Mode Choice Results Sample Clauses

Mode Choice Results. Separate error component models were applied for transit users and auto users to estimate travelers’ mode choice while incorporating latent attitudinal factors. Respondents were faced with three alternatives in each scenario: public transit, exclusive on-demand services, and shared on-demand services. Table 23 and Table 24 presents the results of the error component models, for transit users and auto users, respectively. Travel time and travel costs were treated as random parameters, and transit was considered as the base category. The final model embraces several socio-economic and demographic characteristics as well as attitudinal factors that offer a deeper vision on the role of attitudes in the propensity to use ridesourcing services. The tables present the variables that are significant at a 90% confidence level.
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Mode Choice Results. An error component model with a nested structure was used to estimate travelers’ mode choice while incorporating habitual behavior. As indicated, respondents were faced with three alternatives in each scenario: conventional modes, exclusive on-demand services, and shared on- demand services. One nest was considered for the emerging modes, including exclusive and shared on-demand services. The model results are summarized in Table 20 and Table 21, for regular (frequent) and occasional trips, respectively. Travel time and travel cost are considered as random parameters, the mean and standard deviation values are presented in the tables. Vehicle availability is an alternative specific variable which only applies to private vehicle passenger alternative. Conventional modes (i.e., private vehicle driver, private vehicle passenger, and public transit) are considered as the base category for all other parameters. The tables only present variables that showed significant impacts at a 95% confidence interval. Both models showed a reasonable overall model fit based on 2 values, and the likelihood ratio test were found to be significant at a level greater than 99 percent. Both models showed significant standard deviation values for travel time and travel cost suggesting taste variations among the individuals. In terms of correlations between the two on-demand service alternatives, both models presented significant variance between the nest, n= 0.613 (t=12.51) for regular trips, and n= 0.189 (t=15.07) for occasional trips, indicating the presence of common unobserved factors in choosing emerging modes. This might indicate that the decision on whether to use ridesourcing services could be viewed as a binary decision at a higher level, with substitution patterns between the two ridesourcing alternatives at the lower level. In particular, this effect was stronger for regular trips, which exhibited a higher covariance value than occasional trips. This is reasonable considering that for regular or long-term mobility arrangements, the decision is more likely to involve a higher-level choice on whether to adopt on-demand services or not. Since the purpose of this study is to gauge the impacts of habitual behavior, we focus on habitual related variables. A quick review of the model results for regular trips (Table 20) reveals that the other variables, including alternative specific attributes and socio-economic demographic variables showed reasonable coefficient values in ter...
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