Ambivalence, that is holding positive and negative evaluations about a single topic, has traditionally been studied in relation to specific attitude objects. However, prior findings suggest that some individuals may be more ambivalent than others, regardless of the attitude object. To investigate this possibility, we conducted a registered secondary analysis of two existing datasets. In both datasets, individuals (ns = 41; 111) rated multiple stimuli (DS1=29; DS2=61) drawn from widely-used normed sets (IAPS; ANEW). Results indicate that ambivalence varies more strongly with individuals than with stimuli. Individual differences account for 16-28% of variance whereas the differences between stimuli only account for 3-10%. Furthermore, we document substantial individual differences in the relationships between different aspects of ambivalence. These findings show that levels of ambivalence are as much a function of the person as they are of the attitude object.

We describe a model for predicting subjective user satisfaction based on objective ride conditions. The model takes ride features (e.g., duration, crowding, waiting time) as inputs and estimates users' single-ride satisfaction. This model enables transit authorities to predict the effect of service changes on user satisfaction. This in turn makes it possible to compare multiple candidate system configurations to determine which would yield the highest level of satisfaction. Using a sample of mass rapid transit users (n=641), the model is first trained then validated against unseen data. Prediction uncertainty is accounted for using a residual term. We also provide an example of how this model can be used to derive predictions, using artificial data. Highlights We derive a predictive model of single-ride satisfaction from a diverse sample of transit users (n=641). Transit authorities can use this model to predict the impact of changes to service on rider satisfaction. Validation analysis is provided to confirm the model's applicability to new cases. PREDICTIVE MODEL OF TRANSIT SATISFACTION 3

Not all transit users have the same preferences when making route decisions. Understanding the factors driving this heterogeneity enables better tailoring of policies, interventions, and messaging. However, existing methods for assessing these factors require extensive data collection. Here we present an alternative approach - an easily-administered single item measure of overall preference for speed versus comfort. Scores on the self-report item predict decisions in a choice task and account for a proportion of the differences in model parameters between people (n=298). This single item can easily be included on existing travel surveys, and provides an efficient method to both anticipate the choices of users and gain more general insight into their preferences.