Factors Influencing Long-Haul Freight Truck Driver Behavior: A Random Forest Analysis in South Sulawesi
DOI:
https://doi.org/10.32832/astonjadro.v15i3.21235Keywords:
long-haul freight transport, truck driver behavior, random forest, speeding violations and data balancing.Abstract
This study examines factors influencing speeding violations among long-haul freight truck drivers in South Sulawesi using the Random Forest (RF) algorithm. Data were collected from 370 drivers at the UPPKB Datae weigh station in Sidrap Regency, covering variables such as delivery pressure, sleep duration, truck age and size, monthly income, driving experience, daily driving hours, and driver age. Two modeling scenarios were tested: without balancing and with data balancing to address class imbalance. The unbalanced model achieved the highest performance (accuracy = 0.9929; F1-score = 0.9752; AUROC = 0.982), while the balanced model improved minority class detection despite a lower AUROC (0.711). Feature importance analysis revealed sleep duration, truck size, delivery pressure, and driver age as dominant predictors. Biological factors, vehicle characteristics, and operational pressures significantly affected speeding behavior. Policy implications include enforcing limits on working hours, ensuring minimum rest periods, revising incentive structures, providing regular safety training, and employing monitoring technologies such as e-logbooks and GPS. Future research should explore alternative algorithms, advanced balancing techniques, and integration of real-time operational and behavioral data to enhance predictive accuracy.
References
A. Budiharjo, T. Andika, N. Fitriani, R. Rukman, and B. Turasno, “Operational Data Analytics of Over Dimensional and Overloaded Truck in Indonesia,” RSF Conf. Ser. Eng. Technol., vol. 2, no. 2, pp. 88–98, 2022, doi: 10.31098/cset.v2i2.562.
C. Archetti, L. Peirano, and M. G. Speranza, “Optimization in multimodal freight transportation problems: A Survey,” Eur. J. Oper. Res., vol. 299, no. 1, pp. 1–20, 2022, doi: 10.1016/j.ejor.2021.07.031.
D. Meyer et al., “Modelling the relationship of driver license and offense history with fatal and serious injury (FSI) crash involvement,” J. Safety Res., vol. 79, pp. 83–93, 2021, doi: https://doi.org/10.1016/j.jsr.2021.08.008.
B. Zhou et al., “Comparing Factors Affecting Injury Severity of Passenger Car and Truck Drivers,” IEEE Access, vol. 8, pp. 153849–153861, 2020, doi: 10.1109/ACCESS.2020.3018183.
A. Williamson, D. A. Lombardi, S. Folkard, J. Stutts, T. K. Courtney, and J. L. Connor, “The link between fatigue and safety,” Accid. Anal. Prev., vol. 43, no. 2, pp. 498–515, 2011, doi: 10.1016/j.aap.2009.11.011.
M. Shattell, Y. Apostolopoulos, S. Sönmez, and M. Griffin, “Occupational Stressors and the Mental Health of Truckers,” Issues Ment. Health Nurs., vol. 31, pp. 561–568, Sep. 2010, doi: 10.3109/01612840.2010.488783.
G. J. Casey, T. Miles-Johnson, and G. J. Stevens, “Heavy vehicle driver fatigue: Observing work and rest behaviours of truck drivers in Australia,” Transp. Res. Part F Traffic Psychol. Behav., vol. 104, no. November 2023, pp. 136–153, 2024, doi: 10.1016/j.trf.2024.05.016.
B. Shandhana Rashmi and S. Marisamynathan, “Factors affecting truck driver behavior on a road safety context: A critical systematic review of the evidence,” J. Traffic Transp. Eng. (English Ed., vol. 10, no. 5, pp. 835–865, 2023, doi: 10.1016/j.jtte.2023.04.006.
H. Hakzah, S. Yusuf, and A. I. Pawelloi, “The Importance of Motorcycle Taxi Transport of Agricultural Products and Operator Income in Indonesia,” Open Transp. J., vol. 16, no. 1, pp. 1–8, 2022, doi: 10.2174/18744478-v16-e2201071.
T. Inkinen and E. Hämäläinen, “Reviewing truck logistics: Solutions for achieving low emission road freight transport,” Sustain., vol. 12, no. 17, pp. 1–11, 2020, doi: 10.3390/SU12176714.
M. K. Lemke, Y. Apostolopoulos, and S. Sönmez, “A novel COVID-19 based truck driver syndemic? Implications for public health, safety, and vital supply chains,” Am. J. Ind. Med., vol. 63, no. 8, pp. 659–662, 2020, doi: 10.1002/ajim.23138.
A. M. Crizzle, M. McLean, and J. Malkin, “Risk factors for depressive symptoms in long-haul truck drivers,” Int. J. Environ. Res. Public Health, vol. 17, no. 11, 2020, doi: 10.3390/ijerph17113764.
S. S. Nadler and J. F. Kros, “Truck driver turnover: a logistic regression approach,” in Contemporary approaches and strategies for applied logistics, IGI Global Scientific Publishing, 2018, pp. 382–404.
A. P. Afghari et al., “Investigating the effects of sleepiness in truck drivers on their headway: An instrumental variable model with grouped random parameters and heterogeneity in their means,” Anal. Methods Accid. Res., vol. 36, p. 100241, 2022, doi: 10.1016/j.amar.2022.100241.
B. S. Rashmi and S. Marisamynathan, “Investigating the contributory factors influencing speeding behavior among long-haul truck drivers traveling across India: Insights from binary logit and machine learning techniques,” Int. J. Transp. Sci. Technol., vol. 16, pp. 194–211, 2024.
Z. Jin, J. Shang, Q. Zhu, C. Ling, W. Xie, and B. Qiang, “RFRSF: Employee Turnover Prediction Based on Random Forests and Survival Analysis,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 12343 LNCS, pp. 503–515, 2020, doi: 10.1007/978-3-030-62008-0_35.
O. O. Ajayi, A. M. Kurien, K. Djouani, and L. Dieng, “Analysis of Road Roughness and Driver Comfort in ‘Long-Haul’Road Transportation Using Random Forest Approach,” Sensors (Basel)., vol. 24, no. 18, p. 6115, 2024.
M. Imani, A. Beikmohammadi, and H. R. Arabnia, “Comprehensive Analysis of Random Forest and XGBoost Performance with SMOTE, ADASYN, and GNUS Under Varying Imbalance Levels,” Technologies, vol. 13, no. 3, pp. 1–40, 2025, doi: 10.3390/technologies13030088.
I. K. UMAR, “Machine learning approach for predicting truck drivers’ involvement in an injury accident,” Sigma, vol. 43, no. 3, pp. 808–815, 2025.
H. Hakzah, M. I. Ramli, and A. I. Pawelloi, “the Characteristics Operational Cost of Freight Transport in South Sulawesi Province - Indonesia,” Sinergi, vol. 25, no. 1, p. 81, 2020, doi: 10.22441/sinergi.2021.1.011.
A. M. Crizzle, R. Toxopeus, and J. Malkin, “Impact of limited rest areas on truck driver crashes in Saskatchewan: A mixed-methods approach,” BMC Public Health, vol. 20, no. 1, pp. 1–10, 2020, doi: 10.1186/s12889-020-09120-7.
B. Shandhana Rashmi and S. Marisamynathan, “Investigating the contributory factors influencing speeding behavior among long-haul truck drivers traveling across India: Insights from binary logit and machine learning techniques,” Int. J. Transp. Sci. Technol., no. xxxx, pp. 1–18, 2024, doi: 10.1016/j.ijtst.2024.01.008.
O. O. Ajayi, A. M. Kurien, K. Djouani, and L. Dieng, “Analysis of Road Roughness and Driver Comfort in ‘Long-Haul’ Road Transportation Using Random Forest Approach,” Sensors, vol. 24, no. 18, 2024, doi: 10.3390/s24186115.
M. Altalhan, A. Algarni, and M. Turki-Hadj Alouane, “Imbalanced Data Problem in Machine Learning: A Review,” IEEE Access, vol. 13, pp. 13686–13699, 2025, doi: 10.1109/ACCESS.2025.3531662.
J. F. Hair, B. J. Babin, R. E. Anderson, and W. C. Black, Multivariate Data Analysis. Cengage Learning, 2022. [Online]. Available: https://books.google.co.id/books?id=PONXEAAAQBAJ
T. Fawcett, “An introduction to ROC analysis,” Pattern Recognit. Lett., vol. 27, no. 8, pp. 861–874, 2006, doi: https://doi.org/10.1016/j.patrec.2005.10.010.
T. Hasanin, T. M. Khoshgoftaar, J. L. Leevy, and R. A. Bauder, “Severely imbalanced Big Data challenges: investigating data sampling approaches,” J. Big Data, vol. 6, no. 1, 2019, doi: 10.1186/s40537-019-0274-4.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 ASTONJADRO
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Paper submitted to ASTONJADRO is the sole property of the Astonjadro Journal. Unless the author withdraws the paper because he does not want to be published in this journal. The publication rights are in the journal Astonjadro.ASTONJADRO
LICENSE
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Based on a work at http://ejournal.uika-bogor.ac.id/index.php/ASTONJADRO
