Johannes Kristinsson

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Prediction of the traffic information such as flow, density, speed, and travel time is important for traffic control systems, optimizing vehicle operations, and the individual driver. Prediction of future traffic information is a challenging problem due to many dynamic contributing factors. In this paper, various methodologies for traffic information(More)
This paper deals with the problem of on-board learning of typical stop locations and the prediction of the vehicle destination. Such a learning and prediction procedure is used to summarize the stop locations, estimate the frequent destinations, and learn the driver's decision model of selecting the next destinations under different conditions. The(More)
Accurate prediction of the traffic information in real time such as flow, density, speed, and travel time has important applications in many areas, including intelligent traffic control systems, optimizing vehicle operations, and the routing selection for individual drivers on the road. This is also a challenging problem due to dynamic changes of traffic(More)
Prediction of the traffic information such as flow, density, speed, and travel time is important for traffic control systems, optimizing vehicle operations, and the individual driver. Prediction of future traffic information is a challenging problem due to many dynamic contributing factors. In this paper, macroscopic and kinetic traffic modeling approaches(More)
This paper presents a study in using quality attribute scenarios to evaluate and improve the software architecture of a control system that plans and affects energy use on a plug-in hybrid electric vehicle. The study confirmed for us the value of the quality attribute workshop approach in an industrial, automotive controls setting. This experience has(More)