Bayesian Procedures for Modeling Truck Route Choices

This study examines logit models applied to the truck route choice problem using GPS trucking data from the Dallas metropolitan area. Instead of assuming a constant coefficient for each variable in the conventional multinomial logit model, the proposed mixed c-logit model assumes a certain probability distribution for each coefficient, in an attempt to better reflect the drivers’ preference heterogeneity. A commonality factor is introduced in the model to address roadway travel time correlations due to route overlaps. Three Bayesian models with different hierarchy levels are introduced and are solved using mean-field variational inference with the block coordinate algorithm. In the reduced subnetwork of the examined area, the drivers are assumed to make their route choices in three groups of routes, referred to as choice groups. Attributes that would affect the truck driver’s route choice decisions are different among choice groups. With this setting, the proposed Bayesian models are then tested with the three truck route choice groups respectively. Generally, the study finds that the factors considered in truckers’ route choice vary with context.