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A publicação pode ser exportada nos seguintes formatos: referência da APA (American Psychological Association), referência do IEEE (Institute of Electrical and Electronics Engineers), BibTeX e RIS.

Exportar Referência (APA) 
Simões, R., Lopes, A. & Brito e Abreu, F. (2026). A genetic algorithm for pedestrian origin-destination flow estimation. In Filipe Araújo, Eugénio Ribeiro (Ed.), INForum 2025: Atas do 16º Simpósio Nacional de Informática (pp. 188?200). https://doi.org/10.5281/zenodo.19640654
Exportar Referência (IEEE) 
R. J. Simões et al.,  "A genetic algorithm for pedestrian origin-destination flow estimation", in INForum 2025: Atas do 16º Simpósio Nacional de Informática , Filipe Araújo, Eugénio Ribeiro, Ed., Setúbal, Portugal, 2026, pp. 188-200
Exportar BibTeX 
@inproceedings{simões2026_1791158074334,
	author = "Simões, R. and Lopes, A. and Brito e Abreu, F.",
	title = "A genetic algorithm for pedestrian origin-destination flow estimation",
	booktitle = "INForum 2025: Atas do 16º Simpósio Nacional de Informática ",
	year = "2026",
	editor = "Filipe Araújo, Eugénio Ribeiro",
	volume = "",
	number = "",
	series = "",
	doi = "10.5281/zenodo.19640654",
	pages = "188-200",
	publisher = "",
	address = "Setúbal, Portugal",
	organization = "",
	url = "https://inforum.org.pt/"
}
Exportar RIS 
TY  - CPAPER
TI  - A genetic algorithm for pedestrian origin-destination flow estimation
T2  - INForum 2025: Atas do 16º Simpósio Nacional de Informática 
AU  - Simões, R.
AU  - Lopes, A.
AU  - Brito e Abreu, F.
PY  - 2026
SP  - 188-200
DO  - 10.5281/zenodo.19640654
CY  - Setúbal, Portugal
UR  - https://inforum.org.pt/
AB  - Urbanenvironments are facing increasing complexity, mainly due to rising populations, vehicle usage, and tourism. This intensifies urban flows, including traffic, daily commuting, and tourist movements, posing challenges for congestion management, environmental sustainability, and overall quality of life. In popular tourist destinations, where urban infrastructure often limits carrying capacity, managing the combined flows of residents and visitors becomes particularly difficult. This can degrade both the visitor experience and the mobility of local communities. To address these challenges, mobility flows can be modeled using trajectory data, typically obtained via GPS. However, such data is costly and often unavailable. In this study, we leverage a more accessible but less granular dataset from the Lisboa Aberta initiative, which provides aggregated mobile device counts across Lisbon by location and time. While this dataset lacks individual identifiers or trajectory information, we aim to reconstruct pedestrian origin-destination (OD) flows from it. We frame this as a dynamic OD estimation problem, which is an inherently under-determined optimization task. To address this, we present a genetic algorithm constrained by a simplified pedestrian mobility model. This approach enables us to infer plausible mobility patterns from aggregated data, offering a cost-effective method for urban flow analysis and contributing to more sustainable urban planning.
ER  -