Preference Heterogeneity and Willingness to Pay for Travel Time Savings

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.     

Modeling heterogeneous parking choice behavior on university campuses

Studies on campus parking indicate more severe problems and a wider range of characteristics than commercial parking because of limited parking places, special conditions, specific policies and enclosed space on university campuses. Heterogeneous characteristics are usually ignored in analyses of campus parking behavior. In this paper, a mixed logit model is applied to analyze parking choice behavior on a campus using data collected from a stated-preference survey of Tongji University, Shanghai, China. The heterogeneity of individuals with various sociodemographic characteristics is evaluated by interaction terms and random parameters. Comparison between the proposed approach and the conditional logit model shows that the results of the mixed logit model are more interpretable because they are not limited by the independence from irrelevant alternatives assumption. Key factors that have considerable effects on campus parking choices are identified and analyzed. Important regularities are also concluded from elasticity analyses. Finally, the campus is divided into two areas according to the walking distance to a new parking lot, and the modeling results show that area-specific policies should be established because the two areas have quite distinct parking choice features.     

The valuation of commuter travel time savings for car drivers: evaluating alternative model specifications

The empirical valuation of travel time savings is a derivative of the ratio of parameter estimates in a discrete choice model. The most common formulation (multinomial logit) imposes strong restrictions on the profile of the unobserved influences on choice as represented by the random component of a preference function. As we progress our ability to relax these restrictions we open up opportunities to benchmark the values derived from simple (albeit relatively restrictive) models. In this paper we contrast the values of travel time savings derived from multinomial logit and alternative specifications of mixed (or random parameter) logit models. The empirical setting is urban car commuting in six locations in New Zealand. The evidence suggests that less restrictive choice model specifications tend to produce higher estimates of values of time savings compared to the multinomial logit model; however the degree of under-estimation of multinomial logit remains quite variable, depending on the context.     

A stated preference examination of time of travel choice for a recreational trip

A stated preference experiment was performed in Calgary in Canada to examine how people are influenced in the selection of a departure time for a hypothetical trip to see a movie. A total of 635 complete observations were obtained. In each observation the respondent was presented with a set of possible departure time scenarios and asked to indicate the order of preference for these scenarios. Each scenario was described by specifying the automobile travel time, the expected arrival time relative to the movie start time, the parking cost, the probability of being at least ten minutes late for the movie and the length of time the movie had been running. This forced the respondent to trade off between conditions regarding these attributes. Age, gender and frequency of movie attendance were also recorded. The observations thus obtained were used to estimate the parameter values for a range of alternative utility functions in logit models representing this choice behaviour. The results indicate that all of the attributes included have significant effects on departure time choice in the situation being considered. They also indicate that travellers are prepared to arrive roughly two minutes early for each minute of travel time saved; that the money value of driving time for trips to recreational activities is about half that for trips to work; that one additional percent in the probability of arriving late is equivalent to roughly 0.20 Canadian dollars or 1.93 minutes drive time; and that there is a preference for a non-zero expected early arrival time regardless of the associated probability of arriving late. Some of these results are novel and others are consistent with findings for work trips in work done by others, which is seen to add credence to the approach being used here.     

Inconsistent choices in Stated Choice data;Use of the logit scaling approach to handle resulting variance increases

The scaling approach is a statistical estimation method that allows for differences in the amount of unexplained variation in different types of data, which can then be used together in the analysis. This approach has been mostly used in context of combining Stated Preference and Revealed Preference data, but has also been used as a method of identifying systematic differences in the variance of choices within a single Stated Preference data set, e.g. for investigation of learning and fatigue effects. This paper investigates whether a scaling approach is suitable for handling inconsistencies in Stated Choice data. Both the number of inconsistent choices, based on a test of violations of the transitivity axiom, and education are used as scaling variables. Scaling effects appear to exist due to inconsistent choices, and the amount of unexplained variance is shown to increase as the number of inconsistent choice increase. Scaling due to inconsistencies significantly improves the models and reduces the valuations of travel time. In addition, the scaling approach makes the valuations of travel time from the Stated Choice data more consistent with the valuations from Contingent Valuation data included in the same study. In spite of the fact that education is the only significant explanatory variable for the number of inconsistent choices, scaling due to education gives no significant improvement in the model.