Evolutionary multicriteria planning of bus stops location in smart cities

Autores
López de Haro, Santiago; Nesmachnow, Sergio; Rossit, Diego Gabriel
Año de publicación
2026
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
In modern smart cities, public transportation systems are the foundation of mobility services. Specifically, public bus services are crucial for improving accessibility and the quality of life for many residents, as they provide an affordable and flexible means of travel. A primary consideration in bus network planning involves strategically determining the optimal placement of bus stops throughout the service area. Conventional bus network planning prioritized system operator cost-efficiency as the primary objective, relegating user quality of service to a subordinate goal. Conversely, recent modeling efforts have shifted focus toward user-centric objectives to facilitate the principles of the Transit Oriented Development paradigm. This article introduces a solution to the multicriteria Bus Stop Location Problem, to find the best placement for bus stops to effectively balance user accessibility, service efficiency, and operational costs. To solve this complex trade-off, a multi-objective evolutionary algorithm is developed. The algorithm simultaneously optimizes three key, often conflicting, objectives: i) maximize demand coverage, to ensure the service reaches the most potential users, ii) minimize travel times, by reducing the total number of stops, and iii) minimize operational costs, measured and weighted based on passenger demand. The research applies the resolution approach to real-world case studies using actual public transport mobility data from Montevideo, Uruguay, and Buenos Aires, Argentina. The experimental results confirm that the proposed multi-objective evolutionary algorithm is effective, reliably generating high-quality Pareto fronts that capture the significant trade-offs among the objectives: demand coverage, travel time, and operational cost. Compared with a local search heuristic and a single-objective evolutionary algorithm, superior convergence and diversity were obtained.
Fil: López de Haro, Santiago. Universidad de la República; Uruguay
Fil: Nesmachnow, Sergio. Universidad de la República; Uruguay
Fil: Rossit, Diego Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería; Argentina
Materia
PUBLIC TRANSPORTATION
BUS STOPS LOCATION
MULTICRITERIA OPTIMIZATION
EVOLUTIONARY ALGORITHMS
SMART CITIES
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/287920
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Evolutionary multicriteria planning of bus stops location in smart citiesLópez de Haro, SantiagoNesmachnow, SergioRossit, Diego GabrielPUBLIC TRANSPORTATIONBUS STOPS LOCATIONMULTICRITERIA OPTIMIZATIONEVOLUTIONARY ALGORITHMSSMART CITIEShttps://purl.org/becyt/ford/2.11https://purl.org/becyt/ford/2In modern smart cities, public transportation systems are the foundation of mobility services. Specifically, public bus services are crucial for improving accessibility and the quality of life for many residents, as they provide an affordable and flexible means of travel. A primary consideration in bus network planning involves strategically determining the optimal placement of bus stops throughout the service area. Conventional bus network planning prioritized system operator cost-efficiency as the primary objective, relegating user quality of service to a subordinate goal. Conversely, recent modeling efforts have shifted focus toward user-centric objectives to facilitate the principles of the Transit Oriented Development paradigm. This article introduces a solution to the multicriteria Bus Stop Location Problem, to find the best placement for bus stops to effectively balance user accessibility, service efficiency, and operational costs. To solve this complex trade-off, a multi-objective evolutionary algorithm is developed. The algorithm simultaneously optimizes three key, often conflicting, objectives: i) maximize demand coverage, to ensure the service reaches the most potential users, ii) minimize travel times, by reducing the total number of stops, and iii) minimize operational costs, measured and weighted based on passenger demand. The research applies the resolution approach to real-world case studies using actual public transport mobility data from Montevideo, Uruguay, and Buenos Aires, Argentina. The experimental results confirm that the proposed multi-objective evolutionary algorithm is effective, reliably generating high-quality Pareto fronts that capture the significant trade-offs among the objectives: demand coverage, travel time, and operational cost. Compared with a local search heuristic and a single-objective evolutionary algorithm, superior convergence and diversity were obtained.Fil: López de Haro, Santiago. Universidad de la República; UruguayFil: Nesmachnow, Sergio. Universidad de la República; UruguayFil: Rossit, Diego Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería; ArgentinaFrontiers2026-04-14info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/287920López de Haro, Santiago; Nesmachnow, Sergio; Rossit, Diego Gabriel; Evolutionary multicriteria planning of bus stops location in smart cities; Frontiers ; Frontiers in Future Transportation; 7; 14-4-2026; 1-202673-5210CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.frontiersin.org/journals/future-transportation/articles/10.3389/ffutr.2026.1800459/fullinfo:eu-repo/semantics/altIdentifier/doi/10.3389/ffutr.2026.1800459info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2026-08-25T14:33:21Zoai:ri.conicet.gov.ar:11336/287920instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982026-08-25 14:33:22.157CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Evolutionary multicriteria planning of bus stops location in smart cities
title Evolutionary multicriteria planning of bus stops location in smart cities
spellingShingle Evolutionary multicriteria planning of bus stops location in smart cities
López de Haro, Santiago
PUBLIC TRANSPORTATION
BUS STOPS LOCATION
MULTICRITERIA OPTIMIZATION
EVOLUTIONARY ALGORITHMS
SMART CITIES
title_short Evolutionary multicriteria planning of bus stops location in smart cities
title_full Evolutionary multicriteria planning of bus stops location in smart cities
title_fullStr Evolutionary multicriteria planning of bus stops location in smart cities
title_full_unstemmed Evolutionary multicriteria planning of bus stops location in smart cities
title_sort Evolutionary multicriteria planning of bus stops location in smart cities
dc.creator.none.fl_str_mv López de Haro, Santiago
Nesmachnow, Sergio
Rossit, Diego Gabriel
author López de Haro, Santiago
author_facet López de Haro, Santiago
Nesmachnow, Sergio
Rossit, Diego Gabriel
author_role author
author2 Nesmachnow, Sergio
Rossit, Diego Gabriel
author2_role author
author
dc.subject.none.fl_str_mv PUBLIC TRANSPORTATION
BUS STOPS LOCATION
MULTICRITERIA OPTIMIZATION
EVOLUTIONARY ALGORITHMS
SMART CITIES
topic PUBLIC TRANSPORTATION
BUS STOPS LOCATION
MULTICRITERIA OPTIMIZATION
EVOLUTIONARY ALGORITHMS
SMART CITIES
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.11
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv In modern smart cities, public transportation systems are the foundation of mobility services. Specifically, public bus services are crucial for improving accessibility and the quality of life for many residents, as they provide an affordable and flexible means of travel. A primary consideration in bus network planning involves strategically determining the optimal placement of bus stops throughout the service area. Conventional bus network planning prioritized system operator cost-efficiency as the primary objective, relegating user quality of service to a subordinate goal. Conversely, recent modeling efforts have shifted focus toward user-centric objectives to facilitate the principles of the Transit Oriented Development paradigm. This article introduces a solution to the multicriteria Bus Stop Location Problem, to find the best placement for bus stops to effectively balance user accessibility, service efficiency, and operational costs. To solve this complex trade-off, a multi-objective evolutionary algorithm is developed. The algorithm simultaneously optimizes three key, often conflicting, objectives: i) maximize demand coverage, to ensure the service reaches the most potential users, ii) minimize travel times, by reducing the total number of stops, and iii) minimize operational costs, measured and weighted based on passenger demand. The research applies the resolution approach to real-world case studies using actual public transport mobility data from Montevideo, Uruguay, and Buenos Aires, Argentina. The experimental results confirm that the proposed multi-objective evolutionary algorithm is effective, reliably generating high-quality Pareto fronts that capture the significant trade-offs among the objectives: demand coverage, travel time, and operational cost. Compared with a local search heuristic and a single-objective evolutionary algorithm, superior convergence and diversity were obtained.
Fil: López de Haro, Santiago. Universidad de la República; Uruguay
Fil: Nesmachnow, Sergio. Universidad de la República; Uruguay
Fil: Rossit, Diego Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería; Argentina
description In modern smart cities, public transportation systems are the foundation of mobility services. Specifically, public bus services are crucial for improving accessibility and the quality of life for many residents, as they provide an affordable and flexible means of travel. A primary consideration in bus network planning involves strategically determining the optimal placement of bus stops throughout the service area. Conventional bus network planning prioritized system operator cost-efficiency as the primary objective, relegating user quality of service to a subordinate goal. Conversely, recent modeling efforts have shifted focus toward user-centric objectives to facilitate the principles of the Transit Oriented Development paradigm. This article introduces a solution to the multicriteria Bus Stop Location Problem, to find the best placement for bus stops to effectively balance user accessibility, service efficiency, and operational costs. To solve this complex trade-off, a multi-objective evolutionary algorithm is developed. The algorithm simultaneously optimizes three key, often conflicting, objectives: i) maximize demand coverage, to ensure the service reaches the most potential users, ii) minimize travel times, by reducing the total number of stops, and iii) minimize operational costs, measured and weighted based on passenger demand. The research applies the resolution approach to real-world case studies using actual public transport mobility data from Montevideo, Uruguay, and Buenos Aires, Argentina. The experimental results confirm that the proposed multi-objective evolutionary algorithm is effective, reliably generating high-quality Pareto fronts that capture the significant trade-offs among the objectives: demand coverage, travel time, and operational cost. Compared with a local search heuristic and a single-objective evolutionary algorithm, superior convergence and diversity were obtained.
publishDate 2026
dc.date.none.fl_str_mv 2026-04-14
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/287920
López de Haro, Santiago; Nesmachnow, Sergio; Rossit, Diego Gabriel; Evolutionary multicriteria planning of bus stops location in smart cities; Frontiers ; Frontiers in Future Transportation; 7; 14-4-2026; 1-20
2673-5210
CONICET Digital
CONICET
url http://hdl.handle.net/11336/287920
identifier_str_mv López de Haro, Santiago; Nesmachnow, Sergio; Rossit, Diego Gabriel; Evolutionary multicriteria planning of bus stops location in smart cities; Frontiers ; Frontiers in Future Transportation; 7; 14-4-2026; 1-20
2673-5210
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.frontiersin.org/journals/future-transportation/articles/10.3389/ffutr.2026.1800459/full
info:eu-repo/semantics/altIdentifier/doi/10.3389/ffutr.2026.1800459
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Frontiers
publisher.none.fl_str_mv Frontiers
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.265058

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