Laurie A. Garrow

Data Set---Choice-Based Revenue Management: Data from a Major Hotel Chain

In this paper, we describe data collected from five U.S. properties of a major hotel chain that can be used to benchmark the performance of choice-based revenue management (RM) algorithms. The process used to collect this data illustrates subtle complexities involved in extracting product availability information from current RM systems and sheds new light on practical issues that need to be addressed to successfully implement choice-based RM systems. The data described in this paper is publicly available at the journals website at http://msom.pubs.informs.org .

Estimation of Choice-Based Models Using Sales Data from a Single Firm

We develop a parameter estimation routine for multinomial logit discrete choice models in which one alternative is completely censored, i.e., when one alternative is never observed to have been chosen in the estimation data set. Our method is based on decomposing the log-likelihood function into marginal and conditional components. Our method is computationally efficient, provides consistent parameter estimates, and can easily incorporate price and other product attributes. Simulations based on industry hotel data demonstrate the superior computational performance of our method over alternative estimation methods that are capable of estimating price effects. Because most existing revenue management choice-based optimization algorithms do not include price as a decision variable, our estimation procedure provides the inputs needed for more advanced product portfolio availability and price optimization models.

Impacts of Climate Change on Scour-Vulnerable Bridges: Assessment Based on HYRISK

More than 20% of the bridges in the United States were built more than 50 years ago, at a time in which intense precipitation events were much less common. However, very little work has been done on the use of scour risk-assessment models to assess how climate change increases bridge failure probabilities. This paper develops a risk-assessment framework based on HYRISK, a model developed to assess the probability of a bridge failure due to scour, and illustrates oneway in which current engineering risk-assessment models can be used to quantify the additional risks and expected economic losses associated with a changing climate. Application of this framework to all bridges in the United States that carry vehicular traffic over water finds that economic losses due to climate change factors will increase by at least 15% over current losses and that the expected number of annual bridge failures in the United States will increase by at least 10% over current failures. Climate-based risk measures, such as those developed as part of this study, could be included in asset management systems to help state DOTs prioritize maintenance, operation, and replacement schedules. DOI: 10.1061/(ASCE)IS.1943-555X.0000109.

Effects of the Traditional and Flipped Classrooms on Undergraduate Student Opinions and Success

AbstractThe 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 shortcom...

The Impact of Bus Door Crowding on Operations and Safety

This study examines how bus design factors influence door crowding and quantifies how door crowding relates to operational performance and passenger safety. Results are based on data collected for 2,807 stops in Dhaka, Bangladesh. Door crowding is affected by multiple bus design factors, including door placement, aisle length, presence of a front seating area, and service type. Increases in door crowding are associated with longer marginal boarding times and an increased number of unsafe boarding and alighting movements that occur when the bus has not come to a complete stop. Results underscore the importance of educating conductors on the dangers associated with door crowding.

Conceptual Framework for Collecting Online Airline Pricing Data: Challenges, Opportunities, and Preliminary Results

The Internet provides new opportunities for aviation firms to develop decision support systems that take advantage of the wealth of detailed online pricing and product information. Although the airline industry has been able to incorporate large volumes of these data systematically into its business models, the academic community has generally conducted its analyses on a small set of nonrepresentative markets. The challenges and opportunities facing researchers who want to collect large volumes of data from airline websites and travel agencies are discussed. Several case studies are used to highlight the types of research questions that can be investigated with this type of data, including how average prices and price dispersion evolve in U.S. markets. A new sample design is proposed to enable researchers to investigate effects caused by carriers’ pricing strategies and multiairport competition.

Lifestyle Segmentation Variables as Predictors of Home-Based Trips for Atlanta, Georgia, Airport

This research investigated the influence of demographic and socio-economic factors on air travel demand by using a unique data set purchased from a credit-reporting agency. Linear regression models based on lifestyle segmentation variables were used to predict air passenger trips for Hartsfield–Jackson International Airport in Atlanta, Georgia. The study focused on predicting trips that originated from or terminated at residences in Atlantas 13-county metropolitan area. The lifestyle regression models were compared with regression models based on income, because the latter were similar to the regression models currently used by the Atlanta Regional Commission to predict home-based airport passenger trips. The results provide directional evidence for using lifestyle clusters over income groups in predicting airport passenger trips. The evidence suggests that alternative data sources with adequate information for lifestyle segmentation can improve airport passenger models. The discussion points out the need for air passenger surveys to collect information about the number of annual air trips a surveyed individual takes.

Estimating Discrete Choice Models with Incomplete Data

In this paper a new approach is developed for estimating discrete choice modeling parameters for data sets in which one of the alternatives is never observed to have been chosen. This estimation approach, inspired by methods used in revenue management, is applied to a multinomial logit model that has a full set of identifiable alternative specific constants. Parameter estimates are found by combining disaggregate data (obtained from a random or exogenous sample) with aggregate observations of market shares for the observed choices. The method provides an estimate of the market share for the alternative that was not observed to be chosen in the estimation data set. This estimate is significant because it suggests that the transportation community can forecast demand for a new alternative or demand for alternatives that are expensive to collect with intercept surveys without having observations for individuals who have chosen these alternatives. The methodology can be extended to other generalized extreme value models.

Airline Planning and Schedule Development

Airlines have evolved over the past 70 years from simple contract mail carriers into sophisticated businesses. The current airline environment is very competitive and dynamic.

Multiairport Choice Models for the New York Metropolitan Area: Application Based on Ticketing Data

This study examines the potential to use online ticketing data to model airport choice for domestic flights originating in one of the three major airports located in the New York City area. Results indicate that airport accessibility and level of service influence airport choice. Results also suggest that capacity constraints—reflected in sold-out flights and higher fares—may lead to more switching across airports as the flight departure dates approach. This underscores the importance of incorporating the actual flights available and the actual prices seen by consumers at the time that they ticket into multiairport choice models.