Latent temporal preferences: An application to airline travel

An 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 accounts 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. 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.     

Stochastic user equilibrium with equilibrated choice sets: Part I – Model formulations under alternative distributions and restrictions

The aim of this paper is to remove the known limitations of Deterministic and Stochastic User Equilibrium (DUE and SUE), namely that only routes with the minimum cost are used in DUE, and that all permitted routes are used in SUE regardless of their costs. We achieve this by combining the advantages of the two principles, namely the definition of unused routes in DUE and of mis-perception in SUE, such that the resulting choice sets of used routes are equilibrated. Two model families are formulated to address this issue: the first is a general version of SUE permitting bounded and discrete error distributions; the second is a Restricted SUE model with an additional constraint that must be satisfied for unused paths. The overall advantage of these model families consists in their ability to combine the unused routes with the use of random utility models for used routes, without the need to pre-specify the choice set. We present model specifications within these families, show illustrative examples, evaluate their relative merits, and identify key directions for further research.     

Transition choice probabilities and welfare analysis in random utility models with imperfect before–after correlation

Welfare in random utility models is used to be analysed on the basis of only the expectation of the compensating variation. De Palma and Kilani (De Palma, A., Kilani, K., 2011. Transition choice probabilities and welfare analysis in additive random utility models. Economic Theory 46(3), 427–454) have developed a framework for conditional welfare analysis which provides analytic expressions of transition choice probabilities and associated welfare measures. The contribution is of practical relevance in transportation because it allows to compute shares of shifters and non-shifters and attribute benefits to them in a rigorous way. In De Palma and Kilani (2011) the usual assumption of unchanged random terms before and after is made.The paper generalises the framework for conditional welfare analysis to cases of imperfect before–after association of the random terms. The joint before–after distribution of the random terms is introduced with postulated properties in terms of marginal distributions and covariance matrix. Analytic expressions, based on the probability density function and the cumulative distribution function of the joint before–after distribution, and simulation procedures for computation of the transition choice probabilities and the conditional expectations of the compensating variation are provided. Results are specialised for multinomial logit and probit. In the case without income effects, it is proved that the unconditional expectation of the compensating variation depends only on the marginal distributions.The theory is illustrated by a numerical example which refers to a multinomial logit applied to the choice of the transport mode with two specifications, one without and one with income effects. Results show that transition probabilities and conditional welfare measures are affected significantly by the assumption on the before–after correlation. The variability in the transition probabilities across transitions tends to decrease as the before–after correlation decreases. In the extreme case of independent random terms, the conditional expectations of the compensating variation tend to be close to the unconditional expectation.     

A mixed logit model for predicting exit choice during building evacuations

Knowledge on human behaviour in emergency is crucial to increase the safety of buildings and transportation systems. Decision making during evacuations implies different choices, of which one of the most important concerns is the escape route. The choice of a route may involve local decisions on alternative exits from an enclosed environment. This study investigates the effect of environmental (presence of smoke, emergency lighting and distance of exit) and social factors (interaction with evacuees close to the exits and with those near the decision-maker) on local exit choice. This goal is pursued using an online stated preference survey carried out making use of non-immersive virtual reality. A sample of 1503 participants is obtained and a mixed logit model is calibrated using these data. The model shows that the presence of smoke, emergency lighting, distance of exit, number of evacuees near the exits and the decision-maker and flow of evacuees through the exits significantly affect local exit choice. Moreover, the model indicates that decision making is affected by a high degree of behavioural uncertainty. Our findings support the improvement of evacuation models and the accuracy of their results, which can assist in designing and managing building and transportation systems. The main aim of this study is to enrich the understanding of how local exit choices are made and how behavioural uncertainty affects these choices.     

An assessment of UK drivers’ attitudes regarding the forthcoming ban on the sale of petrol and diesel vehicles

The purpose of the study was to predict how drivers of petrol or diesel cars might vote in a ‘Yes/No’ referendum concerning the UK government’s decision to ban from 2040 onwards the sale of all new non-electric vehicles. Five main factors were hypothesised to influence voting intention: a person’s (i) level of environmental concern, (ii) attitude towards electric cars (measured via an Implicit Association Test), (iii) belief about the importance of air pollution, (iv) driving requirements, and (v) reaction to the cost of the ban to the individual (assessed using a contingency valuation approach). The study also examined possible determinants of attitudes to electric vehicles, e.g., whether an individual was a ‘technology enthusiast’, had prior knowledge of and searched for knowledge about electric vehicles, and whether a person had played an online game where the player assumed the identity of an electric car driver. A structural equation model was developed and tested on a sample of 675 UK drivers, none of whom had ever owned or driven an electric car. The results suggested a good fit of the model to the data, except that neither environmental concern nor belief in the importance of clean air affected attitude to electric cars. Also, high levels of environmental concern did not motivate people to search for knowledge about electric vehicles. Social marketing campaigns that will be needed to precede the ban should focus on its health benefits, and not target particular age groups, gender, or whether a participant had children.     

A measurement and Comparison of Cost Competitiveness of Container Ports in Southeast Asia

Terminal users face a variety of costs associated with the container terminal and those that are harder to quantify should not be ignored for they might represent a larger component of the overall costs associated with using a particular terminal. Similarly, the competitive advantage of a container terminal operator goes beyond the elements that can be quantified. The paper uses and modifies Cournot’s simultaneous quantity-setting model as a means to derive the overall costs of using the terminal. The application of this model to the perspective of competition between container terminal operators in Singapore, Port Klang and Tanjung Pelepas finds that the increasingly cost competitive operators in Port Klang and Tanjung Pelepas were able to close the gap with PSA Corporation in Singapore in the overall costs of using their terminal facilities between 1998 and 2002 although PSAC continued to enjoy a dominant share of the container-handling market in the region. The paper also highlights the tremendous amount of opportunities available to these terminal operators to advance and capitalise on their competitive advantages beyond aggressive price competition.The author would like to stress that the views presented do not represent those of his organization.     

Applications of Random-Utility-based Multi-region Input–Output Models of Transport and the Spatial Economy

Abstract

An introduction to random-utility-based multiregional input–output models used for the purpose of spatial economic and transport interaction modelling is provided. The main methodological developments and important results of a dozen applications from the years 1996–2013 are described. This is followed by an outlook of potential future directions. Further research is mainly needed in five areas: (a) overall validation of the method, perhaps through back-casting applications on infrastructure plans with observed trade impacts; (b) extensions of trade coefficient models to add realism and improve accuracy; (c) the use of multi-scale modelling to capture interdependencies between geographical scales and to improve the representation of exports and imports; (d) improvements in the representation of price effects, as well as innovation and technological progress, by way of variable technical coefficients; and (e) a deeper investigation of the algorithm used to include elastic selling prices.