ARTIFICIAL INTELLIGENCE MODELS IN THE IN THE MANAGEMENT OF BRAZILIAN INTER-MUNICIPAL CONSORTIA
DOI:
https://doi.org/10.25112/rgd.v20i2.3424Keywords:
Intermunicipal Consortia, Artificial Intelligenc, Artificial Intelligence, Public ManagementAbstract
The main objective was proposing the use of artificial intelligence (AI) frameworks in the management of intercity consortia in Brazil, with the implementation of technological tools that allow, in a coordinated and strategic way, the efficient management of sanitation, security, and public transport services. This discussion gains importance for several reasons, among which the improvement in decision-making is one of them, providing guidelines capable of making the decision-making process more informed and accurate. Thus, the concept of inter-municipal consortia and variants are explored as theoretical assumptions, conceiving a qualitative study, with descriptive and propositional characteristics. It was concluded that the use of AI for the management of consortia of intercity services is perceived as a promising and innovative approach, optimizing processes, improving operational efficiency, reducing costs, and increasing the quality of services offered to the population. Further studies involving performance evaluation, economic feasibility studies, assessment of social and environmental impacts, ethical and legal aspects, as well as involving adoption of new technologies in exploring the use of other AI techniques and approaches, such as cloud computing, the internet of things (IoT), and blockchain.
References
ABAD, P.; OSABA, E.; DEL SER, J.; BAHILLO, A.; LÓPEZ-GUEDE, J. M. Estimativa de tempos de viagem em
uma rede de ônibus real a partir de fontes de dados escassas usando abordagem de aprendizado profundo.
International Transactions in Operational Research, v. 24, n. 4, p. 933-959, 2017.
AKCAY, S.; ATAPOUR-ABARGHOUEI, A.; BRECKON, T. P. Levantamento de conjuntos de dados de vídeo
para reconhecimento de ações e atividades humanas. Computer Vision and Image Understanding, v.
, p. 28-48, 2018.
BALESTRIN, A.; VERSCHOORE, J. Redes de Cooperação Empresarial: Estratégias de Gestão na Nova
Economia. Bookman editora, 2016.
BANGINWAR, A. M.; SATONE, M. P. Sistema de Gerenciamento de Água Inteligente Utilizando IoT. International
Research Journal of Engineering and Technology (IRJET), v. 6, n. 6, p. 1503-1506, 2019.
BOLAÑOS, M.; WEBER, R.; DÍAZ-AGUDO, B. Abordagem multi-sensorial para a análise de dados de crimes
urbanos. Sensors, v. 17, n. 3, p. 549, 2017.
CAI, H.; LO, H. K.; WU, D. Otimização orientada por dados para operações de trânsito responsivas à demanda.
Transportation Research, v. 100, p. 98-116, 2019.
CALMON, P.; COSTA, A. T. M. Redes e governança das políticas públicas. Revista de Pesquisa em Políticas
Públicas, v. 1, p. 1-29, 2013.
CAMPOS, S.; FIGUEIREDO, J. Aplicação de Inteligência Artificial no Ciclo de Políticas Públicas. Cadernos de
Prospecção, v.15, p.196-214, 2022.
CARDOSO, A. B.; CUNHA, F. M. Uso da Inteligência Artificial na Melhoria da Tomada de Decisões Governamentais.
Revista de Administração Pública, v. 50, n. 3, p. 462-477, 2020.
CARLEY, K. M.; MAO, W.; RAMESH, D.; VENKATANATHAN, J. A Dynamic Model of Social Network Formation
and Crime Propagation. Social Networks, v. 48, p. 263-274, 2017.
CHAN, E. W.; LIU, X.; HOU, Z. Fleet Management in Public Transportation: A Review and A Path Forward.
IEEE Transactions on Intelligent Transportation Systems, v. 20, n. 11, p. 4089-4107, 2018.
CHANDOLA, V.; BANERJEE, A.; KUMAR, V. Anomaly detection: A survey. ACM Computing Surveys
(CSUR), v. 41, n. 3, p. 1-58, 2009.
CHEAH, L.; KU-MAHAMUD, K. R.; FONG, S. Data mining approach for public transportation analysis. IEEE
Access, v. 6, p. 3664-3674, 2018.
CHEN, J.; MINSKER, B. S.; RAJAGOPALAN, B. Real-time reservoir inflow forecasting with data assimilation
using machine learning. Journal of Water Resources Planning and Management, v. 146, n. 9, p.
, 2020.
CHEN, L.; GAO, Y.; MA, J.; WANG, S. Demand prediction of subway passenger flow based on wavelet analysis
and ARIMA model. Transportation Research, v. 115, p. 102602, 2020.
CHEN, L.; JI, Y.; ZHANG, X.; QI, D. Anomaly detection of subway operational data using long short-term
memory networks. Transportation Research, v. 100, p. 91-106, 2019.
COSTA, F. R.; SANTOS, G. A.; OLIVEIRA, T. S. Desafios de Integração de Soluções de IA com Sistemas Legados
na Administração Pública. Revista de Tecnologia e Gestão Pública, v. 8, n. 2, p. 145-160, 2019.
COSTA, P.; FRANCO, A.; SILVA, L. Survey and performance evaluation of deep learning algorithms for object
recognition. Neurocomputing, v. 266, p. 42-64, 2017.
DÍAZ-MORALES, A.; GONZÁLEZ-BRIONES, A.; GONZÁLEZ-CANTERGIANI, P. Intelligent public transportation
system based on video surveillance and Internet of Things. Journal of Ambient Intelligence and
Humanized Computing, v. 11, n. 8, p. 3485-3500, 2020.
DUARTE, P. A. Potencialidades e Desafios de Consórcios intermunicipais de resíduos sólidos. Boletim
regional, urbano e ambiental, n. 29, jan.-jun., 2023
ENDLICH, A. M. Cooperações intermunicipais em áreas não metropolitanas. Revista do Desenvolvimento
Regional, v. 23, n. 3, p. 95-116, 2018.
FJELD, J.; NELE A.; HANNAH, H.; NAGY, A.; SRIKUMAR, M. Principled Artificial Intelligence: Mapping
Consensus in Ethical and Rights-based Approaches to Principles for AI. Berkman Klein Center for Internet
& Society, 2020.
FLEXA, R. G. C.; BARBASTEFANO, R. G. Consórcios públicos de saúde: uma revisão da literatura. Ciência &
Saúde Coletiva, v. 25, p. 325-338, 2019.
FREITAS, B. R.; OLIVEIRA, A. R de. Avaliação dos consórcios intermunicipais de saúde da zona da mata
mineira: uma análise sob a ótica dos gestores de saúde. HOLOS, v. 3, p. 338-353, 2015.
GENG, Y.; JIAO, W.; ZHAO, Y.; ZHAO, J. Vehicle license plate recognition using SSD object detection. Computers
& Electrical Engineering, v. 74, p. 123-133, 2019.
GERBER, M. S.; JOHNSON, M. P.; LIU, L. Predicting crime using Twitter and kernel density estimation. Decision
Support Systems, v. 61, p. 115-125, 2014.
GHIMIRE, J. A.; GUPTA, S. C. Water Loss Management in Urban Water Distribution Systems: A Review.
Water Resources Management, v. 33, n. 7, p. 2433-2451, 2019.
GIFANI, P.; DEHGHANTANHA, A.; CONTI, M.; DARGAHI, T. DeepIoT-Guard: A Novel Intrusion Detection
Framework for Internet of Things Using Deep Neural Networks. IEEE Internet of Things Journal, v. 8, n.
, p. 5700-5708, 2020.
GIL, A. B.; DUARTE, F. Data-driven modeling and analytics for urban water systems. Environmental
Modelling & Software, v. 88, p. 1-4, 2017.
GRIN, E. J. et al. Together it is possible to go further: Brazilian health inter-municipal consortium as a
collaborative and innovative governance to fight COVID-19. International Research Society for Public
Management, 2021.
GRIN, E. J.; SEGATO, C. I. Consórcios intermunicipais ou arranjos de desenvolvimento da educação? Uma
análise de duas experiências no federalismo educacional brasileiro, Revista do Serviço Público (RSP), v.
, n. 1, p. 101-132, 2021
GUO, X.; ZHAO, S.; YANG, C.; ZHANG, Y. Crime recognition model based on improved deep learning in public
security video surveillance. IEEE Access, v. 8, p. 186469-186479, 2020.
HABIB, M. A.; PATHAN, A. S. K.; MIAN, A. S. A deep learning framework for abnormal behavior detection
in video surveillance. IEEE Transactions on Industrial Informatics, v. 16, n. 4, p. 2381-2390, 2019.
HAN, J.; KAMBER, M.; PEI, J. Data mining: concepts and techniques. Morgan Kaufmann, 2011.
HAUGELAND, J. Artificial Intelligence: The Very Idea. Massachusetts: The MIT Press, 1985.
HARVARD BUSINESS REVIEW (HBR). Artificial Intelligence. HBR Press, 2021.
HUANG, H. C.; HSU, Y. L.; TSAI, J. S. H. Privacy, Ethics, and Legal Issues in AI and Machine Learning for Video
Surveillance: A Comprehensive Survey. IEEE Transactions on Intelligent Transportation Systems, v.
, n. 4, p. 2445-2462, 2021.
HUANG, H. J.; CHEN, A.; FANG, H.; TZENG, G. H. Data mining in demand forecasting of an innovative public
transportation system: A case study of YouBike in Taipei. Transportation Research, v. 97, p. 258-267,
JAIN, A. K.; ROSS, A.; PRABHAKAR, S. An Introduction to Biometric Recognition. IEEE Transactions on
Circuits and Systems for Video Technology, v. 30, n. 11, p. 3719-3729, 2021.
JIN, J.; SIMONOVIC, S. P.; HE, S. A review of application of artificial intelligence in water resources. Water
Resources Management, v. 31, n. 15, p. 4925-4947, 2017.
JONES, R. M.; BROWN, C. M. Forecasting daily domestic water demand using socio-economic and
meteorological variables. Journal of Water Resources Planning and Management, v. 145, n. 10, p.
, 2019.
KEEN, P. G. W. Decision support systems: a research perspective. 1980.
KOÇ, S. N.; OZKAPTAN, O.; ERDOGAN, S. An adaptive large neighborhood search algorithm for the school
bus routing problem with time windows. Computers & Operations Research, v. 98, p. 124-135, 2018.
KOTUSEV, S.; KURNIA, S.; CATER-STEEL, A. Big data in public transportation: A systematic literature review.
Journal of Urban Technology, v. 26, n. 4, p. 81-105, 2019.
LEÃO, L.; ANDRADE BASTOS, S. Q.; RIBEIRO, H. M. D. Relação entre consórcios públicos e desenvolvimento
municipal: uma análise a partir do tamanho e diversidade das redes intermunicipais em Minas
Gerais. Gestão & Regionalidade, v. 39, n. 116, 2022.
LECUN, Y.; BENGIO, Y.; HINTON, G. Deep learning. Nature, v. 521, n. 7553, p. 436-444, 2015.
LI, D.; WANG, S.; LIU, J.; ZHONG, Q. An improved water demand forecasting model using the hybrid of artificial
intelligence methods. Water Science and Engineering, v. 12, n. 4, p. 329-340, 2019.
LI, M.; ZHANG, J.; LI, M. A review of video-based human activity recognition. Artificial Intelligence Review,
v. 51, n. 4, p. 555-573, 2019.
LIMA, A. S.; PEREIRA, R. F.; COSTA, M. J. Proteção de Dados Pessoais no Contexto da Inteligência Artificial
na Administração Pública. Revista Brasileira de Direito e Tecnologia, v. 14, n. 1, p. 76-92, 2021.
LIU, X.; CAO, X.; ZHANG, C.; YU, S. Video surveillance for intelligent and urban transportation systems: A
review. IEEE Transactions on Intelligent Transportation Systems, v. 20, n. 6, p. 2111-2129, 2018.
LONG, H.; FRENCH, D. P.; BROOKS, J. M. Optimising the value of the critical appraisal skills programme
(CASP) tool for quality appraisal in qualitative evidence synthesis. Research Methods in Medicine & Health
Sciences, v. 1, n. 1, p. 31–42, 2020.
LUDERMIR, T. B. Inteligência Artificial e Aprendizado de Máquina: estado atual e tendências. Estudos
Avançados, v. 35, p. 85-94, 2021.
LUI, L.; SCHABBACH, L. M.; NORA, C. R. D. Regionalização da saúde e cooperação federativa no Brasil: o
papel dos consórcios intermunicipais. Ciência & Saúde Coletiva, v.25, p. 5065-5074, 2020.
MA, X.; WANG, W.; SONG, S.; LIU, Z. Public transportation demand prediction based on an attention-
based LSTM model. Transportation Research, v. 129, p. 103288, 2021.
MALIK, U.; ULLAH, S.; KIM, J. H. Predicting crime using the processing of big data. Sensors, v. 19, n. 13, p.
, 2019.
MASMOUDI, M.; BEN HMIDA, F.; ALIMI, A. M. Fleet management system: a predictive maintenance based
on the internet of things and machine learning. Procedia Computer Science, v. 151, p. 34-40, 2019.
MCCARTHY, J. What is artificial intelligence? Stanford: 2007.
MOHAMMADI, A.; JALALI, M. Low-cost sensor network for real-time water quality monitoring. Procedia
Engineering, v. 186, p. 459-464, 2017.
MOHLER, G.; SHORT, M. B.; BRANTINGHAM, P. J.; SCHOENBERG, F. P.; TITA, G. E. Self-exciting point process
modeling of crime. Journal of the American Statistical Association, v. 110, n. 512, p. 100-108,
MOHLER, G.; SHORT, M. B.; BRANTINGHAM, P. J.; SCHOENBERG, F. P.; TITA, G. E. Self-exciting point process
modeling of crime. Journal of the American Statistical Association, v. 106, n. 493, p. 100-108,
MORAIS, S. M. Gestão de consórcios intermunicipais: o caso do consórcio RIDES. 2019. 126f. Dissertação
(Mestrado em Administração Pública) - Programa de Mestrado Profissional em Administração Pública
em Rede Nacional, Universidade Federal do Triângulo Mineiro, Uberaba, 2019.
NAIK, N. A. G. S.; APTE, C. D.; MANJUNATH, A. M. Anomaly Detection in Water Distribution Systems Using
Machine Learning. Procedia Computer Science, v. 171, p. 170-177, 2020.
NASCIMENTO, O. S.; NUNES, A.; DE AVILA, M. L. O Neoinstitucionalismo e os consórcios federativos no
Brasil. Caderno Profissional de Administração da UNIMEP, v. 8, n. 2, p. 128-139, 2018.
NASH, D. J.; WICAKSONO, S.; MAKSIMOVIC, Č.; DJORDJEVIĆ, S. Smart Water Networks: A Literature Review.
Water Resources Management, v. 31, n. 10, p. 3251-3265, 2017.
NUNES, R. S.; SOUZA, M. J. F. Smart public transportation: A clustering approach. Expert Systems with
Applications, v. 98, p. 327-335, 2018.
OLIVEIRA, S. S.; ALVES, M. F. A reforma da gestão das redes estaduais de Goiás e do Rio de Janeiro sob a
égide da Nova Gestão Pública. Revista online de Política e Gestão Educacional, v. 22, n. 1, p. 177-192,
OSTFELD, A.; UBER, J. G.; SALOMONS, E.; SHAMIR, U. Smart water networks: concepts and applications.
Journal of Water Resources Planning and Management, v. 142, n. 2, p. 04015052, 2016.
PEREIRA, A. B.; SOUZA, L. S. Capacitação e Treinamento de Pessoal para a Implementação de Tecnologias
Emergentes na Gestão Pública. Cadernos de Administração Pública, v. 27, n. 4, p. 643-660, 2017.
PESANTEZ, N. S.; NAVIA-VÁZQUEZ, A.; VÁSQUEZ-ARANGO, J. In-Vehicle Monitoring System for Drivers
Based on Internet of Things. IEEE Access, v. 8, p. 92649-92659, 2020.
RAJALAKSHMI, V.; MAHESWARI, R. Smart Water Management Using IoT and Cloud Computing. In:
IEEE International Conference on Smart Technologies and Management for Computing, Communication,
Controls, Energy and Materials (ICSTM). pp. 111-116, 2018.
REN, Y.; WANG, X.; XUE, F. A novel bus dispatching model for reducing the maximum passenger waiting
time. Transportation Research, v. 141, p. 97-121, 2020.
SANTOS, R. M.; ALVES, J. F.; SILVA, M. A. Aplicação de Chatbots no Atendimento ao Cidadão: Um Estudo
de Caso Governamental. Gestão Pública & Sociedade, v. 12, n. 2, p. 265-280, 2019.
SHARMA, A.; JAIN, S.; VERMA, P. Real-time Leak Detection in Water Distribution Networks using Machine
Learning. In: 2019 2nd International Conference on Computing, Communication, and Security (ICCCS).
pp. 1-5, 2019.
SICHMAN, J. S. Inteligência Artificial e sociedade: avanços e riscos. Estudos Avançados, São Paulo, v.35,
n. 101, p. 37-50, 2021.
SIDDIQUI, S. A.; AHMED, E.; MAHMOOD, A. N. Surveillance Video Analysis for Anomaly Detection: A Survey.
IEEE Access, v. 8, p. 42400-42422, 2020.
SILVA, L. M.; OLIVEIRA, C. R. Implicações Éticas do Uso da Inteligência Artificial na Gestão Pública. Ética e
Sociedade, v. 20, n. 2, p. 128-145, 2018.
SMITH, A.; JOHNSON, B.; WILLIAMS, C. Neural network modeling for water demand forecasting. Journal
of Hydroinformatics, v. 20, n. 4, p. 924-939, 2018.
SMITH, B. C. The promise of artificial intelligence: reckoning and judgment. Mit Press, 2019.
SPINELLI, J.; MESQUITA, L. P. Policentrismo e cooperações intermunicipais: um estudo em regiões de baixa
densidade demográfica no norte do Rio Grande do Sul/Brasil. Revista do Desenvolvimento Regional,
v.25, n. 3, p. 989-1008, 2020.
SUN, H.; JIAO, L.; ZOU, X.; CAI, X. Urban public transport development: A critical review. Journal of Cleaner
Production, v. 208, p. 725-739, 2019.
TANG, L.; XU, X.; ZHANG, Y. A passenger flow forecasting model for urban rail transit network considering
travel behavior. Transportation Research, v. 126, p. 102874, 2021.
TOZLU, S.; KARASAN, O. E.; GARAIX, T. An adaptive large neighborhood search algorithm for the school
bus routing problem with time windows. Computers & Operations Research, v. 98, p. 124-135, 2019.
TURHAN, A. U.; INCE, H.; TASCIKARAOGLU, A.; DEMIRORS, O. Crowd Behavior Anomaly Detection with
Deep Learning. IEEE Transactions on Circuits and Systems for Video Technology, v. 29, n. 8, p. 2312-
, 2019.
VAZ, J. C. Consórcios intermunicipais. 1997.
WALCZAK, S. Artificial neural networks. In: Advanced methodologies and technologies in artificial intelligence,
computer simulation, and human-computer interaction. IGI global, 2019.
WANG, D.; SHAN, W.; JIN, P. Public transportation cloud service based on big data analysis. Future Generation
Computer Systems, v. 86, p. 1231-1237, 2018.
WANG, R.; SHAN, S.; CHEN, X.; GAO, W. Artificial intelligence for automatic facial expression recognition:
Where are we now?. IEEE Transactions on Affective Computing, v. 11, n. 4, p. 552-565, 2020.
WANG, Y.; JOHNSON, D. Artificial intelligence for optimizing water distribution systems: A review. Environmental
Modelling & Software, v. 139, p. 105089, 2021.
WU, C. H.; WANG, X.; YANG, S. S. Water Quality Anomaly Detection and Evaluation Using Machine Learning
Techniques. Water, v. 11, n. 8, p. 1710, 2019.
WU, Y.; JI, X.; ZHENG, Y. A survey of pedestrian detection and tracking for autonomous driving. IEEE Transactions
on Intelligent Transportation Systems, v. 20, n. 9, p. 3368-3383, 2019.
YANG, X.; LI, W.; ZHI, L. An intelligent optimization algorithm for public transport routes and timetables.
Mathematical Problems in Engineering, v. 2017, p. 1-12, 2017.
YAO, Z.; WANG, L.; ZHOU, Y. Water quality prediction based on improved long short-term memory neural
network. Environmental Monitoring and Assessment, v. 192, n. 7, p. 451, 2020.
ZHANG, Y.; JIANG, C.; GENG, N.; LUAN, Y. On-demand bus dispatching problem with uncertain passengers’
demands. Transportation Research, v. 127, p. 103226, 2021.
ZHANG, Y.; MA, H.; LI, K. An intelligent fault detection approach for vehicle health monitoring based on
deep autoencoder. IEEE Access, v. 7, p. 181268-181279, 2019.
ZHENG, Y.; MA, Y.; WOLFSON, O.; XU, K. Moving towards efficient public transportation: an overview of
challenges and opportunities. Transportation Research, v. 114, p. 1-21, 2020.
ZHOU, Z. Machine learning. Springer Nature, 2021.
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