Decision tree analysis of commuter mode choice in Baguio City, Philippines

Transportation is a multidisciplinary system. Solving its issues would require the knowledge of social, economic, engineering, environmental, and technological disciplines. Emerging techniques used in problem-solving involve the use of machine learning techniques. In this study, a machine learning technique, decision tree, is used for mode choice analysis in Baguio City, Philippines. Using data from a household survey, the developed model uncovers the most significant factors affecting mode choice of residents in the city. The results highlight the role of income, which is related to the individuals’ career level and stage in life. Interestingly, a mid-level income group seems to be highly inclined towards private vehicle use. To conclude, the authors note that the primary advantage of a decision tree is its simplicity and straightforward results interpretation, which is paramount in policymaking. For future work, the authors recommend exploring larger decision tree models for mode choice and conducting a validation interview of the insights obtained from the study.