Ảnh bìa

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

This 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...

Mô tả đầy đủ

Đã lưu trong:
Tác giả chính: Gamarra, Walter (author)
Tác giả khác: Santacruz Bogado, Maira (author), Cikel, Kevin (author), Martínez, Elvia (author)
Định dạng: article
Ngôn ngữ:Tiếng Anh
Được phát hành: 2021
Những chủ đề:
4. Transporte, telecomunicaciones y otras infraestructuras
TRAFFIC SIMULATION
DEEP LEARNING
GENETIC ALGORITHMS
Truy cập trực tuyến:http://hdl.handle.net/20.500.14066/3588
Các nhãn: Thêm thẻ
Không có thẻ, Là người đầu tiên thẻ bản ghi này!
_version_ 1870612070706184192
author Gamarra, Walter
author2 Santacruz Bogado, Maira
Cikel, Kevin
Martínez, Elvia
author2_role author
author
author
author_browse Cikel, Kevin
Gamarra, Walter
Martínez, Elvia
Santacruz Bogado, Maira
author_facet Gamarra, Walter
Santacruz Bogado, Maira
Cikel, Kevin
Martínez, Elvia
author_role author
bitstream.checksum.fl_str_mv 4e79b4d36f2ef0bae5a8f9999b1bf2f6
858b22fda432bd774e469302988c1974
152f5fb5c096bad6f74d86aa69736b32
bitstream.checksumAlgorithm.fl_str_mv MD5
MD5
MD5
bitstream.url.fl_str_mv http://repositorio.conacyt.gov.py/bitstream/20.500.14066/3588/1/PINV15-66art.pdf
http://repositorio.conacyt.gov.py/bitstream/20.500.14066/3588/2/license.txt
http://repositorio.conacyt.gov.py/bitstream/20.500.14066/3588/3/PINV15-66art.pdf.txt
dc.contributor.other.es.fl_str_mv Universidad Nacional de Asunción - Facultad de Ingeniería
dc.creator.none.fl_str_mv Gamarra, Walter
Santacruz Bogado, Maira
Cikel, Kevin
Martínez, Elvia
dc.date.accessioned.none.fl_str_mv 2022-04-25T16:02:58Z
dc.date.available.none.fl_str_mv 2022-04-25T16:02:58Z
dc.date.issued.none.fl_str_mv 2021
dc.identifier.uri.none.fl_str_mv http://hdl.handle.net/20.500.14066/3588
http://hdl.handle.net/20.500.14066/3588
dc.language.iso.es.fl_str_mv eng
dc.relation.projectCONACYT.es.fl_str_mv PINV15-66
dc.rights.accessRights.es.fl_str_mv info:eu-repo/semantics/openAccess
dc.subject.classification.es.fl_str_mv 4. Transporte, telecomunicaciones y otras infraestructuras
dc.subject.other.es.fl_str_mv TRAFFIC SIMULATION
DEEP LEARNING
GENETIC ALGORITHMS
dc.title.es.fl_str_mv Deep Learning for Traffic Prediction with an Application to Traffic Lights Optimization.
dc.type.es.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
description This 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.
eu_rights_str_mv openAccess
format article
id CONACYT_a77e14c64ae0b44a0a1bf959fc6cafc0
language eng
network_acronym_str CONACYT
network_name_str Repositorio Institucional CONACYT
oai_identifier_str oai:repositorio.conacyt.gov.py:20.500.14066/3588
publishDate 2021
publishDateSort 2021
repository.mail.fl_str_mv repositorio.institucional@conacyt.gov.py
repository.name.fl_str_mv Repositorio Institucional CONACYT
repository_id_str
spelling d2249601-0b1b-4951-9334-b670a1b73bc66001676000000-0002-5197-33189bf06c4b-2c0f-4565-bfa2-ec85d81e2943600cb266dd2-ac31-4088-bab7-c4d698bf1920600Universidad Nacional de Asunción - Facultad de Ingeniería2022-04-25T16:02:58Z2022-04-25T16:02:58Z2021http://hdl.handle.net/20.500.14066/3588http://hdl.handle.net/20.500.14066/3588This 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.Consejo Nacional de Ciencia y TecnologíaPROCIENCIAeng4. Transporte, telecomunicaciones y otras infraestructurasTRAFFIC SIMULATIONDEEP LEARNINGGENETIC ALGORITHMSDeep Learning for Traffic Prediction with an Application to Traffic Lights Optimization.info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionPINV15-66info:eu-repo/semantics/openAccessGamarra, WalterSantacruz Bogado, MairaCikel, KevinMartínez, ElviaORIGINALPINV15-66art.pdfPINV15-66art.pdfPINV15-66artapplication/pdf2469721http://repositorio.conacyt.gov.py/bitstream/20.500.14066/3588/1/PINV15-66art.pdf4e79b4d36f2ef0bae5a8f9999b1bf2f6MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-81698http://repositorio.conacyt.gov.py/bitstream/20.500.14066/3588/2/license.txt858b22fda432bd774e469302988c1974MD52TEXTPINV15-66art.pdf.txtPINV15-66art.pdf.txtExtracted texttext/plain26314http://repositorio.conacyt.gov.py/bitstream/20.500.14066/3588/3/PINV15-66art.pdf.txt152f5fb5c096bad6f74d86aa69736b32MD5320.500.14066/3588oai:repositorio.conacyt.gov.py:20.500.14066/35882026-02-12 19:30:29.043Repositorio Institucional CONACYTrepositorio.institucional@conacyt.gov.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
spellingShingle Deep Learning for Traffic Prediction with an Application to Traffic Lights Optimization.
Gamarra, Walter
4. Transporte, telecomunicaciones y otras infraestructuras
TRAFFIC SIMULATION
DEEP LEARNING
GENETIC ALGORITHMS
status_str publishedVersion
title Deep Learning for Traffic Prediction with an Application to Traffic Lights Optimization.
title_full Deep Learning for Traffic Prediction with an Application to Traffic Lights Optimization.
title_fullStr Deep Learning for Traffic Prediction with an Application to Traffic Lights Optimization.
title_full_unstemmed Deep Learning for Traffic Prediction with an Application to Traffic Lights Optimization.
title_short Deep Learning for Traffic Prediction with an Application to Traffic Lights Optimization.
title_sort Deep Learning for Traffic Prediction with an Application to Traffic Lights Optimization.
topic 4. Transporte, telecomunicaciones y otras infraestructuras
TRAFFIC SIMULATION
DEEP LEARNING
GENETIC ALGORITHMS
url http://hdl.handle.net/20.500.14066/3588

Những quyển sách tương tự