Kondisi Lalu Lintas, Jarak Tempuh, Kelelahan Berkendara, dan Motivasi Akademik Mahasiswa
DOI:
https://doi.org/10.37481/jmh.v6i3.2121Keywords:
Commuting Distance, Driving Fatigue, Academic Motivation, Traffic Conditions, University StudentsAbstract
Traffic congestion and long commuting distances have become common challenges for university students who travel daily to campus. These transportation-related conditions may increase driving fatigue and reduce students' psychological readiness to engage in academic activities. However, empirical evidence explaining the mechanism through which commuting conditions influence academic motivation remains limited. This study aimed to examine the effects of traffic conditions and commuting distance on driving fatigue and to investigate its implications for students' academic motivation. A quantitative correlational design was employed involving 150 university students in the Bandung metropolitan area who regularly commuted to campus using private vehicles. Participants were selected through purposive sampling based on commuting frequency and travel distance criteria. Data were collected using Likert-scale questionnaires and analyzed through descriptive statistics, multiple regression, and path analysis. The findings revealed that both traffic conditions (β = 0.335, p < 0.001) and commuting distance (β = 0.416, p < 0.001) had significant positive effects on driving fatigue, jointly explaining 43.8% of its variance. Furthermore, driving fatigue negatively affected academic motivation (β = −0.495, p < 0.001) with an explained variance of 33.6%. These findings indicate that daily commuting experiences play an important role in shaping students' physical readiness and academic engagement. Therefore, transportation improvements and more flexible academic policies should be considered to better support commuter students.
References
Akhter, M. S., Kapukotuwa, S., Dai, C. L., Awan, A., Odejimi, O. A., & Sharma, M. (2024). Theory-based determinants of stopping drowsy driving behavior in college students: A cross-sectional study. International Journal of Environmental Research and Public Health, 21(9), 1157.
Danna, G. C., Randall, J. G., & Mahabir, B. K. (2023). Commute-based learning: Integrating literature across transportation, education, and industrial-organizational psychology. Human Resource Development Review, 22(2), 143-168.
Darma, Y., Saleh, S., Fisaini, J., Sugiarto, S., Saida, P. R., & Mauladea, F. (2026). Assessing how physical and mental fatigue affect driving speed on the Banda Aceh-Medan Highway, Indonesia. Traffic Safety Research, 8, e000131.
Dawson, D., Searle, A. K., & Paterson, J. L. (2021). Look before you (s)leep: Evaluating the use of fatigue detection technologies within a fatigue risk management system for the road transport industry. Sleep Medicine Reviews, 58, 101438.
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2022). Multivariate data analysis (9th ed.). Cengage Learning.
Hayes, A. F. (2022). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach (3rd ed.). The Guilford Press.
He, J., Li, Z., Ma, Y., Sun, L., & Ma, K. H. (2023). Physiological and behavioral changes of passive fatigue on drivers during on-road driving. Applied Sciences, 13(2), 1200.
Herlambang, M. B., Cnossen, F., & Taatgen, N. A. (2021). The effects of intrinsic motivation on mental fatigue. PLOS ONE, 16(1), e0243754.
Hobfoll, S. E., Halbesleben, J., Neveu, J. P., & Westman, M. (2021). Conservation of resources in the organizational context: The reality of resources and their consequences. Annual Review of Organizational Psychology and Organizational Behavior, 8, 103-128.
Hopson, L. M., Lidbe, A., Jackson, M. S., & Adanu, E. (2022). Transportation to school and academic outcomes: A systematic review. Educational Review, 76(2), 1-21.
Kline, R. B. (2023). Principles and practice of structural equation modeling (5th ed.). The Guilford Press.
Masduki, Y., & Johari, M. E. (2025). Digital learning fatigue and academic motivation in students at Ahmad Dahlan University Yogyakarta. Jurnal Tarbiyatuna, 16(2), 219-234.
Matsumura, T., Hatoyama, K., Kimura, D., Sano, K., Sultanova, F., & Barabanshchikova, V. (2021). Effects of secondary tasks on drivers’ mental fatigue, perceived time and attention level under traffic congestion. Journal of Traffic and Transportation Engineering, 7(4), A1-A7.
Memon, M. A., Ting, H., Cheah, J. H., Thurasamy, R., Chuah, F., & Cham, T. H. (2020). Sample size for survey research: Review and recommendations. Journal of Applied Structural Equation Modeling, 4(2), 1-20.
Neubauer, C. E., Matthews, G., & De Los Santos, E. P. (2023). Fatigue and secondary media impacts in the automated vehicle: A multidimensional state perspective. Safety, 9(1), 11.
Richardson, M., Abraham, C., & Bond, R. (2021). Psychological correlates of university students' academic performance: A systematic review and meta-analysis. Psychological Bulletin, 147(2), 115-158.
Ryan, R. M., & Deci, E. L. (2020). Intrinsic and extrinsic motivation from a self-determination theory perspective: Definitions, theory, practices, and future directions. Contemporary Educational Psychology, 61, 101860.
Salmela-Aro, K., & Upadyaya, K. (2020). School engagement and school burnout profiles during high school: The role of socio-emotional skills. European Journal of Developmental Psychology, 17(6), 943-964.
Taber, K. S. (2019). The use of Cronbach's alpha when developing and reporting research instruments in science education. Research in Science Education, 48(6), 1273-1296.
van der Meer, J., Jansen, E., & Torenbeek, M. (2020). It's almost a mindset that teachers need to change: First-year students' need to be inducted into time management. Studies in Higher Education, 45(9), 1883-1899.
Wu, D. (2024). Improving automatic detection of driver fatigue and distraction using machine learning. Sensors.
Zhang, C., Lu, X., & Huang, Z. (2020). A driver fatigue recognition algorithm based on spatio-temporal feature sequence. IEEE Access.
