Cover Image

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

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 mos...

Full description

Saved in:
Bibliographic Details
Main Authors: Nur Fahriza, Mohd Ali, Ahmad Farhan, Mohd Sadullah, Abdul Majeed, Anwar P. P., Mohd Azraai, Mohd Razman, Choong, Chun Sern, Musa, Rabiu Muazu
Format: Conference or Workshop Item
Language:English
English
Published: Springer Science and Business Media Deutschland GmbH 2022
Subjects:
T Technology (General)
TA Engineering (General). Civil engineering (General)
TJ Mechanical engineering and machinery
TK Electrical engineering. Electronics Nuclear engineering
TS Manufactures
Online Access:http://umpir.ump.edu.my/id/eprint/39753/1/Forecasting%20Daily%20Travel%20Mode%20Choice%20of%20Kuantan%20Travellers.pdf
http://umpir.ump.edu.my/id/eprint/39753/2/Forecasting%20daily%20travel%20mode%20choice%20of%20kuantan%20travellers%20by%20means%20of%20machine%20learning%20models_ABS.pdf
http://umpir.ump.edu.my/id/eprint/39753/
https://doi.org/10.1007/978-981-33-4597-3_89
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.ump.umpir.39753
record_format eprints
spelling my.ump.umpir.397532023-12-26T03:28:00Z http://umpir.ump.edu.my/id/eprint/39753/ Forecasting daily travel mode choice of kuantan travellers by means of machine learning models Nur Fahriza, Mohd Ali Ahmad Farhan, Mohd Sadullah Abdul Majeed, Anwar P. P. Mohd Azraai, Mohd Razman Choong, Chun Sern Musa, Rabiu Muazu T Technology (General) TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery TK Electrical engineering. Electronics Nuclear engineering TS Manufactures 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. Springer Science and Business Media Deutschland GmbH 2022 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/39753/1/Forecasting%20Daily%20Travel%20Mode%20Choice%20of%20Kuantan%20Travellers.pdf pdf en http://umpir.ump.edu.my/id/eprint/39753/2/Forecasting%20daily%20travel%20mode%20choice%20of%20kuantan%20travellers%20by%20means%20of%20machine%20learning%20models_ABS.pdf 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 https://doi.org/10.1007/978-981-33-4597-3_89
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
English
topic T Technology (General)
TA Engineering (General). Civil engineering (General)
TJ Mechanical engineering and machinery
TK Electrical engineering. Electronics Nuclear engineering
TS Manufactures
spellingShingle T Technology (General)
TA Engineering (General). Civil engineering (General)
TJ Mechanical engineering and machinery
TK Electrical engineering. Electronics Nuclear engineering
TS Manufactures
Nur Fahriza, Mohd Ali
Ahmad Farhan, Mohd Sadullah
Abdul Majeed, Anwar P. P.
Mohd Azraai, Mohd Razman
Choong, Chun Sern
Musa, Rabiu Muazu
Forecasting daily travel mode choice of kuantan travellers by means of machine learning models
description 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.
format Conference or Workshop Item
author Nur Fahriza, Mohd Ali
Ahmad Farhan, Mohd Sadullah
Abdul Majeed, Anwar P. P.
Mohd Azraai, Mohd Razman
Choong, Chun Sern
Musa, Rabiu Muazu
author_facet Nur Fahriza, Mohd Ali
Ahmad Farhan, Mohd Sadullah
Abdul Majeed, Anwar P. P.
Mohd Azraai, Mohd Razman
Choong, Chun Sern
Musa, Rabiu Muazu
author_sort Nur Fahriza, Mohd Ali
title Forecasting daily travel mode choice of kuantan travellers by means of machine learning models
title_short Forecasting daily travel mode choice of kuantan travellers by means of machine learning models
title_full Forecasting daily travel mode choice of kuantan travellers by means of machine learning models
title_fullStr Forecasting daily travel mode choice of kuantan travellers by means of machine learning models
title_full_unstemmed Forecasting daily travel mode choice of kuantan travellers by means of machine learning models
title_sort forecasting daily travel mode choice of kuantan travellers by means of machine learning models
publisher Springer Science and Business Media Deutschland GmbH
publishDate 2022
url http://umpir.ump.edu.my/id/eprint/39753/1/Forecasting%20Daily%20Travel%20Mode%20Choice%20of%20Kuantan%20Travellers.pdf
http://umpir.ump.edu.my/id/eprint/39753/2/Forecasting%20daily%20travel%20mode%20choice%20of%20kuantan%20travellers%20by%20means%20of%20machine%20learning%20models_ABS.pdf
http://umpir.ump.edu.my/id/eprint/39753/
https://doi.org/10.1007/978-981-33-4597-3_89
_version_ 1822924008291565568
score 13.232681

Similar Items