Stochastic User Equilibrium Traffic Assignment with Price-sensitive Demand: Do Methods matter (much)?

We compare three stochastic user equilibrium traffic assignment models multinomial probit, nested logit, and generalized nested logit), using a congestible transport network. We test the models in two situations: one in which they have theoretically equivalent coefficients, and one in which they are calibrated to have similar traffic flows. In each case, we examine the differences in traffic flows between the SUE models, and use them to evaluate policy decisions, such as profit-maximizing tolling or second-best socially optimal tolling. We then investigate how the optimal tolls, and their performance, depend on the model choice, and hence, how important the differences between models are. We show that the differences between models are small, as a result of the congestibility of the network, and that a better calibration does not always lead to better traffic flow predictions. As the outcomes are so similar, it may be better to use computationally more efficient logit models instead of probit models, in at least some applications, even if the latter is preferable from a conceptual viewpoint.