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All Journal Jurnal Teknik Jurnal HPJI (Himpunan Pengembangan Jalan Indonesia) International Journal of Marine Engineering Innovation and Research Jurnal Teknik Sipil Terapan Jurnal Sosial dan Teknologi International Journal of Islamic Education, Research and Multiculturalism (IJIERM) Edunity: Kajian Ilmu Sosial dan Pendidikan Eduvest - Journal of Universal Studies Journal of Social Science EDUCATIONE: Journal of Education Research and Review Jurnal Pengabdian Ilmu Kesehatan
Lucia Ingrid Regina Lefrandt
Universitas Sam Ratulangi
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Modeling Transportation Mode Choice Using the Multinomial Logit (MNL) Model in the City of Kotamobagu Rendy Lodewiyk Suwu; Lucia Ingrid Regina Lefrandt; Joice Waani
Edunity Kajian Ilmu Sosial dan Pendidikan Vol. 5 No. 7 (2026): Edunity: Social and Educational Studies
Publisher : PT Publikasiku Academic Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57096/edunity.v5i7.545

Abstract

This research aims to analyze the factors affecting transportation mode choice in Kotamobagu City, measure the influence of travelers’ socioeconomic characteristics, and determine the probability of choosing among motorcycles, private cars, and bentor (motorized three-wheeled vehicles). The research employed a quantitative descriptive method using a survey approach conducted across four sub-districts. Primary data were collected through questionnaires administered to 153 respondents and analyzed using the Multinomial Logit Model (MNL) in SPSS. The results of the simultaneous tests showed that all independent variables examined (cost, safety, travel time, and convenience considerations) significantly contributed to transportation mode choice decisions. However, based on partial tests, three main factors were found to be the most dominant and statistically significant, namely gender, travel destination, and the reasons for choosing a mode, particularly cost and travel time considerations. Male travelers and those traveling for work-related purposes demonstrated a higher tendency to choose motorcycles. Meanwhile, segments such as housewives, students, and civil servants showed a stronger preference for bentor compared with motorcycles. The final results of the modeling indicate that mobility preferences in Kotamobagu City are highly dominated by private vehicles, with a combined market share of 94%. Motorcycles and private cars are the two most dominant modes, with choice probabilities of 47.64% and 46.36%, respectively. In contrast, bentor occupies the lowest position, with a choice probability of only 6.00%. These findings indicate that bentor remains a secondary transportation option, primarily serving specific passenger segments or particular travel needs in Kotamobagu City.
OPTIMIZATION MODEL FOR URBAN PUBLIC TRANSPORT OPERATIONS USING MACHINE LEARNING-BASED DEMAND PREDICTION Joshua Banua; Lucia Ingrid Regina Lefrandt; Semuel Yacob Recky Rompis
EDUCATIONE Volume 4, Issue 2, July 2026
Publisher : CV. TOTUS TUUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59397/edu.v4i2.303

Abstract

Angkot, a route-based urban paratransit or public minibus service, remains an important component of daily mobility in Manado City. However, its predominantly supply-based operating pattern does not systematically adjust vehicle deployment to hourly passenger demand, which can produce low occupancy during off-peak periods and insufficient capacity during demand peaks. This study integrates machine-learning-based hourly passenger-demand prediction with an integer-constrained fleet-sizing model for the Paal Dua-Pasar 45 route. Direct observations were collected for 21 days, from 25 May to 14 June 2026, through an on-board survey of one angkot and a separate vehicle-headway survey; 1,538 passenger boardings were directly recorded. Random Forest and XGBoost were compared using a chronological 80:20 holdout and Leave-One-Day-Out Cross-Validation (LODO). Random Forest remained the better-performing model after tuning (LODO R² = 0.462; MAE = 0.76) compared with XGBoost (R² = 0.421; MAE = 0.83). The selected predictions were transformed from vehicle-level boardings to estimated corridor demand using observed hourly vehicle frequency and were then entered into the fleet-sizing model. The model-derived corridor demand averaged 57 passengers per hour, and the resulting fleet requirement averaged six vehicles, ranging from five to nine vehicles. Because corridor demand was inferred from one directly surveyed vehicle and predictive performance was moderate, these fleet values should be interpreted as route-specific planning estimates rather than externally validated operating prescriptions. The study demonstrates a practical framework for linking demand prediction with adaptive fleet allocation while highlighting the need for multi-vehicle validation and uncertainty-aware deployment.
Co-Authors Adinda Laloma Amelia Andrita Alika Rondo Audie Lexie Egbert Rumayar Diana Vanda D. Doda Dumbela, Haslinda Eva Mariane Mantjoto Gisela Ondang Jehosua S. V. Sinolungan Joice Elfrida Waani, Joice Elfrida Joice Waani Joshua Banua Kumaat, Meike Lexie Egbert Rumayar, Audie Meike Kumaat Oktovian Berty Alexander Sompie Punuhsingon, Charles S. C. Rendy Lodewiyk Suwu Semuel Y. R. Rompis, Semuel Y. R. Shofian Edy Harianto Bongso Wulan Pingkan Julia Kaunang