Luis Ignacio Rizzi

Estimating the Willingness‐to‐Pay for Road Safety Improvements

Abstract 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.

The impact of traffic images on travel time valuation in stated-preference choice experiments

We develop a simple but modern stated-preference (SP) choice experiment to analyse the impact of traffic images on the estimated value of travel time savings (VTTS). A random subsample of survey respondents view computer-generated images that depict traffic conditions for congested and free-flow portions of hypothetical travel alternatives. The remaining respondents receive otherwise-identical surveys that only verbally describe the attributes of those alternatives. From surveys with images, the estimated VTTS is

Development of Surveys for Study of Departure Time Choice

7.40 per hour for congested travel time and

The influence of built environment and travel attitudes on walking: A case study of Porto Alegre, Brazil

5.70 for free-flow travel time, implying a ‘congestion premium’ of 30%. From surveys without images, however, congested and free-flow VTTS estimates are identical at

Estimating the Value of Risk Reduction for Pedestrians in the Road Environment: An Exploratory Analysis

5.90 per hour. Our results indicate that traffic images, however rudimentary, can substantially influence travel-time valuation, thus compelling further research on incorporating them into SP experimental design.

Valuing air quality impacts using stated choice analysis: trading off visibility against morbidity effects.

Modeling of departure time choice has recently received renewed attention because of the increasing levels of congestion in many cities and the growing popularity of travel demand management strategies such as road pricing. Current practice for evaluation of the effectiveness of travel demand management policies usually involves incorporation of the temporal dimension into transport planning models only through fixed factors derived from origin–destination data, but this practice makes them unsuitable for use in proper prediction of demand at different times of the day. To mitigate these deficiencies, the authors argue in favor of estimation and application of specially formulated time-of-day choice models. Here the concentration is on the process used to generate a survey design that obtains data suitable for estimation of such models that will ensure both realism and simplicity in the presentation; in particular, the stated preference exercise includes dependence between attribute levels. The proposed procedure should be widely applicable and offers many improvements over current practice in the field.

Survey Data to Model Time-of-Day Choice: Methodology and Findings

ABSTRACT Studies conducted in cities from developed countries reveal associations between travel behavior and built environment. A priori we would expect that most of these associations hold for cities from developing countries but we do not have any basis to suppose whether or not they will be quantitatively the same because of differences in the nature of urban form between cities of developed and developing countries, and potential differences in attitudes and travel preferences from their travelers. This article analyzes the relationship between walking patterns, travel attitudes, and neighborhoods urban structure in Porto Alegre, Brazil, under the assumption that built environment affects travel attitudes, instead of the more typical assumption that goes the other way around. We develop a two-step ordered choice model enriched with attitudinal variables to integrate travel attitudes, built environment, and travel behavior. A multiple indicator multiple cause (MIMIC) model is used to identify travel attitude as latent constructs that feed a frequency-of-walking ordered logit model with both the built environment and travel attitudes as explanatory variables. Our results support the a priori assumption that we would replicate many of the results already reported in the literature. However, we obtained the same differences in the magnitudes of some key elasticities. The results of the elasticities computed for the number of walking trips with respect to the built environment variables were smaller than in other studies. Only the effect of population density on walking frequency seems to be marginally higher.

Valuation of Transport Externalities by Stated Choice Methods

Abstract In recent years there has been a re-focus on the valuation of a statistical life from the ex post or human capital method to an ex ante willingness to pay (WTP) approach. This is in part a recognition that we need to focus on establishing the amount, ex ante, that individuals are willing to pay to reduce the risk of exposure to circumstances that might lead to death or degree of injury in the road environment. This study sets out a framework in which to identify the degree of preference heterogeneity in WTP of pedestrians to avoid being killed or injured. A stated choice experiment approach is developed. The empirical setting is a choice of walking route for a particular trip that a sample of pedestrians periodically undertakes in Australia. Mixed logit models are estimated to obtain the marginal (dis)utilities associated with each influence on the choice amongst the attribute packages offered in the stated choice scenarios. These conditional estimates are used to obtain the WTP distributions for fatality and classes of injury avoidance, which are then aggregated to obtain estimates for pedestrians of the value of risk reduction (VRR).

Diseño de Instrumentos Económicos para la Internalización de Externalidades de Accidentes de Tránsito

Direct valuation of air quality has as a drawback; that estimated willingness to pay figures cannot be apportioned to the several environmental goods affected by air quality, such as mortality and morbidity effects, visibility, outdoor recreation, among others. To address this issue, we implemented a survey in Santiago de Chile to identify component values of confounded environmental services by means of a choice experiment. We designed a survey where two environmental goods, a morbidity health endpoint and improved visibility, had to be jointly traded off against each other and against money in a unified framework. The health endpoint is a respiratory illness that results in an emergency room visit with a probability of hospitalization being required for appropriate treatment. Visibility is described as an aesthetic effect related to the number of days per year of high visibility. Modeling comprises both a logit model with covariates and a mixed-logit model. The results suggest that the health endpoint midpoint value is in a range from USD 2,800 to USD 13,000, mainly depending on the model and age stratum. The mid point value of an extra day of high visibility per year ranges from USD 281,000 to USD 379,000.

Cost‐Effective Measures for Reducing Road Fatalities in the Short Term

Abstract Purpose – Departure time choice not only depends on the desire to carry out activities at certain times and places; it is a complex decision making process influenced by travel conditions, congestion levels, activity schedules, and external trip factors. To estimate departure time choice models capturing the factors influencing it in appropriate form, a complex data collection procedure allowing to obtain detailed input data from different sources and at different time periods is required. The main aim of this chapter is to describe and discuss the survey methodology we used in a time-of-day choice project, involving the collection of revealed preference (RP) and stated preference (SP) data to estimate hybrid discrete departure time choice models incorporating latent variables. Preliminary model results are also presented as an example. Methodology/approach – Data was obtained from 405 workers at different private and public institutions located in the centre of Santiago, Chile. The survey process had three different stages and used various collection methods (e-mail, web-page, and personal interviews at the workplace) in order to satisfy efficiency, reliability and cost criteria. The RP component survey design was based on the last origin-destination survey implemented in Santiago (i.e. a travel diary filled under an activity recall framework). Relevant level-of-service measures at different time periods were obtained from GPS data measured from instrumented vehicles in the public and private transport networks. A SP-off-RP optimal design considering dependence among attribute levels was also developed. Finally, several 1–7 Likert scale questions were included to incorporate the latent variables. Findings – The survey methodology described in this chapter represents a successful experience in terms of collecting high quality data, from different sources, with the aim of estimating appropriate time-of-day choice models. The data collection process was carried out in different stages, by means of web pages, email, and personal interviews. The data was further enriched with level-of-service attributes measured at different times of the day with unusual precision. Preliminary results reported in this chapter show that data obtained through this methodology are appropriate to model time-of-day choices. Originality/value of chapter – The novelty of the survey methodology described in this chapter is the collection of data of a different nature for time-of-day choice modelling through the integration of different collection techniques. Acquisition of very precise information about preferred departure/arrival times, level of service at different times of the day, detailed information about flexibility in schedules, employment information and attitudes towards departure times, should allow practitioners to estimate hybrid time-of-day choice models incorporating latent variables.