{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T19:28:27Z","timestamp":1770751707437,"version":"3.50.0"},"reference-count":43,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T00:00:00Z","timestamp":1769644800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Applied Sciences"],"abstract":"<jats:p>This study explores the application of multiple predictive algorithms under general versus route-specialized modeling strategies to estimate passenger boarding demand in public bus transportation systems. Accurate estimation of boarding patterns is essential for optimizing service planning, improving passenger comfort, and enhancing operational efficiency. This research evaluates a range of predictive models to identify the most effective techniques for forecasting demand across different routes and times. Two modeling strategies were implemented: a generalistic approach and a specialized one. The latter was designed to capture route-specific characteristics and variability. A real-world case study from a medium-sized metropolitan region in Brazil was used to assess model performance. Results indicate that ensemble-tree-based models, particularly XGBoost, achieved the highest accuracy and robustness in handling nonlinear relationships and complex interactions within the data. Compared to the generalistic approach, the specialized approach demonstrated superior adaptability and precision, making it especially suitable for long-term and strategic planning applications. It reduced the average RMSE by 19.46% (from 13.84 to 11.15) and the MAE by 17.36% (from 9.60 to 7.93), while increasing the average R2 from 0.289 to 0.344. However, these gains came with higher computational demands and mean Forecast Bias (from 0.002 to 0.560), indicating a need for bias correction before operational deployment. The findings highlight the practical value of predictive modeling for transit authorities, enabling data-driven decision making in fleet allocation, route planning, and service frequency adjustment. Moreover, accurate demand forecasting contributes to cost reduction, improved passenger satisfaction, and environmental sustainability through optimized operations.<\/jats:p>","DOI":"10.3390\/app16031384","type":"journal-article","created":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T14:52:36Z","timestamp":1769698356000},"page":"1384","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Data-Driven Approach to Estimating Passenger Boarding in Bus Networks"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-6257-4149","authenticated-orcid":false,"given":"Gustavo","family":"Bongiovi","sequence":"first","affiliation":[{"name":"Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, s\/n, 4200-465 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6209-3626","authenticated-orcid":false,"given":"Teresa Galv\u00e3o","family":"Dias","sequence":"additional","affiliation":[{"name":"Instituto de Engenharia de Sistemas e Computadores, Tecnologia e Ci\u00eancia (INESC TEC), Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, s\/n, 4200-465 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4820-4435","authenticated-orcid":false,"given":"Jose Nauri","family":"Junior","sequence":"additional","affiliation":[{"name":"Ag\u00eancia Reguladora de Servi\u00e7os P\u00fablicos Delegados do Estado do Cear\u00e1 (Arce), Av. Gen. Afonso Albuquerque Lima, Cambeba, Fortaleza 60822-325, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9505-5730","authenticated-orcid":false,"given":"Marta","family":"Campos Ferreira","sequence":"additional","affiliation":[{"name":"Instituto de Engenharia de Sistemas e Computadores, Tecnologia e Ci\u00eancia (INESC TEC), Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, s\/n, 4200-465 Porto, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1199","DOI":"10.3390\/smartcities7030051","article-title":"Exploring Sustainable Urban Transportation: Insights from Shared Mobility Services and Their Environmental Impact","volume":"7","author":"Garus","year":"2024","journal-title":"Smart Cities"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Karjalainen, L.E., and Juhola, S. (2019). 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