demographic characteristics, but we found no statistical evidence for different air trip characteristics. In our data, proportionately more MTurk respondents were in the younger, single, male, and lower-income categories than for Qualtrics respondents. We found that airline itinerary choice models estimated from the MTurk and Qualtrics survey data were similar, with the key difference related to price sensitivities. Although our results provide evidence that MTurk can be used for travel demand modeling applications, we offer words of caution for others planning to conduct surveys in online marketplaces, particularly for those seeking to recruit more than 1000 participants or for those targeting specific geographic areas

Airline customers’ connection time preferences in domestic US markets

Within the United States, there have been evolving perceptions on the benefits and disadvantages of using peaked flight schedules. Arguments in favor of using peaked schedules have centered on a traditional assumption that consumers prefer itineraries with the shortest connection. However, prior work based on stated preference survey data has suggested that consumers avoid itineraries with the shortest possible (or minimum) connection times, and prefer those that add in an additional buffer of up to 15 min. In this study, we use a revealed preference dataset based on ticketing data from major U.S. carriers and find that, on average, consumers prefer itineraries that add in an additional buffer time of up to 25 min. Consumer preferences for buffer times beyond 25 min are less clear, with the exception of markets that are less than 600 miles apart, in which we see consumers are more sensitive to longer buffer times (and by extension, longer connection times). Our results can be used to help inform depeaking decisions within U.S. markets.\u3cbr/\u3e\u3cbr/\u3

Modeling competition among airline itineraries

\u3cp\u3eDiscrete choice models are commonly used to forecast the probability an airline passenger chooses a specific itinerary. In a prior study, we estimated an itinerary choice model based on a multinomial logit specification that corrected for price endogeneity. In this paper, we extend the analysis to include inter-itinerary competition along three dimensions: nonstop versus connecting level of service, carrier, and time of day using nested logit (NL) and ordered generalized extreme value (OGEV) models. To the best of our knowledge, these are the first NL and OGEV itinerary choice models to correct for price endogeneity. Despite the many structural changes that have occurred in the airline industry, our results are strikingly similar to models estimated more than a decade ago. These results are important because it suggests that customer preferences, on average, have been stable over time and are similar across distribution channels. The stability in inter-itinerary competition patterns provides an important practical implication for airlines, namely it reduces the need to frequently update the parameter estimates for these models.\u3c/p\u3

Accounting for price endogeneity in airline Itinerary choice models:an application to continental U.S. markets

Network planning models, which forecast the profitability of airline schedules, support many critical decisions, including equipment purchase decisions. Network planning models include an itinerary choice model that is used to allocate air total demand in a city pair to different itineraries. Multinomial logit (MNL) models are commonly used in practice and capture how individuals make trade-offs among different itinerary attributes; however, none that we are aware of account for price endogeneity. This study formulates an itinerary choice model that is consistent with those used by industry and corrects for price endogeneity using a control function that uses several types of instrumental variables. We estimate our model using a database of more than 10 million passenger trips provided by the Airlines Reporting Corporation. Results based on Continental U.S. markets for May 2013 departures show that models that fail to account for price endogeneity overestimate customers’ value of time and result in biased price estimates and incorrect pricing recommendations. The size and comprehensiveness of our database allows us to estimate highly refined departure time of day preference curves that account for distance, direction of travel, number of time zones traversed, departure day of week and itinerary type (outbound, inbound or one-way). These time of day preference curves can be used by airlines, researchers, and government organizations in the evaluation of different policies such as congestion pricing