LAPSE:2023.32497
Published Article
LAPSE:2023.32497
Smart Cities: Data-Driven Solutions to Understand Disruptive Problems in Transportation—The Lisbon Case Study
April 20, 2023
Abstract
Transportation data in a smart city environment is increasingly becoming available. This data availability allows building smart solutions that are viewed as meaningful by both city residents and city management authorities. Our research work was based on Lisbon mobility data available through the local municipality, where we integrated and cleaned different data sources and applied a CRISP-DM approach using Python. We focused on mobility problems and interdependence and cascading-effect solutions for the city of Lisbon. We developed data-driven approaches using artificial intelligence and visualization methods to understand traffic and accident problems, providing a big picture to competent authorities and supporting the city in being more prepared, adaptable, and responsive, and better able to recover from such events.
Transportation data in a smart city environment is increasingly becoming available. This data availability allows building smart solutions that are viewed as meaningful by both city residents and city management authorities. Our research work was based on Lisbon mobility data available through the local municipality, where we integrated and cleaned different data sources and applied a CRISP-DM approach using Python. We focused on mobility problems and interdependence and cascading-effect solutions for the city of Lisbon. We developed data-driven approaches using artificial intelligence and visualization methods to understand traffic and accident problems, providing a big picture to competent authorities and supporting the city in being more prepared, adaptable, and responsive, and better able to recover from such events.
Record ID
Keywords
accidents, data visualization, data-driven, smart cities, traffic, transportation
Suggested Citation
Albuquerque V, Oliveira A, Barbosa JL, Rodrigues RS, Andrade F, Dias MS, Ferreira JC. Smart Cities: Data-Driven Solutions to Understand Disruptive Problems in Transportation—The Lisbon Case Study. (2023). LAPSE:2023.32497
Author Affiliations
Albuquerque V: NOVA Information Management School (NOVA IMS), Campus de Campolide, Universidade Nova de Lisboa, 1070-312 Lisbon, Portugal [ORCID]
Oliveira A: ISTAR-IUL, Instituto Universitário de Lisboa (ISCTE-IUL), 1649-026 Lisbon, Portugal
Barbosa JL: ISTAR-IUL, Instituto Universitário de Lisboa (ISCTE-IUL), 1649-026 Lisbon, Portugal
Rodrigues RS: ISTAR-IUL, Instituto Universitário de Lisboa (ISCTE-IUL), 1649-026 Lisbon, Portugal
Andrade F: ISTAR-IUL, Instituto Universitário de Lisboa (ISCTE-IUL), 1649-026 Lisbon, Portugal
Dias MS: NOVA Information Management School (NOVA IMS), Campus de Campolide, Universidade Nova de Lisboa, 1070-312 Lisbon, Portugal; ISTAR-IUL, Instituto Universitário de Lisboa (ISCTE-IUL), 1649-026 Lisbon, Portugal [ORCID]
Ferreira JC: ISTAR-IUL, Instituto Universitário de Lisboa (ISCTE-IUL), 1649-026 Lisbon, Portugal; Inov Inesc Inovação—Instituto de Novas Tecnologias, 1000-029 Lisbon, Portugal [ORCID]
Oliveira A: ISTAR-IUL, Instituto Universitário de Lisboa (ISCTE-IUL), 1649-026 Lisbon, Portugal
Barbosa JL: ISTAR-IUL, Instituto Universitário de Lisboa (ISCTE-IUL), 1649-026 Lisbon, Portugal
Rodrigues RS: ISTAR-IUL, Instituto Universitário de Lisboa (ISCTE-IUL), 1649-026 Lisbon, Portugal
Andrade F: ISTAR-IUL, Instituto Universitário de Lisboa (ISCTE-IUL), 1649-026 Lisbon, Portugal
Dias MS: NOVA Information Management School (NOVA IMS), Campus de Campolide, Universidade Nova de Lisboa, 1070-312 Lisbon, Portugal; ISTAR-IUL, Instituto Universitário de Lisboa (ISCTE-IUL), 1649-026 Lisbon, Portugal [ORCID]
Ferreira JC: ISTAR-IUL, Instituto Universitário de Lisboa (ISCTE-IUL), 1649-026 Lisbon, Portugal; Inov Inesc Inovação—Instituto de Novas Tecnologias, 1000-029 Lisbon, Portugal [ORCID]
Journal Name
Energies
Volume
14
Issue
11
First Page
3044
Year
2021
Publication Date
2021-05-24
ISSN
1996-1073
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Original Submission
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PII: en14113044, Publication Type: Journal Article
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LAPSE:2023.32497
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https://doi.org/10.3390/en14113044
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Apr 20, 2023
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