Mode choice prediction using machine learning technique for a door-to-door journey in Kuantan City
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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Penerbit UMP
2020
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| Online Access: | http://umpir.ump.edu.my/id/eprint/33623/1/Mode%20choice%20prediction%20using%20machine%20learning%20technique.pdf |
| _version_ | 1796995001964560384 |
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| author | Nur Fahriza, Mohd Ali Ahmad Farhan, Mohd Sadullah Anwar P. P., Abdul Majeed Mohd Azraai, Mohd Razman Rabiu Muazu, Musa |
| author_facet | Nur Fahriza, Mohd Ali Ahmad Farhan, Mohd Sadullah Anwar P. P., Abdul Majeed Mohd Azraai, Mohd Razman Rabiu Muazu, Musa |
| author_sort | Nur Fahriza, Mohd Ali |
| collection | UMP |
| description | 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. |
| first_indexed | 2024-03-06T12:55:54Z |
| format | Article |
| id | UMPir33623 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-03-06T12:55:54Z |
| publishDate | 2020 |
| publisher | Penerbit UMP |
| record_format | dspace |
| spelling | UMPir336232022-04-05T03:05:49Z http://umpir.ump.edu.my/id/eprint/33623/ Mode choice prediction using machine learning technique for a door-to-door journey in Kuantan City Nur Fahriza, Mohd Ali Ahmad Farhan, Mohd Sadullah Anwar P. P., Abdul Majeed Mohd Azraai, Mohd Razman Rabiu Muazu, Musa TK Electrical engineering. Electronics Nuclear engineering 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. Penerbit UMP 2020-06 Article PeerReviewed pdf en cc_by_nc_4 http://umpir.ump.edu.my/id/eprint/33623/1/Mode%20choice%20prediction%20using%20machine%20learning%20technique.pdf 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) https://doi.org/10.15282/mekatronika.v2i1.6745 https://doi.org/10.15282/mekatronika.v2i1.6745 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Nur Fahriza, Mohd Ali Ahmad Farhan, Mohd Sadullah Anwar P. P., Abdul Majeed Mohd Azraai, Mohd Razman Rabiu Muazu, Musa Mode choice prediction using machine learning technique for a door-to-door journey in Kuantan City |
| title | Mode choice prediction using machine learning technique for a door-to-door journey in Kuantan City |
| title_full | Mode choice prediction using machine learning technique for a door-to-door journey in Kuantan City |
| title_fullStr | Mode choice prediction using machine learning technique for a door-to-door journey in Kuantan City |
| title_full_unstemmed | Mode choice prediction using machine learning technique for a door-to-door journey in Kuantan City |
| title_short | Mode choice prediction using machine learning technique for a door-to-door journey in Kuantan City |
| title_sort | mode choice prediction using machine learning technique for a door to door journey in kuantan city |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/33623/1/Mode%20choice%20prediction%20using%20machine%20learning%20technique.pdf |
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