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• Clustering on two eVTOL attitudes, Concern and Enthusiasm, identifies six segments. • The six identified segments differ substantially in their willingness to adopt eVTOL, among many other traits. • Air taxi market segmentation is affected by mean-centering the attitudinal factor scores. • The survey results provide evidence of potential demand for eVTOL commuting. • Attitudinal survey responses identify potential market segments for commuting by eVTOL air taxis. Many companies are developing prototypes for novel electric vertical takeoff and landing (eVTOL) aircraft to serve as air taxis in cities. These eVTOL air taxis are envisioned as a new form of shared transportation that has the potential to reduce commuting times in urban areas with congested roadway networks. In this study, an attitudinal survey of high-income commuters in five U.S. cities was conducted to better understand the segmentation of the market for future eVTOL urban air taxis. Factor analysis of eVTOL perceptions identified two dimensions: Concern and Enthusiasm. Cluster analysis of the scores on these factors identified six meaningful segments, which differed on a variety of demographic, travel behavior, and attitudinal variables, as well as on participants' inclination to adopt eVTOL travel. The study demonstrates how assessment of the sizes of the various air taxi market segments is affected by mean-centering the factor scores obtained from Likert-scale survey results; in particular, it finds that the conventional mean-centered factor scoring approach understates positive interest in eVTOL air taxis. [ABSTRACT FROM AUTHOR]

• We model mode choice for an on-demand air taxi commuting service and traditional modes. • Individuals who are male and frequent ridesharing users are more likely to select the air taxi. • Incorporating a distribution for the value of time (VOT) improves model fit. • Among traders, the median air taxi in-vehicle VOT is about 25 USD/hr, but 10 percent have VOTs higher than 64 USD/hr. In this study, a stated choice experiment was conducted to better understand individuals' preferences for an air taxi commute service. Random taste parameter models with panel effects were used to model the choice among: (1) traditional auto, (2) transit, and (3) air taxi. We find that individuals who are male and are frequent ridesharing users are more likely to select the air taxi. A non-trader analysis of individuals who always selected the same mode across our eight stated choice scenarios showed that air taxi preferences are heterogeneous and polarized, with 14 percent always selecting air taxi and 14 percent never selecting air taxi. Among the traders, significant variation across individuals' value of time (VOT) was observed. Results based on a negative lognormal distribution show the median in-vehicle VOTs for the air taxi mode was about 25 USD/hour, but 10 percent had VOTs higher than 64 USD/hour. Our results highlight the need for future studies of potential market demand for eVTOL aircraft to incorporate the percentage of the population who will likely never consider using an air taxi, as well as the distribution of VOTs for those who will consider using this new mode. [ABSTRACT FROM AUTHOR]

Discrete 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The flipped classroom is becoming increasingly popular at universities because of its perceived benefits in promoting active learning and decreasing educational costs. Studies have found positive benefits associated with flipped classrooms; however, many have failed to control for confounding factors. Examples of confounding factors include comparing courses taught by different instructors or across courses taught in different semesters using different quizzes. The objective of this paper is to compare the traditional and flipped classrooms in an undergraduate civil engineering course while controlling for potential confounding factors. The quasi-experimental study incorporates students' online behaviors, in-class performance, office hour attendance, and responses to both attitudinal and behavioral questions to assess student opinions and learning outcomes. It was found that student performance on quizzes was not significantly different across the traditional and flipped classrooms. A key shortcoming noted with the flipped classroom was students' inability to ask questions during lectures. Students in flipped classrooms were more likely to attend office hours compared to traditional classroom students, but the difference was not statistically significant. Future research should explore whether students' inability to ask questions when the material is presented in flipped classrooms affects learning outcomes. [ABSTRACT FROM AUTHOR]

Understanding homeowners’ marginal willingness-to-pay (MWTP) for proximity to public transportation infrastructure is important for planning and policy. Naïve estimates of MWTP, however, may be biased as a result of spatial dependence, spatial correlation, and/or spatially endogenous variables. In this paper we discuss a class of spatial autoregressive models that control for these spatial effects, and apply them to sample data collected for the Atlanta, Georgia housing market. We provide evidence that a general-to-specific model selection methodology that relies on the generality of the spatial Durbin model (SDM) should be preferred to the classical specific-to-general methodology that begins with an assumption of no spatial effects. We show that applying the SDM raises the estimate of MWTP for transit proximity in Atlanta but also widens its confidence interval, relative to ordinary linear regression. This finding may have implications for risk estimations in land value capture forecasts and transportation policy decisions. [ABSTRACT FROM AUTHOR]

This study explores the relationship between historical exposure to the built environment and current vehicle ownership patterns. The influence of past exposure to the built environment on current vehicle ownership decisions may be causal, but there are alternative explanations. Households may primarily select to live in neighborhoods that facilitate their vehicle ownership preferences, or they may retain preferences that they have developed in the past, irrespective of their current situations. This study seeks to control for these alternative explanations by including the built environment attributes of households’ past residences as an influence on vehicle ownership choices. We use a dataset from a credit reporting firm that contains up to nine previous residential ZIP codes for households currently living in the 13-county Atlanta, Georgia, metropolitan area. Results show that past location is significant, but of marginal influence relative to the attributes of the current location. From a practical perspective, our results suggest that models that include current but not past neighborhood attributes (also controlling for standard socioeconomic variables) can forecast vehicle ownership decisions reasonably well. However, models that include both current and past neighborhood attributes can provide a more nuanced understanding of the built environment’s potentially causal influences on vehicle ownership decisions. This better understanding may provide more realistic forecasts of responses to densification or other travel demand management strategies. [ABSTRACT FROM AUTHOR]

We investigate factors that influence airline customers’ purchases of premium coach seats using a database of online prices and seat map displays collected from JetBlue’s website. Results show that multiple factors influence purchasing behavior; these factors include the amount of the seat fee, how far in advance the ticket is purchased, the number of passengers traveling together, and load factors (as revealed through seat map displays). We find that customers are between 2 and 3.3 times more likely to purchase premium coach seats (with extra legroom and early boarding privileges) when there are no regular coach window or aisle seats that can be reserved for free. In addition, we find that customers who purchase tickets closer to the departure date are less price-sensitive and are willing to pay higher seat fees. We use these model results to show that JetBlue’s seat fees are currently underpriced in many markets; an optimal static fee would increase revenues by 8% whereas optimal time-dependent fees would increase revenues by 10.2%. In addition, if JetBlue were to leave their seat fees unchanged and instead reserve certain rows of seats for premier customers, they could potentially increase revenues by 12.8%. [ABSTRACT FROM AUTHOR]

We estimate flight-level price elasticities using a database of online prices and seat map displays. In contrast to market-level and route-level elasticities reported in the literature, flight-level elasticities can forecast responses in demand due to day-to-day price fluctuations. Knowing how elasticities vary by flight and booking characteristics and in response to competitors' pricing actions allows airlines to design better promotions. It also allows policy makers the ability to evaluate the impacts of proposed tax increases or time-of-day congestion pricing policies. Our elasticity results show how airlines can design optimal promotions by considering not only which departure dates should be targeted, but also which days of the week customers should be allowed to purchase. Additionally, we show how elasticities can be used by carriers to strategically match a subset of their competitors' sale fares. Methodologically, we use an approach that corrects for price endogeneity; failure to do so results in biased estimates and incorrect pricing recommendations. Using an instrumental variable approach to address this problem we find a set of valid instruments that can be used in future studies of air travel demand. We conclude by describing how our approach contributes to the literature, by offering an approach to estimate flight-level demand elasticities that the research community needs as an input to more advanced optimization models that integrate demand forecasting, price optimization, and revenue optimization models. [ABSTRACT FROM AUTHOR]

Abstract: This paper reviews product debundling trends that have occurred in the US airline industry. Multiple sources of ancillary fees related to ticketing refunds and exchanges, checked baggage, on-board pets, preferred and/or advanced seating assignments, frequent flyer ticket redemptions, and day of departure standby policies are reviewed. Despite the fact that both low cost and network carriers stress the importance of future ancillary fees in their investor reports, our assessment suggests that these fees will be more broadly adopted by low cost carriers. We anticipate that many network carriers will eliminate ancillary fees, particularly as they begin to recognize how these fees can impact other system performance objectives such as minimizing the number of misconnecting passengers. We estimate that the debundling phenomenon has diluted revenues to the US Airport and Airways Trust Fund by at least 5%. [Copyright &y& Elsevier]

Abstract: This paper explores the association of socio-demographic and built environment characteristics on the odds of being overweight and obese using data from the Atlanta SMARTRAQ travel survey. A new methodological framework based on a multinomial logit (MNL) model and an enhanced odds ratio plot is presented. The use of an MNL model overcomes limitations of many prior studies that employ a sequence of binary logit models to examine multiple weight categories. The use of an enhanced odds ratio plot provides important information into the relative importance of socio-demographic and built environment characteristics. Several new findings for the Atlanta area result from this study. Socio-demographic variables, including age and educational attainment, exhibit a non-linear relationship with the odds of being overweight or obese. Gender, age, ethnicity, and educational attainment are strongly associated with the odds of being overweight or obese, while income and number of students between 5 and 16 years old in the household have smaller effects. Built environment characteristics such as increased net residential densities and enhanced street connectivity are associated with reductions in the odds of being overweight and/or obese. Relative to socio-demographic variables, however, such built environment characteristics have a much smaller impact on describing the odds of being overweight or obese. [Copyright &y& Elsevier]

Abstract: This study explores the use of discrete choice methods for airline passenger cancellation behavior. A discrete time proportional odds model with a prospective time scale is estimated based on the occurrence of cancellations (defined as refund and exchange events) in a sample of tickets provided by the Airline Reporting Corporation. Empirical results based on 2004 data from eight domestic US markets indicate that the intensity of the cancellation process is strongly influenced by both the time from ticket purchase and the time before flight departure. Higher cancellations rates are generally observed for recently purchased tickets, and for tickets whose associated flight departure dates are near. Cancellations rates are influenced by several other covariates, including departure day of week, market, and group size. [Copyright &y& Elsevier]

• We compile a database of about 800 UAM, EV, and AV articles. • We use insights from the EV and AV literature to inform future UAM research directions. • UAM research has primarily focused on aircraft technologies and operations. • EV and AV research has a greater emphasis on technology adoption and integration with existing infrastructure. • To date, the majority of UAM research has been conducted by U.S. researchers. Urban air mobility (UAM), if successful, will disrupt urban transportation. UAM is not the first disruptive technology in transportation, with recent examples including electric ground vehicles (EVs), autonomous ground vehicles (AVs), and sharing services. In this paper, we conduct a meta -analysis of about 800 articles in the UAM, EV, and AV areas that have been published from January 2015 to June 2020, and compare and contrast research thrusts in order to inform future UAM research. Alongside this effort, we conduct an in-depth review of articles related to demand modeling, operations, and integration with existing infrastructure. We use insights from the meta -analysis and comprehensive review to inform future UAM research directions. Some of the potential research directions we identify include: (1) developing more refined demand models that incorporate the timing of when individuals will adopt UAM; (2) developing high-fidelity simulation models for UAM operations that capture interactions among vertiport locations, vertiport topology, demand, pricing, dispatching, and airspace restrictions; (3) explicitly considering one-way demand and parking constraints in demand and operational models; and (4) developing more realistic time-of-day energy profiles for UAM vehicles in order to assess whether the current electrical grid can support UAM operations. [ABSTRACT FROM AUTHOR]

• We use cell phone data, census data, and a mode choice model calibrated from a stated 3 preference survey to identify potential air taxi commuter routes in 40 U.S. cities. • Air taxi commuter demand varies across the cities and is sensitive to the location of existing 5 vertiports, current commute patterns, existing ground infrastructure, and other factors. • More than 30 percent of potential air taxi demand is concentrated in the metro areas of 7 New York City, Los Angeles, and Washington, D.C. • A set of interactive maps allows readers to visualize the location of potential air taxi 9 commuter routes for the 40 cities. Given the high levels of congestion that many commuters in the United States experience, the urban air mobility community has been exploring the potential of using a new class of electric vertical takeoff and landing (eVTOL) aircraft for commuters. To date, only a few studies have been conducted that compare how potential air taxi demand across cities varies. This study calculates a measure of air taxi demand for commuters for the 40 most populous combined statistical areas (CSAs) in the U.S. by using: (1) cell phone data to identify regular commuters in cities, (2) census data to associate household income characteristics with commuters, and (3) a mode choice model calibrated from a stated preference survey to predict the number of commuters that would use an air taxi. Air taxi commuter demand is concentrated in a handful of CSAs; the CSAs for New York City, Los Angeles, and Washington, D.C., generate 33 percent of the overall air taxi demand. Results are sensitive to location of existing vertiports, existing ground infrastructure and congestion levels on competing modes, and current commute patterns. A resultant set of online maps of potential air taxi routes allows readers to visualize how air taxi commuter routes differ across CSAs. Results will be of value both to aircraft manufacturers seeking to design air taxi vehicles to serve different cities as well as city planners for identifying where investments in port infrastructure may be needed to support a commuter air taxi service. [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]

This study explores the use of Bayesian methods to estimate hazard models of airline passengers’ cancellation behavior. We show how the discrete time proportional odds (DTPO) cancellation model can be rewritten as an equivalent fixed parameter discrete choice model that can be easily estimated using Bayesian methods and extended to random parameters that account for unobserved heterogeneity. The use of Bayesian methods allows us to address several limitations of existing airline cancellation models. First, because of the random parameter reformulation, it is possible to calculate individual-specific cancellation probabilities. Second, unlike existing DTPO models that forecast average cancellation probabilities only, our model can be used to forecast both means and a measure of variance (credible intervals) associated with an individual’s cancellation probability. We apply the Bayesian estimation method to a dataset of tickets purchased over a two-year period by employees of a university in Atlanta, Georgia. During this time period, the major carrier in Atlanta terminated an agreement in which it allowed employees to purchase discounted fares that could be refunded or exchanged without a fee. The data allow us to investigate how passenger cancellation behavior changed when these fares were discontinued. Cancellations are reduced on average 3.3% when customers must pay to exchange their tickets. For a simulated hypothetical flight the coefficient of variation of cancellation is 43% when the state rate was offered, and 83% without state rates. [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]

Highlights: [•] We model emissions failures using vehicle and household characteristics. [•] Failures are correlated with low-income and minority households. [•] Vehicle attributes are correlated with both initial and subsequent failures. [•] Vehicle age-based testing policies disproportionately benefit wealthier households. [•] Cash-for-Clunkers programs have a high cost per avoided emissions failure. [ABSTRACT FROM AUTHOR]

Highlights: [•] Some, but not all, methods for choice-based samples can be applied to censored data. [•] ASCs and other parameters for censored alternatives can be identified in many cases. [•] Having an entire nest censored in a NL model does not necessarily prevent estimation. [ABSTRACT FROM AUTHOR]

Abstract: We investigate how customers respond to an opaque airline product offered by a European carrier. In this opaque product design, customers are randomly assigned to travel to one of approximately ten destinations; however, for a fee they may exclude one or more destinations from the choice set (or a particular package design) prior to learning which destination they will travel to. We use a multidimensional binary logit model to predict the probability that one or more alternatives will be chosen by a customer. Results show that customers are more likely to pay to exclude destinations located close to the origin airport and destinations that speak the same language as the origin airport. Length of stay, cost of living at the destination, and measures of destination attractiveness are also found to be significant predictors for some package designs. Based on these findings, we offer general recommendations for how to design opaque packages for airline customers. [Copyright &y& Elsevier]