Deep Learning for Traffic Prediction with an Application to Traffic Lights Optimization.

dc.contributorUniversidad Nacional de Asunción - Facultad de Ingeniería
dc.creatorGamarra, Walter
dc.creatorBogado, Maira Santacruz
dc.creatorCikel, Kevin
dc.creatorMartínez, Elvia
dc.date2022-04-25T16:02:58Z
dc.date2022-04-25T16:02:58Z
dc.date2021
dc.date.accessioned2023-09-25T13:31:39Z
dc.date.available2023-09-25T13:31:39Z
dc.identifierhttp://hdl.handle.net/20.500.14066/3588
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/8807535
dc.descriptionThis work proposes the use of deep neural networks for the prediction of traffic variables for measuring traffic congestion. Deep neural networks are used in this work in order to determine how much time each vehicle spends in traffic, considering a certain amount of vehicles in the traffic network and traffic light configurations. A genetic algorithm is also implemented that finds an optimal traffic light configuration. With the implementation of a deep neural network for the simulation of traffic instead of using a simulation software, the computation time of the fitness function in the genetic algorithm improved considerably, with a decrease of precision of less than 10%. Genetic algorithms are used in order to show how useful deep neural networks models can be when dealing with vehicular flow slowdown.
dc.descriptionCONACYT - Consejo Nacional de Ciencia y Tecnología
dc.descriptionPROCIENCIA
dc.languageeng
dc.relationPINV15-66
dc.rightsopen access
dc.subject4 Transporte, telecomunicaciones y otras infraestructuras
dc.subjectTRAFFIC SIMULATION
dc.subjectDEEP LEARNING
dc.subjectGENETIC ALGORITHMS
dc.titleDeep Learning for Traffic Prediction with an Application to Traffic Lights Optimization.
dc.typeresearch article


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