Estimating time of day demand with errors in reported preferred times: An application to airline travel

AbstractAn essential element of demand modeling in the airline industry is the representation of time of day demand—the demand for a given itinerary as a function of its departure or arrival times. It is an important datum that drives successful scheduling and fleet decisions. There are two key components to this problem: the distribution of the time of day demand and how preferred travel time influences itinerary choice. This paper focuses on estimating the time of day distribution. Our objective is to estimate it in a manner that is not confounded with air travel supply; is a function of the characteristics of the traveler, the trip, and the market; and accounts for potential measurement errors in self-reported travel time preferences. We employ a stated preference dataset collected by intercepting people who were booking continental US trips via an internet booking service. Respondents reported preferred travel times as well as choices from a hypothetical set of itineraries. We parameterize the time of day distribution as a mixture of normal distributions (due to the strong peaking nature of travel time preferences) and allow the mixing function to vary by individual characteristics and trip attributes. We estimate the time of day distribution and the itinerary choice model jointly in a manner that can account for measurement error in the self-reported travel time preferences. We find that the mixture of normal distributions fits the time of day distribution well and is behaviorally intuitive, and the strongest covariates of travel time preferences are party size and time zone change. The methodology employed to treat self-reported travel time preferences as potentially having error contributes to the broader transportation time of day demand literature, which either assumes that the desired travel times are known with certainty or that they are unknown. We find that the error in self-reported travel time preferences is statistically significant and impacts the inferred time of day demand distribution

Understanding future mode choice intentions of transit riders as a function of past experiences with travel quality

This paper empirically investigates the causes for transit use cessation, focusing on the influence of users’ personal experiences, resulting levels of satisfaction, and subsequent behavioral intentions. It builds on a novel data set in which observed, objective measures of travel times are mapped to smartphone-based surveys where participants assess their travel experience. An integrated choice and latent variable model is developed to explain the influence of satisfaction with operations (travel times) and satisfaction with the travel environment (e.g., crowding) on behavioral intentions. Satisfaction is modeled as a latent variable, and the choice consists of participants’ stated desire and intention to continue using public transportation. The results show how delays, in particular in-vehicle delays but also transfer times and being left behind at stops, contribute to passengers’ intentions to cease transit use. Furthermore, a number of critical incidents, i.e., particularly memorable negative experiences, are found to have negative and significant effects on overall satisfaction and on willingness to continue using public transportation. The usefulness of the framework is demonstrated in a set of simulations in which the effect of three types of delays on passengers’ willingness to remain transit riders is modeled. This work highlights the value and potential of using new data collection methods to gain insights on complex behavioral processes, and it is intended to form the basis for new modeling tools to understand the causes of transit use cessation and the impact of various strategies and service quality improvements to reduce ridership turnove

Identity and disease cycle of a smut fungus on wiregrass in a longleaf pine-grassland ecosystem in the southeastern USA

Abstracts from the April 12-14, 2019 MASC Conferenc

Latino Parents\u27 Motivations for Involvement in Their Children\u27s Schooling: An Exploratory Study

This study examines the ability of a theoretical model of the parental involvement process to predict Latino parents\u27 involvement in their children\u27s schooling. A sample of Latino parents (N = 147) of grade 1 through 6 children in a large urban public school district in the southeastern United States responded to surveys assessing model-based predictors of involvement (personal psychological beliefs, contextual motivators of involvement, perceived life-context variables), as well as levels of home- and school-based involvement. Home-based involvement was predicted by partnership-focused role construction (a personal psychological belief) and by specific invitations from the student (a contextual motivator of involvement). School-based involvement was predicted by specific invitations from the teacher (a contextual motivator) and by perceptions of time and energy for involvement (a life-context variable). Results are discussed with reference to research on Latino parents\u27 involvemen

Beware of black swans: Taking stock of the description–experience gap in decision under uncertainty

Uncertainty pervades most aspects of life. From selecting a new technology to choosing a career, decision makers rarely know in advance the exact outcomes of their decisions. Whereas the consequences of decisions in standard decision theory are explicitly described (the decision from description (DFD) paradigm), the consequences of decisions in the recent decision from experience (DFE) paradigm are learned from experience. In DFD, decision makers typically overrespond to rare events. That is, rare events have more impact on decisions than their objective probabilities warrant (overweighting). In DFE, decision makers typically exhibit the opposite pattern, underresponding to rare events. That is, rare events may have less impact on decisions than their objective probabilities warrant (underweighting). In extreme cases, rare events are completely neglected, a pattern known as the “Black Swan effect.” This contrast between DFD and DFE is known as a description–experience gap. In this paper, we discuss several tentative interpretations arising from our interdisciplinary examination of this gap. First, while a source of underweighting of rare events in DFE may be sampling error, we observe that a robust description–experience gap remains when these factors are not at play. Second, the residual description–experience gap is not only about experience per se but also about the way in which information concerning the probability distribution over the outcomes is learned in DFE. Econometric error theories may reveal that different assumed error structures in DFD and DFE also contribute to the gap

D-efficient or deficient? A robustness analysis of stated choice experimental designs

This paper is motivated by the increasing popularity of efficient designs for stated choice experiments. The objective in efficient designs is to create a stated choice experiment that minimizes the standard errors of the estimated parameters. In order to do so, such designs require specifying prior values for the parameters to be estimated. While there is significant literature demonstrating the efficiency improvements (and cost savings) of employing efficient designs, the bulk of the literature tests conditions where the priors used to generate the efficient design are assumed to be accurate. However, there is substantially less literature that compares how different design types perform under varying degree of error of the prior. The literature that does exist assumes small fractions are used (e.g., under 20 unique choice tasks generated), which is in contrast to computer-aided surveys that readily allow for large fractions. Further, the results in the literature are abstract in that there is no reference point (i.e., meaningful units) to provide clear insight on the magnitude of any issue. Our objective is to analyze the robustness of different designs within a typical stated choice experiment context of a trade-off between price and quality. We use as an example transportation mode choice, where the key parameter to estimate is the value of time (VOT). Within this context, we test many designs to examine how robust efficient designs are against a misspecification of the prior parameters. The simple mode choice setting allows for insightful visualizations of the designs themselves and also an interpretable reference point (VOT) for the range in which each design is robust. Not surprisingly, the D-efficient design is most efficient in the region where the true population VOT is near the prior used to generate the design: the prior is $20/h and the efficient range is$10–$30/h. However, the D-efficient design quickly becomes the most inefficient outside of this range (under$5/h and above $40/h), and the estimation significantly degrades above$50/h. The orthogonal and random designs are robust for a much larger range of VOT. The robustness of Bayesian efficient designs varies depending on the variance that the prior assumes. Implementing two-stage designs that first use a small sample to estimate priors are also not robust relative to uninformative designs. Arguably, the random design (which is the easiest to generate) performs as well as any design, and it (as well as any design) will perform even better if data cleaning is done to remove choice tasks where one alternative dominates the other. Keywords: Stated choice experiments, Robustness, Mode choice model, Value-of-time Experimental design, D-efficien