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      • Department of Electrical and Electronics Engineering
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      Learning traffic congestion by contextual bandit problems for optimum localization

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      Author(s)
      Şahin, Ümitcan
      Yücesoy, V.
      Koç, A.
      Tekin, Cem
      Date
      2017
      Source Title
      Proceedings of the IEEE 25th Signal Processing and Communications Applications Conference, SIU 2017
      Publisher
      IEEE
      Language
      Turkish
      Type
      Conference Paper
      Item Usage Stats
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      Abstract
      Optimum localization problem, which has a wide range of application areas in real life such as emergency services, command and control systems, warehouse localization, shipment planning, aims to find the best location to minimize the arrival, response or return time which might be vital in some applications. In most of the cases, uncertainty in traffic is the most challenging issue and in the literature generally it is assumed to obey a priori known stochastic distribution. In this study, problem is defined as the optimum localization of ambulances for emergency services and traffic is modeled to be Markovian to generate context data. Unlike the solution methods in the literature, there exists no mutual information transfer between the model and solution of the problem; thus, a contextual multi-armed bandit learner tries to determine the underlying traffic with simple assumptions. The performance of the bandit algorithm is compared with the performance of a classical estimation method in order to show the effectiveness of the learning approach on the solution of the optimum localization problem.
      Keywords
      Best location detection
      Contextual bandit problems
      Emergency medical systems
      Command and control systems
      Stochastic systems
      Traffic signals
      Location detection
      Multi armed bandit
      Mutual informations
      Stochastic distribution
      Traffic congestion
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      http://hdl.handle.net/11693/37592
      Published Version (Please cite this version)
      http://dx.doi.org/10.1109/SIU.2017.7960447
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      • Department of Electrical and Electronics Engineering 4011
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