Safe and Sound: Driver Safety-Aware Vehicle Re-Routing Based on Spatiotemporal Information

Detalhes bibliográficos
Autor(a) principal: Souza, Allan M. de
Data de Publicação: 2020
Outros Autores: Braun, Torsten, Botega, Leonardo C. [UNESP], Villas, Leandro A., Loureiro, Antonio A. F.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1109/TITS.2019.2958624
http://hdl.handle.net/11449/195643
Resumo: Vehicular traffic re-routing is key to provide better vehicular mobility. However, considering just traffic-related information to recommend better routes for each vehicle is far from achieving the desired requirements of a good Traffic Management System, which intends to improve not only mobility but also driving experience and safety of drivers and passengers. Context-aware and multi-objective re-routing approaches will play an important role in traffic management. However, most of these approaches are deterministic and can not support the strict requirements of traffic management applications, since many vehicles potentially will take the same route, and, thus, degrade the overall traffic efficiency. In this work, we introduce Safe and Sound (SNS), a non-deterministic multi-objective re-routing approach for improving traffic efficiency and reduce public safety risks (based on criminal events) for drivers and passengers. SNS employs a hybrid architecture and a cooperative re-routing approach for improving system scalability and computation efforts. SNS uses a recurrent neural network to both predict future safety risks dynamics and enable a personalized re-routing in which each vehicle decides the risks it wants to avoid. Simulation results revealed that when compared to state-of-the-art approaches, SNS reduces the CPU time of the re-routing algorithm in approximately 99% and decreases the average safety risk for drivers and passengers in at least 30% while keeping efficient traffic mobility.
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spelling Safe and Sound: Driver Safety-Aware Vehicle Re-Routing Based on Spatiotemporal InformationSafetyVehiclesSpatiotemporal phenomenaPrediction algorithmsVehicle dynamicsHeuristic algorithmsRoadsVehicle routingvehicle safetyadvanced driver assistanceVehicular traffic re-routing is key to provide better vehicular mobility. However, considering just traffic-related information to recommend better routes for each vehicle is far from achieving the desired requirements of a good Traffic Management System, which intends to improve not only mobility but also driving experience and safety of drivers and passengers. Context-aware and multi-objective re-routing approaches will play an important role in traffic management. However, most of these approaches are deterministic and can not support the strict requirements of traffic management applications, since many vehicles potentially will take the same route, and, thus, degrade the overall traffic efficiency. In this work, we introduce Safe and Sound (SNS), a non-deterministic multi-objective re-routing approach for improving traffic efficiency and reduce public safety risks (based on criminal events) for drivers and passengers. SNS employs a hybrid architecture and a cooperative re-routing approach for improving system scalability and computation efforts. SNS uses a recurrent neural network to both predict future safety risks dynamics and enable a personalized re-routing in which each vehicle decides the risks it wants to avoid. Simulation results revealed that when compared to state-of-the-art approaches, SNS reduces the CPU time of the re-routing algorithm in approximately 99% and decreases the average safety risk for drivers and passengers in at least 30% while keeping efficient traffic mobility.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Univ Estadual Campinas, Inst Comp, BR-13083970 Campinas, SP, BrazilUniv Bern, Inst Comp Sci & Appl Math, CH-3012 Bern, SwitzerlandState Univ Sao Paulo, Informat Sci Dept, BR-01049010 Sao Paulo, BrazilUniv Fed Minas Gerais, Dept Comp Sci, BR-31270901 Belo Horizonte, MG, BrazilState Univ Sao Paulo, Informat Sci Dept, BR-01049010 Sao Paulo, BrazilFAPESP: 2018/19639-5FAPESP: 2019/24937-8Ieee-inst Electrical Electronics Engineers IncUniversidade Estadual de Campinas (UNICAMP)Univ BernUniversidade Estadual Paulista (Unesp)Universidade Federal de Minas Gerais (UFMG)Souza, Allan M. deBraun, TorstenBotega, Leonardo C. [UNESP]Villas, Leandro A.Loureiro, Antonio A. F.2020-12-10T17:58:54Z2020-12-10T17:58:54Z2020-09-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article3973-3989http://dx.doi.org/10.1109/TITS.2019.2958624Ieee Transactions On Intelligent Transportation Systems. Piscataway: Ieee-inst Electrical Electronics Engineers Inc, v. 21, n. 9, p. 3973-3989, 2020.1524-9050http://hdl.handle.net/11449/19564310.1109/TITS.2019.2958624WOS:000564291100033Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengIeee Transactions On Intelligent Transportation Systemsinfo:eu-repo/semantics/openAccess2021-10-23T10:18:13Zoai:repositorio.unesp.br:11449/195643Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T16:30:08.529016Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Safe and Sound: Driver Safety-Aware Vehicle Re-Routing Based on Spatiotemporal Information
title Safe and Sound: Driver Safety-Aware Vehicle Re-Routing Based on Spatiotemporal Information
spellingShingle Safe and Sound: Driver Safety-Aware Vehicle Re-Routing Based on Spatiotemporal Information
Souza, Allan M. de
Safety
Vehicles
Spatiotemporal phenomena
Prediction algorithms
Vehicle dynamics
Heuristic algorithms
Roads
Vehicle routing
vehicle safety
advanced driver assistance
title_short Safe and Sound: Driver Safety-Aware Vehicle Re-Routing Based on Spatiotemporal Information
title_full Safe and Sound: Driver Safety-Aware Vehicle Re-Routing Based on Spatiotemporal Information
title_fullStr Safe and Sound: Driver Safety-Aware Vehicle Re-Routing Based on Spatiotemporal Information
title_full_unstemmed Safe and Sound: Driver Safety-Aware Vehicle Re-Routing Based on Spatiotemporal Information
title_sort Safe and Sound: Driver Safety-Aware Vehicle Re-Routing Based on Spatiotemporal Information
author Souza, Allan M. de
author_facet Souza, Allan M. de
Braun, Torsten
Botega, Leonardo C. [UNESP]
Villas, Leandro A.
Loureiro, Antonio A. F.
author_role author
author2 Braun, Torsten
Botega, Leonardo C. [UNESP]
Villas, Leandro A.
Loureiro, Antonio A. F.
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Universidade Estadual de Campinas (UNICAMP)
Univ Bern
Universidade Estadual Paulista (Unesp)
Universidade Federal de Minas Gerais (UFMG)
dc.contributor.author.fl_str_mv Souza, Allan M. de
Braun, Torsten
Botega, Leonardo C. [UNESP]
Villas, Leandro A.
Loureiro, Antonio A. F.
dc.subject.por.fl_str_mv Safety
Vehicles
Spatiotemporal phenomena
Prediction algorithms
Vehicle dynamics
Heuristic algorithms
Roads
Vehicle routing
vehicle safety
advanced driver assistance
topic Safety
Vehicles
Spatiotemporal phenomena
Prediction algorithms
Vehicle dynamics
Heuristic algorithms
Roads
Vehicle routing
vehicle safety
advanced driver assistance
description Vehicular traffic re-routing is key to provide better vehicular mobility. However, considering just traffic-related information to recommend better routes for each vehicle is far from achieving the desired requirements of a good Traffic Management System, which intends to improve not only mobility but also driving experience and safety of drivers and passengers. Context-aware and multi-objective re-routing approaches will play an important role in traffic management. However, most of these approaches are deterministic and can not support the strict requirements of traffic management applications, since many vehicles potentially will take the same route, and, thus, degrade the overall traffic efficiency. In this work, we introduce Safe and Sound (SNS), a non-deterministic multi-objective re-routing approach for improving traffic efficiency and reduce public safety risks (based on criminal events) for drivers and passengers. SNS employs a hybrid architecture and a cooperative re-routing approach for improving system scalability and computation efforts. SNS uses a recurrent neural network to both predict future safety risks dynamics and enable a personalized re-routing in which each vehicle decides the risks it wants to avoid. Simulation results revealed that when compared to state-of-the-art approaches, SNS reduces the CPU time of the re-routing algorithm in approximately 99% and decreases the average safety risk for drivers and passengers in at least 30% while keeping efficient traffic mobility.
publishDate 2020
dc.date.none.fl_str_mv 2020-12-10T17:58:54Z
2020-12-10T17:58:54Z
2020-09-01
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://dx.doi.org/10.1109/TITS.2019.2958624
Ieee Transactions On Intelligent Transportation Systems. Piscataway: Ieee-inst Electrical Electronics Engineers Inc, v. 21, n. 9, p. 3973-3989, 2020.
1524-9050
http://hdl.handle.net/11449/195643
10.1109/TITS.2019.2958624
WOS:000564291100033
url http://dx.doi.org/10.1109/TITS.2019.2958624
http://hdl.handle.net/11449/195643
identifier_str_mv Ieee Transactions On Intelligent Transportation Systems. Piscataway: Ieee-inst Electrical Electronics Engineers Inc, v. 21, n. 9, p. 3973-3989, 2020.
1524-9050
10.1109/TITS.2019.2958624
WOS:000564291100033
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Ieee Transactions On Intelligent Transportation Systems
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 3973-3989
dc.publisher.none.fl_str_mv Ieee-inst Electrical Electronics Engineers Inc
publisher.none.fl_str_mv Ieee-inst Electrical Electronics Engineers Inc
dc.source.none.fl_str_mv Web of Science
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
repository.mail.fl_str_mv
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