"Robot cars" are cars that allow for automated driving. They can drive closer together than human driven "normal cars" and thereby raise road capacity. Obtaining a robot car instead of a normal car can also be expected to lower the userś value of time losses (VOT), because travel time can be...

This paper investigates second-best congestion pricing in a monocentric city characterized by distortionary, rigid regulatory mechanisms in the housing market (building height restrictions, zoning and property taxation). The Pigouvian toll is shown to retain its optimality under any setting with...

We study the performance of alternative methods for calculating in-sample confidence and out of-sample forecast bands for time-varying parameters. The in-sample bands reflect parameter uncertainty only. The out-of-sample bands reflect both parameter uncertainty and innovation uncertainty. The...

We introduce a dynamic Skellam model that measures stochastic volatility from high-frequency tick-by-tick discrete stock price changes. The likelihood function for our model is analytically intractable and requires Monte Carlo integration methods for its numerical evaluation. The proposed...

Hypercongestion is the situation where a certain traffic flow occurs at a combination of low speed and high density, and a more favorable combination of these could produce the same flow. The macroscopic fundamental diagram (MFD) allows for such hypercongestion, but does not explicitly describe...

We propose a basic high-dimensional dynamic model for tennis match results with time varying player-specific abilities for different court surface types. Our statistical model can be treated in a likelihood-based analysis and is capable of handling high-dimensional datasets while the number of...

We introduce a mixed-frequency score-driven dynamic model for multiple time series where the score contributions from high-frequency variables are transformed by means of a mixed-data sampling weighting scheme. The resulting dynamic model delivers a flexible and easy-to-implement framework for...

We consider unobserved components time series models where the components are stochastically evolving over time and are subject to stochastic volatility. It enables the disentanglement of dynamic structures in both the mean and the variance of the observed time series. We develop a simulated...

When drivers opt for carpooling, road capacity will be freed up, and this will reduce congestion. Therefore, carpooling is interesting for policy makers as a possible solution to congestion. We investigate the effects of carpooling in a dynamic equilibrium model of congestion, which captures...