Learning traffic congestion by contextual bandit problems for optimum localization
Date
2017Source Title
Proceedings of the IEEE 25th Signal Processing and Communications Applications Conference, SIU 2017
Publisher
IEEE
Language
Turkish
Type
Conference PaperItem Usage Stats
220
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147
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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 detectionContextual bandit problems
Emergency medical systems
Command and control systems
Stochastic systems
Traffic signals
Location detection
Multi armed bandit
Mutual informations
Stochastic distribution
Traffic congestion