Forecasting daily travel mode choice of kuantan travellers by means of machine learning models

Nur Fahriza, Mohd Ali and Ahmad Farhan, Mohd Sadullah and Abdul Majeed, Anwar P. P. and Mohd Azraai, Mohd Razman and Choong, Chun Sern and Musa, Rabiu Muazu (2022) Forecasting daily travel mode choice of kuantan travellers by means of machine learning models. In: Lecture Notes in Electrical Engineering; Innovative Manufacturing, Mechatronics and Materials Forum, iM3F 2020 , 6 August 2020 , Gambang, Kuantan. pp. 979-987., 730 (262829). ISSN 1876-1100 ISBN 978-981334596-6

	Pdf Forecasting Daily Travel Mode Choice of Kuantan Travellers.pdf Restricted to Repository staff only Download (530kB) | Request a copy
Preview	Pdf Forecasting daily travel mode choice of kuantan travellers by means of machine learning models_ABS.pdf Download (68kB) | Preview

Abstract

In transportation studies, forecasting users’ mode choice in daily commute is crucial in order to manage traffic problems due to high number of private vehicles on the road. Conventional statistical techniques have been widely used in order to study users’ mode choice; however, the choice of the most appropriate forecasting method still remains a significant concern. In this paper, we investigate the application of a number of machine learning models, namely Random Forest (RF), Tree, Naïve Bayes (NB), Logistic Regression (LR), k-Nearest Neighbour (k-NN), Support Vector Machine (SVM), as well as Artificial Neural Networks (ANN) in predicting the daily travel mode choice in Kuantan. The data was collected from a survey of Revealed/Stated Preferences (RPSP) Survey among Kuantan travellers in which eight features were taken into consideration in the present study. The classifiers were trained on the collected dataset by using five-folds cross-validation method to predict the daily mode choice. It was shown from this preliminary study that the RF, as well as ANN classifiers, could provide satisfactory classification accuracies to up to 70% in comparison to the other models evaluated. Therefore, it could be concluded that the evaluated features are rather important in deciding the travel model choice of Kuantan travellers.

Item Type:	Conference or Workshop Item (Lecture)
Additional Information:	Indexed by Scopus
Uncontrolled Keywords:	Machine learning; Mode choice; Private vehicles; Public transport
Subjects:	T Technology > T Technology (General) T Technology > TA Engineering (General). Civil engineering (General) T Technology > TJ Mechanical engineering and machinery T Technology > TK Electrical engineering. Electronics Nuclear engineering T Technology > TS Manufactures
Faculty/Division:	Institute of Postgraduate Studies College of Engineering Faculty of Manufacturing and Mechatronic Engineering Technology
Depositing User:	Mr Muhamad Firdaus Janih@Jaini
Date Deposited:	26 Dec 2023 03:28
Last Modified:	26 Dec 2023 03:28
URI:	http://umpir.ump.edu.my/id/eprint/39753
Download Statistic:	View Download Statistics

Actions (login required)

View Item