On Intention-Propagation-Based Prediction in Autonomously Self-adapting Navigation
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It is widely believed that road traffic as a whole self-adapts to the current situation to make travel times shorter by avoiding congestions, if the autonomously operating navigation devices exploit real-time traffic information. The classical theoretical models do not have definite answer if car navigation based on real-time data is able to self-adapt and produce better traffic, or not. The novel theoretical approach to study this belief is the online routing game model. Current commercial car navigation systems are modelled with the class of simple naive online routing games. It is already proved that simple naive online routing games may show undesirable phenomena. One of the approaches to improve car navigation is intention-propagation-based prediction, where agents share their intention, and can forecast future travel times. In this paper we prove that in spite of exploiting this type of prediction in online routing games, the phenomena studied in simple naive online routing games are still possible, although in a different way. With these theoretical results we point out hitherto hidden pivotal phenomena of intention-propagation-based prediction in collective adaptive systems.
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