The Design of Stated Choice Experiments: The State of Practice and Future Challenges

Since the work of Louviere and Woodworth (1983) and Louviere and Hensher (1983), stated choice (SC) methods have become the dominant data paradigm in the study of behavioural responses of individuals and households as well as other organizations, in fields as diverse as marketing, transport and environmental and health economics, to name but a few. In SC experiments, it is usual for sampled respondents to be asked to choose from amongst a number of labelled or unlabelled alternatives defined on a number of attribute dimensions, each in turn described by pre-specified levels drawn from some underlying experimental design. The choice task is then repeated a number of times, up to the total number of choice sets being offered over the experiment. Several experimental design strategies are available to the practitioner, however, within the transport literature, it appears that the most common form of experimental design used are orthogonal fractional factorial designs. In this paper we review the properties of such designs, and demonstrate that these properties are unlikely to be retained through to the estimation process. We also discuss an alternative design construction strategy, used to construct statistically optimal designs

Efficiency and Sample Size Requirements for Stated Choice Studies

Stated choice (SC) experiments represent the dominant data paradigm in the study of behavioral responses of individuals, households as well as other organizations, yet little is known about the sample size requirements for models estimated from such data. Current sampling theory does not adequately address the issue and hence researchers have had to resort to simple rules of thumb or ignore the issue and collect samples of arbitrary size, hoping that the sample is sufficiently large enough to produce reliable parameter estimates. In this paper, we demonstrate how to generate efficient designs (based on D-efficiency and a newly proposed sample size S-efficiency measure) using prior parameter values to estimate multinomial logit models containing both generic and alternative-specific parameters. Sample size requirements for such designs in SC studies are investigated. In a numerical case study is shown that a D-efficient and even more an Sefficient design needs a (much) smaller sample size than a random orthogonal design. Furthermore, it is shown that wide level range has a significant positive influence on the efficiency of the design and therefore on the reliability of the parameter estimates

Sample optimality in the design of stated choice experiments

Stated choice (SC) experiments represent the dominant data paradigm in the study of behavioral responses of individuals, households as well as other organizations, yet little is known about the sample size requirements for models estimated from such data. Current sampling theory does not adequately address the issue and hence researchers have had to resort to simple rules of thumb or ignore the issue and collect samples of arbitrary size, hoping that the sample is sufficiently large enough to produce reliable parameter estimates. In this paper, we demonstrate how to generate efficient designs (based on D-efficiency and a newly proposed sample size S-efficiency measure) using prior parameter values to estimate multinomial logit models containing both generic and alternative-specific parameters. Sample size requirements for such designs in SC studies are investigated. Using a numerical case study, we show that using S-efficiency can substantially reduce the sample size required of SC studies

Efficient Designs for Alternative Specific Choice Experiments

In the past, research on the construction of efficient designs for stated choice experiments has been limited to unlabeled experiments with generic parameter estimates. In this paper, by deriving the asymptotic (co)variance matrix for the alternative-specific MNL model, the authors are able to generate efficient alternative-specific experiments. The authors show that D-error assuming prior parameter values equal to zero is unable to explain statistical efficiency in orthogonal designs and that wide attribute levels are likely to yield more reliable parameter estimates than using narrow attribute levels. The authors also show that the D-optimality criterion may yield inefficient parameter estimates for some design attributes given that trade-offs are made between the efficiencies of different parameter estimates

Modelling speed reduction behaviour on variable speed limit-controlled highways considering surrounding traffic pressure: a random parameters duration modelling approach

Variable speed limits are frequently used to improve traffic safety and harmonise traffic flow. This study investigates how, and to what extent, drivers reduce their speed upon passing a variable speed limit sign. We specifically consider the impact on braking behaviour due to the systematic inclusion of different social pressures exerted by surrounding traffic. This social pressure is the natural result of having two vehicle cohorts created by a change in the variable speed limit (the new speed limit being higher than the original). The cohort with the higher speed limit overtakes vehicles with the lower speed limit, instigating a specific passing rate on drivers in the lower speed cohort. A driving simulator study is employed to obtain individual driver data whilst being able to systematically change the social pressure applied. A sample comprising sixty-seven participants conducted multiple randomised drives, with varying passing rates from as low as 90 veh/h to as high as 360 veh/h. The speed reduction behaviour of the participants is modelled using a random parameter duration modelling approach. Both the panel nature of the data and unobserved heterogeneity are captured through a correlated grouped random parameters with heterogeneity-in-the-mean model. The random parameters are predicated on the different passing rate scenarios, allowing drivers to take shorter or longer to reduce their speeds compared to the reference passing rate. It is shown that the extent of social pressure impacts braking behaviour and therefore affects safety measures, which is a function of the magnitude of the speed limit change. In addition, an extensive decision tree analysis is conducted to understand differential braking behaviour. Results reveal that, on average, female drivers take a shorter time to reduce their speed under a high passing rate but longer in a low passing rate scenario compared to males. Similarly, young drivers are found to take longer to reduce their speeds in a high passing rate scenario compared to other age groups. Our main findings indicate that the within-cohort safety is lowest under low passing rates due to comparatively larger speed differences between drivers. Yet, under a high passing rate, we observe an increase in violation of the speed limit by the lower speed limit vehicles (but less within cohort speed differences). Whilst normally this would be an undesired effect across cohorts, this violation is argued to lead to increased safety due to the smaller discrepancy in speed.</p