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Map matching algorithms try to map a series of location observations to an underlying network, to generate a single hypothesized true location or route. For some applications, including route choice modeling, such map matching is not required, and may intr ...
Discrete choice models are defined conditional to the knowledge of the actual choice set by the analyst. The common practice for many years is to assume that individual-based choice sets can be deterministically generated based on the choice context and th ...
Making the best choice when faced with a chain of decisions requires a person to judge both anticipated outcomes and future actions. Although economic decision-making models account for both risk and reward in single-choice contexts, there is a dearth of s ...
Departure time choice modeling has been part of main stream travel behavior research for more than three decades. Congestion management schemes are based on the assumption that travelers optimize their departure time choice. Ever since Vickrey, in the late ...
Successful implementation of policies and measures is key for effective climate legislation. In the political decision process, implementation depends to some extent on interest group influence. This project deals with the questions of why, how and what bu ...
Implementation of policies and measures is an important feature of effective climate policy design. In the political decision process, implementation depends to some extent on interest group influence. This project deals with business interest groups influ ...
This chapter focuses on valuation in the context of choice under uncertainty. When valuation is a pursuit distinct from choice, values revealed through choice may be different from the valuations that emerge from the (choice-independent) computations. In m ...
We have integrated the Graz brain-computer interface (BCI) system with a highly immersive virtual reality (VR) Cave-like system. This setting allows for a new type of experience, whereby participants can control a virtual world using imagery of movement. H ...
Discrete choice models are defined conditional to the knowledge of the actual choice set by the analyst. The common practice for is to assume that individual-based choice sets can be deterministically generated based on the choice context and the character ...
In this paper it is shown how Stochastic Approximation theory can be used to derive and analyse well-known Iterative Learning Control algorithms for linear systems. The Stochastic Approximation theory gives conditions that, when satisfied, ensure almost su ...