Mode Choice Prediction using Machine Learning Technique for A Door-to-Door Journey in Kuantan City

Authors

  • Nur Fahriza Mohd Ali School of Civil Engineering, Engineering Campus, Universiti Sains Malaysia, 14300, Nibong Tebal, Pulau Pinang, Malaysia.
  • Ahmad Farhan Mohd Sadullah School of Civil Engineering, Engineering Campus, Universiti Sains Malaysia, 14300, Nibong Tebal, Pulau Pinang, Malaysia.
  • Anwar P.P. Abdul Majeed Innovative Manufacturing, Mechatronics and Sports Laboratory, Faculty of Manufacturing and Mechatronics Engineering Technology, Universiti Malaysia Pahang (UMP), 26600 Pekan, Pahang Darul Makmur, Malaysia.
  • Mohd Azraai Mohd Razman Innovative Manufacturing, Mechatronics and Sports Laboratory, Faculty of Manufacturing and Mechatronics Engineering Technology, Universiti Malaysia Pahang (UMP), 26600 Pekan, Pahang Darul Makmur, Malaysia.
  • Rabiu Muazu Musa Centre for Fundamental and Continuing Education, Universiti Malaysia Terengganu, Terengganu, Malaysia

DOI:

https://doi.org/10.15282/mekatronika.v2i1.6745

Keywords:

Mode Choice, Door-to-Door Journey, Machine Learning Models, Classification Accuracy

Abstract

A door-to-door journey in a public transportation system is a notable concept that is practically being promoted among users to consider public transport as an important alternative. The door-to-door journey will integrate the travel segments starting from home to destination, including all visible amenities. Users’ preferences on the time travel of these key segments are necessary to be understood. In this case, Machine Learning technique has been seen as a robust computational advancement to forecast their travel mode choice. However, the most convenient model as the best predictor is still questionable. To address this issue, we employed some pre-eminent machine learning models, specifically Random Forest (RF), Naïve Bayes (NB), Logistic Regression (LR), k-Nearest Neighbor (kNN) as well as Support Vector Machine (SVM), to compare their travel mode choice prediction performance of users in the city of Kuantan. The data collection was conducted in Kuantan City via Revealed/Stated Preferences (RPSP) Survey between 8:00 AM to 5:00 PM on weekdays. The data collected was split into a ratio of 80:20 for training and testing before evaluating them between the aforesaid models. The results depicted that the Random Forest could provide satisfactory classification accuracies for both training and testing data up to 68.3% and 61.3%, respectively, compared to the other evaluated machine learning models. In summary, Random Forest provides a good result in the training and testing data and is considered as the best predictor in this research to forecast users’ mode choice in the city of Kuantan.

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Published

2020-06-14

How to Cite

[1]
N. F. . Mohd Ali, A. F. . Mohd Sadullah, A. P.P. Abdul Majeed, M. A. Mohd Razman, and R. Muazu Musa, “Mode Choice Prediction using Machine Learning Technique for A Door-to-Door Journey in Kuantan City”, MEKATRONIKA, vol. 2, no. 1, pp. 73–78, Jun. 2020.

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Original Article

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