Nur Fahriza, Mohd Ali and Ahmad Farhan, Mohd Sadullah and Anwar P. P., Abdul Majeed and Mohd Azraai, Mohd Razman and Rabiu Muazu, Musa (2020) Mode choice prediction using machine learning technique for a door-to-door journey in Kuantan City. Mekatronika - Journal of Intelligent Manufacturing & Mechatronics, 2 (1). pp. 73-78. ISSN 2637-0883. (Published)
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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.
| Item Type: | Article |
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| Uncontrolled Keywords: | Mode choice; Door-to-door journey; Machine learning models; Classification accuracy |
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Faculty/Division: | Faculty of Manufacturing and Mechatronic Engineering Technology |
| Depositing User: | Mrs Norsaini Abdul Samat |
| Date Deposited: | 05 Apr 2022 03:05 |
| Last Modified: | 05 Apr 2022 03:05 |
| URI: | http://umpir.ump.edu.my/id/eprint/33623 |
| Download Statistic: | View Download Statistics |
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