This paper introduces a new concept of the state model of one traffic microregion based on a maximum utilization of information from all measured traffic variables. The aim of the model is to estimate length of queues that are formed on arms of junctions with traffic lights. This task is trivia in case of complete knowledge of all measured traffic quantities for all junction arms. Then the model only counts simply the queue length from input and output intensities. However, the net of all needed detectors is not usually complete and some significant traffic flows (parking cars, etc.) are not measurable in practice. The model estimates the queue length in this case. In the end of the paper,the model and estimation algorithm is tested for several types of disturbances which can arise in reality. At least partially, these experiments illustrate the functionality and effectiveness of the proposed model for estimating queue lengths on the junction arms in the real traffic.
The paper addresses a problem of sequential bilateral bargaining with incomplete information. We proposed a decision model that helps agents to successfully bargain by performing indirect negotiation and learning the opponent's model. Methodologically the paper casts heuristically-motivated bargaining of a self-interested independent player into a framework of Bayesian learning and Markov decision processes. The special form of the reward implicitly motivates the players to negotiate indirectly, via closed-loop interaction. We illustrate the approach by applying our model to the Nash demand game, which is an abstract model of bargaining. The results indicate that the established negotiation: i) leads to coordinating players' actions; ii) results in maximising success rate of the game and iii) brings more individual profit to the players.INDEX TERMS Learning, Markov decision process, Nash demand game, Negotiation.
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