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The proposed research contributes to our understanding of incorporating heterogeneity in discrete choice models with respect to exogenous variables and decision rules. Specifically, the proposed latent segmentation based mixed models segment population to different classes with their own decision rules while also incorporating unobserved heterogeneity within the segment level models. In our analysis, we choose to consider both random utility and random regret theories. Further, instead of assuming the number of segments (as 2), we conduct an exhaustive exploration with multiple segments across the two decision rules. The model estimation is conducted using a stated preference data from 695 commuter cyclists compiled through a web-based survey. The probabilistic allocation of respondents to different segments indicates that female commuter cyclists are more utility oriented; however, the majority of the commuter cyclist’s choice pattern is consistent with regret minimization mechanism. Overall, cyclists’ route choice decisions are influenced by roadway attributes, cycling infrastructure availability, pollution exposure, and travel time. The analysis approach also allows us to investigate time based trade-offs across cyclists belonging to different classes. Interestingly, we observe that the trade-off values in regret and utility based segments for roadway attributes are similar in magnitude; but the values differ greatly for cycling infrastructure and pollution exposure attributes, particularly for maximum exposure levels.

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Given the burgeoning growth in transport networking companies (TNC)-based ride hailing systems and their growing adoption for trip making, it is important to develop modeling frameworks to understand TNC ride hailing demand flows at the system level. Two choice dimensions are identified: (1) a demand component that estimates origin level TNC demand at the taxi zone level and (2) a distribution component that analyzes how these trips from an origin are distributed across the region. The origin level demand is analyzed using linear mixed models while flows from origin to multiple destinations is analyzed using a multiple discrete-continuous extreme value (MDCEV) model. The data for the analysis is drawn from New York City Taxi and Limousine Commission for 12 months from January through December 2018. For this analysis, weekday morning peak hour demand and distribution patterns are examined. The model components are developed using a comprehensive set of independent variables. The model estimation results offer very intuitive results for origin demand and distribution of flows across destinations. The model was validated by predicting trips to destination taxi zones and it was found that predicted model performs well in identifying high preference destination zones. In addition, elasticity effects are computed by evaluating the percentage change in baseline marginal utility in response to increasing the value of exogenous variables by 10%, 25% and 50%, respectively.

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