An analysis of factors influencing public transportation use by Mulawarman university students in Samarinda City using the binary logistic regression

https://doi.org/10.55214/25768484.v9i5.7600

Authors

  • Gultom Tiopan HM Faculty of Engineering, Mulawarman University, Indonesia.
  • Jamal Mardewi Faculty of Engineering, Mulawarman University, Indonesia.
  • Fatah M Khairul Faculty of Engineering, Mulawarman University, Indonesia.

The students of Mulawarman University represent a significant potential group of public transportation users in Samarinda. Various factors influence their decision to use public transportation, such as comfort, cost, travel time, and personal preferences. To increase the use of public transportation among the students, it is crucial to understand these factors thoroughly. This research aims to analyze the factors affecting public transport usage among Mulawarman University students using the binary logistic regression method. The SPSS (Statistical Program for Social Sciences) software was employed for statistical analysis. A survey was conducted for this research applying the random sampling approach in which students were the sole respondents of the questionnaires. The analysis used binary logistic regression, including multicollinearity detection, parameter estimation via Maximum Likelihood Estimation (MLE), and significance testing. The results show that the distance from student residence to campus, public transportation cost, accessibility, private vehicle ownership, and travel time significantly affect public transport usage. However, public transportation cost, accessibility, and private vehicle ownership do not significantly influence the usage when considered partially. Residential distance has a negative and significant effect, while travel time has a positive and significant effect on public transportation usage by the students.

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How to Cite

HM, G. T. ., Mardewi, J. ., & Khairul, F. M. . (2025). An analysis of factors influencing public transportation use by Mulawarman university students in Samarinda City using the binary logistic regression. Edelweiss Applied Science and Technology, 9(5), 2830–2835. https://doi.org/10.55214/25768484.v9i5.7600

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Published

2025-05-28