FLEET SIZING METHODS USED BY URBAN BUS TRANSPORT COMPANIES IN MANAUS: A THEORETICAL-EMPIRICAL STUDY
Abstract
This study aimed to understand the different fleet-sizing methods used by companies providing urban bus transportation services in Manaus. It used a survey method, with a purposive sample of five individuals responsible for planning and executing fleet sizing in their companies. Data were collected using a semi-structured interview guide, and the results were generated using semantic and content analysis techniques for each guiding question of the research. The results showed that a) the frequency and capacity methods are the most widely used; fleet size sizing is associated with route planning; quantitative and qualitative methods are used simultaneously; and the passenger-per-kilometer index and supply-demand analysis are the central indicators of the methods. b) The methods are employed to reflect the different perspectives on transport management in each company, almost always focused on determining the final fleet and financial and quality aspects. c) The main advantages of the methods are fleet optimization, route definition, and adherence to established schedules. At the same time, the predominant disadvantages are failure to meet service quality and failure to address user problems, and d) the main risks of method failure stem from technical limitations, lack of updating, subjectivity, lack of data, and operational problems. The conclusion shows that fleet sizing in the analyzed system is conducted through a hybrid strategy that combines operational indicators, demand analysis, and user experience assessment, highlighting the need for integration between technical methods and social analyses to improve the efficiency and quality of public transport.
Author Biographies
Undergraduate student in Logistics Technology.
Undergraduate student in Logistics Technology.
Post-doctorate in Management. PhD in Production Engineering. Master in Management. Bachelor in Management.
References
ALBUQUERQUE, V. et al. Sustainability measurement in a logistics transportation company. Transportation Research Procedia, v. 72, p. 48-55, 2023. https://doi.org/10.1016/j.trpro.2023.11.321
AL-NABULSI, D. et al. Analyzing fleet efficiency and passenger delay in demand‐responsive transit: A dual‐model approach with CVRPTW and TAMOS. Journal of Advanced Transportation, v. 2025, n. 1, p. 6761411, 2025. https://doi.org/10.1155/atr/6761411
AUAD-PEREZ, R.; VAN HENTENRYCK, P. Ridesharing and fleet sizing for on-demand multimodal transit systems. Transportation Research Part C: Emerging Technologies, v. 138, p. 103594, 2022. https://doi.org/10.1016/j.trc.2022.103594
BALAC, M.; HÖRL, S.; AXHAUSEN, K. W. Fleet sizing for pooled (automated) vehicle fleets. Transportation Research Record, v. 2674, n. 9, p. 168-176, 2020. https://doi.org/10.1177/0361198120927388
HRUŠOVSKÝ, M. et al. Integrated planning approach for fleet sizing and fleet management of freight railcars. Flexible Services and Manufacturing Journal, v. 37, n. 3, p. 869-903, 2025. https://doi.org/10.1007/s10696-024-09556-8
JEZEQUEL, S.; BOUKHERROUB, T.; BELMOKHTAR-BERRAF, S. Dynamic electric vehicle dispatching problem: A simulation modelling framework. In: 2025 11th International Conference on Control, Decision and Information Technologies (CoDIT). IEEE, 2025. p. 2087-2092. https://doi.org/10.1109/CoDIT66093.2025.11321715
KARMANESH, Y. et al. Two-stage stochastic programming approach for fleet sizing and allocating rail wagons under uncertain demand. Computers & Industrial Engineering, v. 188, p. 109878, 2024. https://doi.org/10.1016/j.cie.2023.109878
KRONMUELLER, M.; FIELBAUM, A.; ALONSO-MORA, J. Fleet sizing for the flash delivery problem from multiple stores: A case study in Amsterdam. In: 2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2024. p. 1159-1165. https://doi.org/10.1109/ITSC58415.2024.10919944
LI, S.; SHEHADEH, K. S.; TSANG, M. Y. Stochastic programming models for a fleet sizing and appointment scheduling problem with random service and travel times. Transportation Research Part C: Emerging Technologies, v. 165, p. 104692, 2024. https://doi.org/10.1016/j.trc.2024.104692
MANCINI, S.; GANSTERER, M. Infrastructure planning, fleet sizing, and scheduling for E-public transport. Flex Serv Manuf J., p. 1-35, 2026. https://doi.org/10.1007/s10696-026-09658-5
MEßMER, M. et al. Data-driven insights into truck utilisation challenges in freight logistics. Research in Transportation Business & Management, v. 60, p. 101373, 2025. https://doi.org/10.1016/j.rtbm.2025.101373
MILENKOVIĆ, M.; BOJOVIĆ, N.; ABRAMIN, D. Railway freight wagon fleet size optimization: A real-world application. Journal of Rail Transport Planning & Management, v. 26, p. 100373, 2023. https://doi.org/10.1016/j.jrtpm.2023.100373
NASCIMENTO-E-SILVA, D. et al. Influence of the external environment on the logistics strategies of industrial organizations. International Journal for Innovation Education and Research, v. 7, n. 12, p. 628-643, 2019. https://doi.org/10.31686/ijier.Vol7.Iss12.2117
NASCIMENTO-E-SILVA, D. Manual do método científico-tecnológico: edição sintética. Florianópolis: DNS Editor, 2020.
NASCIMENTO-E-SILVA, D. Handbook of the scientific-technological method: Synthetic edition. Manaus: DNS Editor, 2021a.
NASCIMENTO-E-SILVA, D. O método científico-tecnológico: fundamentos. Manaus: DNS Editor, 2021b.
NASCIMENTO-E-SILVA, D. O método científico-tecnológico: questões de pesquisa. Manaus: DNS Editor, 2021c.
NASCIMENTO-E-SILVA, D. O método científico-tecnológico: coleta de dados. Manaus: DNS Editor, 2023.
ŞAHİN, M.; YILDIZ, E. A. The heterogeneous fleet drone delivery problem with total weighted lateness considerations: mathematical models and modified farmland fertility algorithm. International Journal of Systems Science: Operations & Logistics, v. 12, n. 1, p. 2566721, 2025. https://doi.org/10.1080/23302674.2025.2566721
SILVA, L. M. R.; WANG, H.; SOARES, C. G. A strategic fleet size and mix vehicle routing model to analyze the impact of demand fluctuation on river-sea liner shipping. Ocean Engineering, v. 307, p. 118096, 2024. https://doi.org/10.1016/j.oceaneng.2024.118096
SON, J.; IM, J.; KIM, D. Urban transit optimization: Efficient electric bus operations and vehicle-to-grid integration. Computers & Industrial Engineering, v. 205, p. 111169, 2025. https://doi.org/10.1016/j.cie.2025.111169
TANG, C.; SHI, H.; LIU, T. Optimization of single-line electric bus scheduling with skip-stop operation. Transportation Research Part D: Transport and Environment, v. 117, p. 103652, 2023. https://doi.org/10.1016/j.trd.2023.103652
TARAN, I. et al. Determining the optimal service area of a logistics center: A quantitative approach. Transport Problems: an International Scientific Journal, v. 20, n. 3, p 1-15, 2025. https://doi.org/10.20858/tp.2025.20.3.01
TRUDEN, C.; HEWITT, M. Multi-objective, multi-attribute fleet sizing in a dynamic and stochastic environment: A data-driven approach. Transportation Research Part E: Logistics and Transportation Review, v. 207, p. 104585, 2026. https://doi.org/10.1016/j.tre.2025.104585
WANG, Shiqi et al. Electrification of a citywide bus network: A data-driven micro-simulation approach. Transportation Research Part D: Transport and Environment, v. 116, p. 103644, 2023. https://doi.org/10.1016/j.trd.2023.103644
WU, Y.; LIU, T.; DU, B. Fleet sizing and static rebalancing strategies for shared E-scooters: A case study in Indianapolis, USA. Transportation Research Part A: Policy and Practice, v. 190, p. 104287, 2024. https://doi.org/10.1016/j.tra.2024.104287
XIE, F.; WANG, C.; DUAN, H. Optimizing fleet size in point-to-point shared demand responsive transportation service: A network decomposition approach. Mathematics, v. 12, n. 19, p. 3048, 2024. https://doi.org/10.3390/math12193048
YATSKIV, I.; TOLUJEVS, J.; PETROVS, V. Framework for the sustainable modeling of electric truck fleet usage. Logistics, v. 8, n. 4, p. 95, 2024. https://doi.org/10.3390/logistics8040095
ZHANG, H. et al. Joint optimization of fleet sizing, charging station planning, and operation for autonomous electric vehicle fleets in urban transportation networks. Sustainable Energy, Grids and Networks, p. 101946, 2025. https://doi.org/10.1016/j.segan.2025.101946
ZHANG, Y. et al. A two-stage optimization framework for UAV fleet sizing and task allocation in emergency logistics using the GWO and CBBA. Drones, v. 9, n. 7, p. 501, 2025. https://doi.org/10.3390/drones9070501
License
Copyright (c) 2026 RECIMA21 - Revista Científica Multidisciplinar - ISSN 2675-6218
This work is licensed under a Creative Commons Attribution 4.0 International License.
Os direitos autorais dos artigos/resenhas/TCCs publicados pertecem à revista RECIMA21, e seguem o padrão Creative Commons (CC BY 4.0), permitindo a cópia ou reprodução, desde que cite a fonte e respeite os direitos dos autores e contenham menção aos mesmos nos créditos. Toda e qualquer obra publicada na revista, seu conteúdo é de responsabilidade dos autores, cabendo a RECIMA21 apenas ser o veículo de divulgação, seguindo os padrões nacionais e internacionais de publicação.