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The article analyzes the public's response to a hypothetical room tax applied to overnight stays in the Canary Islands. Topics include the use of room taxes in Spain, main findings from the opinion survey about a room tax in Gran Canaria, Spain, and results of stated choice survey and accompanying discrete choice model estimation.

Endogeneity is a potential anomaly in econometric models, which may cause inconsistent parameter estimates. Transport models are prone to this problem and applications that properly correct for it are scarce. This paper focuses on how to address this issue in the case of strategic urban mode choice models (i.e., the third stage of classic strategic transport models), possibly the main tool for the assessment of costly transport projects. To address this problem, we propose and validate, for the first time, adequate instruments that may be obtained from data that is already available in this context. The proposed method is implemented using the Control Function approach, which we use to detect and correct for endogeneity in a case study in Valparaiso, Chile. The effects arising from the neglected endogeneity in this case study reflect on an overestimation between 26–49% of the subjective value of time and an underestimation of 33–75% of modal elasticities. [ABSTRACT FROM AUTHOR]

We revisit the practice of combining revealed (RP) and stated preference (SP) data (i.e., the data enrichment, DE, paradigm) in discrete choice models using secondary data obtained from emerging sources; these facilitate access to massive information about travel choices and can be used to improve transport models. Even though the benefits of the DE paradigm have been known for years, there is a large gap between the state of practice and the state of the art, particularly in Global South countries (but also in many industrialized nations). We use a SP dataset considering two new transport alternatives (train and metro) and a RP dataset based on a large mobility survey in Bogotá, Colombia, complemented with fairly precise level-of-service data obtained using GIS utilities and the Distance Matrix API by Google. Our results allow us to discuss good practice, identify barriers and challenges to the paradigm's application, and draw recommendations for forecasting the demand for new alternatives using joint RP and SP data. [ABSTRACT FROM AUTHOR]

The perception of the environment where we move can be a key factor in choosing a transport mode for our daily trips. For example, many cities lack adequate infrastructure or experience environmental conditions that do not favor cycling. We examine the potential demand for using the bicycle as a transport alternative in trips to college in Barranquilla, Colombia, a city with fairly aggressive environmental conditions for cycling. We designed a Stated Choice experiment geared to students and staff living within three kilometers of two college campuses in the city and traveling by bus or bike. We also considered people's perceptions in an effort to understand the reasons why only 2% of Barranquilla's daily trips are made by bicycle. Our mode choice analysis incorporated two latent variables (unsafety/insecurity and convenience), in addition to typical level-of-service attributes, such as travel time and cost. We found that the perception of unsafety/insecurity has an important negative effect on the probability of using bicycle; the possibility of suffering an accident or being mugged, weighs much more than the possible economic savings and improvements in health associated with using the bicycle. In terms of convenience, our results suggest that it could be just as important to have showers on campus as bike lanes available on the route. Also, investment in facilities such as free parking for bikes, and strategies such as planting trees in bike paths and sidewalks to provide shade, could significantly encourage cycling. [ABSTRACT FROM AUTHOR]

Supplying public transport systems with high levels of service quality is fundamental for retaining users and attracting new ones. Policies that improve transit service quality will ultimately lead to more sustainable travel patterns. Measuring overall service quality implies measuring the quality of several specific attributes and is prevalently evaluated through the perceptions of users, using satisfaction rates. In this study, we demonstrate that there is a further element that can influence users' perceptions, the so-called critical incidents (CI), defined as encounters that are particularly satisfying or dissatisfying. The concept is not restricted to ratings of the predefined product or service attributes, because customers who experience CI remember them well and can usually describe the experience. We implement a framework that includes CI and is innovative for several reasons. Firstly, we introduce attribute-specific (e.g. reliability, safety, comfort) CI to explain attribute-specific satisfaction levels, and then we model these with latent constructs allowing for measurement error in recalling the CI. We also demonstrate that using an Ordinal-Probit approach leads to more accurate results than its numerical counterpart, the latter possibly presenting biased results. Finally, we present a full Structural Equation Multiple Cause Multiple Indicator (SEM-MIMIC) model, which corrects for heterogeneity in the perceptions of users regarding satisfaction with the various service attributes, with the overall service, and with loyalty. For these purposes, we analyse an extensive database (96,763 interviewed passengers) derived from Customer Satisfaction Surveys in the railway services offered in the hinterland of Milan. Our main contribution to the literature is that we show that the occurrence of a CI has a substantial negative impact on passenger satisfaction for all service attributes. As it is a policy-related variable, it can be managed directly by the public transport (PT) administrators. To better plan and improve PT services, avoiding CI in specific items should be the strategy to follow. On the other hand, reliability, and added-value services are the primary service attributes that have a positive effect on satisfaction with the overall service and, in turn, on loyalty. Our model can be useful for PT administrators as it sheds light on how to improve the service according to users' preferences, and by considering the differences among user categories. [ABSTRACT FROM AUTHOR]

The shared taxi is a special public transport mode, typical of Chilean cities. It operates with cars offering a maximum capacity of four seats, a predefined coverage area and a route that is fixed in principle, but can be adapted to meet passengers' needs. During a normal day in Santiago, almost 700,000 trips use shared taxis during one of their stages. This represents about 4% of the total trips made in the city, and this modal share increases in zones and periods with low Metro and bus coverage. This study is a first attempt at studying shared taxis as a relevant transport alternative, analysing its main attributes and modelling its demand. With this purpose, after an analysis of the network and its operation, a revealed preference survey (including perceptual indicators) was applied to public transport users in Santiago who had shared taxi as a feasible alternative. Results show a positive evaluation of the mode's unique attributes, such as the possibility of travelling seated, reducing transfers and alighting at a convenient destination. The subjective valuation of the attributes derived from the models confirm the strong penalty assigned by Chilean users to alternatives implying transfers or increased walking times. The analysis also shows that studying the characteristics of shared taxi users is relevant in a discussion about its regulation and modernization, considering that, while it is desirable to preserve its positive attributes, this should be done in a context of efficient integration with the rest of the transport system. [ABSTRACT FROM AUTHOR]

Although the need for more environmentally friendly vehicles was recognized some decades ago, this new market has not yet established itself. Consumer behavior needs to be studied to ascertain when people will decide to purchase hybrid or electric vehicles rather than conventional ones. An in-depth review of the state-of-the-art has identified existing deficiencies and these are addressed in this paper, proposing a new approach that is applied to the case of Santander in Spain. Emphasis is placed on the role of citizens in researching the local market and their requirements with respect to such vehicles; our model assumes variability in user preferences, an utmost requirement as concluded from the literature review. Results suggest that the highest demand for cleaner vehicles would be achieved in two ways: firstly, by penalizing conventional vehicles in terms of costs/km; secondly, by providing incentives directed at lowering the purchasing price of hybrid and electric vehicles. Finally, as demand becomes more elastic, the preferred strategy should initially focus on hybrid vehicles. [ABSTRACT FROM AUTHOR]

We estimated the benefits associated with reducing fatal and severe injuries from traffic accidents using a stated choice experiment where choice situations were generated through a statistically efficient design. Specifically, the risk variables were defined as the expected annual number of vehicle car-users that suffered their death or were severely injured in a traffic accident. In addition, and differing from previous research, the number of pedestrians that died or were severely injured in traffic accidents per year was also included as a risk attribute in the choice experiment, to attempt at measuring drivers’ willingness to pay to reduce the risk of hitting pedestrians in a crash. The empirical setting was a choice of route for a particular trip that a sample of car drivers periodically undertakes in Tenerife, Spain. Models were estimated accounting for random taste heterogeneity and pseudo-panel data correlation. The median of the distribution of simulated parameters was used to obtain a representative measure for the monetary valuation of risk reductions. We found that the ratio between the values of reducing the risk of suffering a serious injury and that of reducing a fatality was approximately 18 %. Further, and quite novel, we also found that the value of reducing a pedestrian fatality was 39 % of the value of reducing a car occupant fatality. [ABSTRACT FROM AUTHOR]

Forecasting the potential demand for electric vehicles is a challenging task. Because most studies for new technologies rely on stated preference (SP) data, market share predictions will reflect shares in the SP data and not in the real market. Moreover, typical disaggregate demand models are suitable to forecast demand in relatively stable markets, but show limitations in the case of innovations. When predicting the market for new products it is crucial to account for the role played by innovation and how it penetrates the new market over time through a diffusion process. However, typical diffusion models in marketing research use fairly simple demand models. In this paper we discuss the problem of predicting market shares for new products and suggest a method that combines advanced choice models with a diffusion model to take into account that new products often need time to gain a significant market share. We have the advantage of a relatively unique databank where respondents were submitted to the same stated choice experiment before and after experiencing an electric vehicle. Results show that typical choice models forecast a demand that is too restrictive in the long period. Accounting for the diffusion effect, instead allows predicting the usual slow penetration of a new product in the initial years after product launch and a faster market share increase after diffusion takes place. [ABSTRACT FROM AUTHOR]

Travel demand models are usually estimated using cross-sectional data. Although the use of panel data has recently increased in many areas, there are still many aspects that have not been fully analyzed. Some examples of unexplored topics are: the optimal length of panel surveys and the resulting issue of how to model panel data correctly in the presence of repeated observations (for example, several trips per week, by people in a panel with waves every six months) and whether, and to which extent, this affects the efficiency of the estimated parameters and their capability to replicate the true situation. In this paper we analyse this issue and test the effect of including journeys made, with the same characteristics, several times in a week. A broad variety of models accounting for fixed parameters but also for random heterogeneity and correlation among individuals were estimated using real and synthetic data. The real data comes from the Santiago Panel (2006-2008), while the synthetic data were appropriately generated to examine the same problem in a controlled experiment. Our results show that having more observations per individual increases the probability of capturing more effects (i.e. different types of heterocedasticity), but having identical observations in a data panel reduces the capability to reproduce the true phenomenon. Consequently, the definition of panel survey length demands us to consider the implicit level of routine (i.e. the proportion of identical observations) in it. [ABSTRACT FROM AUTHOR]

In this paper we challenge the prevailing practice of conducting one-off cross-sectional mobility surveys, making a case for change on the basis of usefulness and cost-effectiveness. We believe that urban areas over say, one million inhabitants, should collect mobility data on a continuous basis as part of their efforts to guarantee sustainable development. This would allow them to gain a proper understanding of the pressing environmental and transport-related issues of today's world, as well as of the effects of economic growth and price (especially for fuel) volatility. In our scope we include panel data (i.e. information from a smaller sample of respondents who are interviewed at different points in time, hopefully during several years), as this type of information is unique in its ability to help understanding behavioural changes and the impact of time-related effects, such as habit and inertia. We revise the state of affairs in different parts of the world, not limiting ourselves to urban data. [ABSTRACT FROM AUTHOR]

Abasic assumption in mixed revealed preference (RP)/stated preference (SP) estimation is that both data sets represent basically the same phenomenon. Thus, we would expect individuals to show the same tastes regardless of the tool used to elicit their preferences. However, different and significant parameters are often found in each case. Although this is not an issue from an estimation standpoint, understanding why differences appear is crucial in forecasting because the model structure used in that case differs from the estimated one. This problem is compounded if differences between both data affect their ability to reproduce systematic or random taste variations because (i) microeconomic conditions on individual behaviour are more difficult to fulfil, and (ii) an erroneous specification may have a major impact on the predicted results. Problems associated with using joint RP/SP models in forecasting have received scant attention and no studies have examined the case where both types of data show different systematic or random heterogeneity. We review the problem from a theoretical viewpoint and suggest analyses that could aid decision taking in this context. Using real data, we provide evidence on the effects of using different joint RP/SP models in forecasting and highlight the importance of performing these analyses. [ABSTRACT FROM AUTHOR]

Individual choices are affected by complex factors and the challenge consists of how to incorporate these factors in order to improve the realism of the modelling work. The presence of limits, cut‐offs or thresholds in the perception and appraisal of both attributes and alternatives is part of the complexity inherent to choice‐making behaviour. The paper considers the existence of thresholds in three contexts: inertia (habit or reluctance to change), minimum perceptible changes in attribute values, and as a mechanism for accepting or rejecting alternatives. It discusses the more relevant approaches in modelling these types of thresholds and analyses their implications in model estimation and forecasting using both synthetic and real databanks. It is clear from the analysis that if thresholds exist but are not considered, the estimated models will be biased and may produce significant errors in prediction. Fortunately, there are practical methods to attack this problem and some are demonstrated. [ABSTRACT FROM AUTHOR]

The value of road safety is the fundamental input in social cost–benefit analysis of road safety schemes. It is also an increasingly important input in the social evaluation of almost any transport infrastructure project. This value is given by the amount that people are willing to pay for reducing the risk of a becoming a fatal victim or of suffering a serious injury. Traditionally, road safety willingness‐to‐pay has been estimated by means of contingent valuation and other surveys without making explicit reference to a particular travel demand context. The paper advocates the use of stated choice techniques that allow one to recreate the context of a particular trip customized to the respondent’s past experience. For this and other reasons, it is argued that the proposed method is clearly superior to previous methods for estimating people’s willingness‐to‐pay for improved road safety. The paper also provides a summary of the Chilean experience on road safety valuation using stated choice techniques; and it concludes by showing the importance of conducting local studies to elicit people’s willingness to pay for safety. [ABSTRACT FROM AUTHOR]

We examined different model specifications to detect the presence of preference heterogeneity in a mode choice context. The specification that worked best allows for both systematic and random variations in tastes, with parameters obtained at the individual level using Bayesian methods. Subjective values of travel time (SVT) and expected individual compensated variation were derived and aggregated to obtain measures of social welfare. Results suggest that the benefit measures, both at the individual and at the social level, are sensitive to preference heterogeneity assumptions. SVT and welfare changes derived from travel time reductions could be underestimated if the traditional assumption of taste homogeneity is made (we detected differences up to 30% in both types of measures). We also obtained an empirical value for the error made when evaluating changes in social welfare using an approximation of the expected individual compensated variation (expressed as a function of individual SVT) rather than its exact expression. [ABSTRACT FROM AUTHOR]

Mixed-logit models are currently the state of the art in discrete-choice modelling, and their estimation in various forms (in particular, mixing revealed-preference and stated-preference data) is becoming increasingly popular. Although the theory behind these models is fairly simple, the practical problems associated with their estimation with empirical data are still relatively unknown and certainly not solved to everybody's satisfaction. In this paper we use a stated-preference dataset—previously used to derive willingness to pay for reduction in atmospheric pollution and subjective values of time—to estimate random parameter mixed logit models with different estimation methods. We use our results to discuss in some depth the problems associated with the derivation of willingness to pay with this class of models. [ABSTRACT FROM AUTHOR]

The state-of-practice in the design and conduction of mobility surveys in metropolitan areas leaves much to be desired, even in most industrialized countries. It is argued that the state-of-the-art in this type of survey is a continuous (ongoing) method, beginning with a somewhat larger sample size in the first year and with smaller sample sizes in subsequent years. The paper discusses three issues related to this methodology. First, the sampling implications; in particular, sample sizes are considered based on stratifications by income, car ownership and household size, designed to estimate trip rates, zone-level car ownership and mode choice models for different strata. Second, the survey method implications, where state-of-the-art and -of-practice considers collecting data for each day of the week during the whole year. Third, the important issues of data correction and weighting, and the need for independent data and validation strategies. The paper concludes with a brief report on the main results of a 1-year pilot study of these recommendations as applied to Santiago, Chile, and with some preliminary data about the first wave of the large-scale mobility survey currently under way in that city that serves to illustrate the magnitude of the effort and to get a feeling about the quality of the approach. [ABSTRACT FROM AUTHOR]

Presents comments on an article by David Metz on the limitation of transport policy. Agreement with Metz's conclusion that there is no possibility of meeting the demand for unconstrained movement through the construction of additional transport infrastructure; Disagreement with many of Metz's other arguments; Description of the transport problems in developing cities; Ways to solve the congestion problem associated with transport.

The popular hierarchical or nested logit model that has been the recent source of heated discussions in the literature is reviewed and critically examined. The fundamental underpinnings of the model are first presented in a concise and easy-to-understand fashion and then used to assess each controversy in turn. Monte Carlo simulation is also used to examine some problems that are not possible to address by just resorting to the theory. The main conclusion is that all the model hypotheses are both essential and unambiguous and should not be ignored for a correct use of the model in practice. [ABSTRACT FROM AUTHOR]

In the past 25 years social housing programmes in Chile have taken three basic forms. During the military regime the traditional basic housing and a neighbourhood upgrading programme operated, while in 1990 the new democratic authorities added the progressive housing line of action. As there was scope for improving these programmes, a research project was set up with the aim of first identifying, and then finding, the relative importance of social housing attributes for different types of dwellers. Three surveys were considered in the project: a two-wave Delphi survey to specialists in the field and a focus group survey to current and potential beneficiaries, in order to identify the most relevant housing attributes, and a stated-preference survey to a sample of five types of social housing dwellers. With the latter data discrete choice models were estimated, which allowed the relative weight of (and subjective valuation, or willingness to pay for) each such attribute to be determined. The methodological features of the project are presented and its main results are discussed. [ABSTRACT FROM AUTHOR]

The article discusses various reports published within the issue, including one by Peter Bickel and Chris Nash on the result of European research project for transportation.