Learning traffic congestion by contextual bandit problems for optimum localization
2017 25th Signal Processing and Communications Applications Conference, SIU 2017
Institute of Electrical and Electronics Engineers Inc.
Item Usage Stats
MetadataShow full item record
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. © 2017 IEEE.
KeywordsBest location detection
Contextual bandit problems
Emergency medical systems
Command and control systems
Real time systems
Emergency Medical system
Multi armed bandit
Published Version (Please cite this version)http://dx.doi.org/10.1109/SIU.2017.7960447
Showing items related by title, author, creator and subject.
Abidi, K.; Yildiz, Y. (IFAC Secretariat, 2015)In this paper, we present the discrete version of the Adaptive Posicast Controller (APC) that deals with parametric uncertainties in systems with input time-delays. The continuous-time APC is based on the Smith Predictor ...
Morgül, Ö. (Institute of Electrical and Electronics Engineers Inc., 2003)We will consider model based anticontrol of chaotic systems. We consider both continuous and discrete time cases. We first assume that the systems to be controlled are linear and time invariant. Under controllability ...
Babaei, N.; Salamci, M. U.; Karakurt, A. H. (American Society of Mechanical Engineers, 2017)The paper presents an approach to the Model Reference Adaptive Control (MRAC) design for nonlinear dynamical systems. A nonlinear reference system is considered such that its response is designed to be stable via Successive ...