Agent-based transport simulations rely on realistic representation of agents’ movements in the system, but also simulation choice making processes that resemble reality well. The MATSim framework uses scoring-based co-evolutionary algorithm to achieve this, based principle utility maximization. Discrete models are another common tool planning which same principle. Through a toy example, paper a...

We consider the estimation of dynamic binary choice models in a semiparametric setting, which per-period utility functions are known up to finite number parameters, but distribution shocks is left unspecified. This setup differs from most existing identification and literature for discrete models. To show we derive exploit new recursive representation unknown quantile function shocks. Our estim...

Marketing practitioners and academics have shown a keen interest in the processes that drive consumers’ choices since the early work of Guadagni and Little (1982). Over the past decade or so, a number of alternative models have been proposed, implemented and analyzed. The common behavioral assumption that underlines these models of discrete choice is random utility maximization (RUM). The RUM a...

There is a growing recognition that discrete choice models are capable of providing a more realistic picture of route choice behavior. In particular, influential factors other than travel time that are found to affect the choice of route trigger the application of random utility models in the route choice literature. This paper focuses on path-based, logit-type stochastic route choice models, i...