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The identification of significant features towards travel mode choice and its prediction via optimised random forest classifier : An evaluation for active commuting behavior

Physical activity is the foundation to staying healthy, but sedentary activities have become not uncommon that ought to be mitigated immediately. The study aims to highlight the role of a transport system that encourages physical activity among users by applying an active door-to-door transport syst...

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Main Authors: Nur Fahriza, Mohd Ali, Ahmad Farhan, Mohd Sadullah, P.P. Abdul Majeed, Anwar, Mohd Azraai, Mohd Razman, Musa, Rabiu Muazu
Format: Article
Language:English
English
Published: Elsevier Ltd 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/42652/1/The%20identification%20of%20significant%20features%20towards%20travel%20mode%20choice.pdf
http://umpir.ump.edu.my/id/eprint/42652/2/The%20identification%20of%20significant%20features%20towards%20travel%20mode%20choice%20and%20its%20prediction%20via%20optimised%20random%20forest%20classifier_An%20evaluation%20for%20active%20commuting%20behavior_ABS.pdf
http://umpir.ump.edu.my/id/eprint/42652/
https://doi.org/10.1016/j.jth.2022.101362
https://doi.org/10.1016/j.jth.2022.101362
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spelling my.ump.umpir.426522025-01-07T03:41:34Z http://umpir.ump.edu.my/id/eprint/42652/ The identification of significant features towards travel mode choice and its prediction via optimised random forest classifier : An evaluation for active commuting behavior Nur Fahriza, Mohd Ali Ahmad Farhan, Mohd Sadullah P.P. Abdul Majeed, Anwar Mohd Azraai, Mohd Razman 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 Physical activity is the foundation to staying healthy, but sedentary activities have become not uncommon that ought to be mitigated immediately. The study aims to highlight the role of a transport system that encourages physical activity among users by applying an active door-to-door transport system. Users’ mode choice is studied to understand their preferences for active commuting. The use of machine learning has since been ubiquitous in a myriad of fields, including transportation studies and hence is also investigated towards its efficacy in predicting travel mode choice. Methodology: The application of the Random Forest (RF) model to identify travel mode choice is explored using the Revealed/Stated Preferences (RP/SP) Survey data in Kuantan City during weekdays. A total of 386 respondents were involved in this survey. The efficacy of the tuned RF models towards predicting the travel mode choice is evaluated via the Classification Accuracy (CA) performance indicator. In addition, a Feature Importance study is also carried out in order to identify significant factors that contribute towards travel mode choice. Results: The results from the present investigation demonstrated that the default RF model has acceptable predictability for both training and test dataset of users’ mode choice, with a CA of 70.2% and 69.3%, respectively. Upon identifying the significant features and further refining the hyperparameters of the RF model heuristically, it was shown that with 145 trees, the CA improved to up to 71.6% and 70.1% for both the training and test dataset, respectively. Through the feature selection technique, the most significant features that affect users mode choice are total travel time (TT), waiting time at a public transport stop (WT), region, walking distance from the last stop to destination (WD2), and walking distance from home to the nearest bus stop (WD1). Conclusions: The study has illustrated the efficacy of the optimised RF in predicting travel mode choice as well as identified the significant factors for the selection. The findings of the present study provide significant insight for policymakers to improve the performance of the public transportation system so that the users will benefit in terms of health and well-being from active commuting. Elsevier Ltd 2022-06 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/42652/1/The%20identification%20of%20significant%20features%20towards%20travel%20mode%20choice.pdf pdf en http://umpir.ump.edu.my/id/eprint/42652/2/The%20identification%20of%20significant%20features%20towards%20travel%20mode%20choice%20and%20its%20prediction%20via%20optimised%20random%20forest%20classifier_An%20evaluation%20for%20active%20commuting%20behavior_ABS.pdf Nur Fahriza, Mohd Ali and Ahmad Farhan, Mohd Sadullah and P.P. Abdul Majeed, Anwar and Mohd Azraai, Mohd Razman and Musa, Rabiu Muazu (2022) The identification of significant features towards travel mode choice and its prediction via optimised random forest classifier : An evaluation for active commuting behavior. Journal of Transport and Health, 25 (101362). pp. 1-14. ISSN 2214-1405. (Published) https://doi.org/10.1016/j.jth.2022.101362 https://doi.org/10.1016/j.jth.2022.101362
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
P.P. Abdul Majeed, Anwar
Mohd Azraai, Mohd Razman
Musa, Rabiu Muazu
The identification of significant features towards travel mode choice and its prediction via optimised random forest classifier : An evaluation for active commuting behavior
description Physical activity is the foundation to staying healthy, but sedentary activities have become not uncommon that ought to be mitigated immediately. The study aims to highlight the role of a transport system that encourages physical activity among users by applying an active door-to-door transport system. Users’ mode choice is studied to understand their preferences for active commuting. The use of machine learning has since been ubiquitous in a myriad of fields, including transportation studies and hence is also investigated towards its efficacy in predicting travel mode choice. Methodology: The application of the Random Forest (RF) model to identify travel mode choice is explored using the Revealed/Stated Preferences (RP/SP) Survey data in Kuantan City during weekdays. A total of 386 respondents were involved in this survey. The efficacy of the tuned RF models towards predicting the travel mode choice is evaluated via the Classification Accuracy (CA) performance indicator. In addition, a Feature Importance study is also carried out in order to identify significant factors that contribute towards travel mode choice. Results: The results from the present investigation demonstrated that the default RF model has acceptable predictability for both training and test dataset of users’ mode choice, with a CA of 70.2% and 69.3%, respectively. Upon identifying the significant features and further refining the hyperparameters of the RF model heuristically, it was shown that with 145 trees, the CA improved to up to 71.6% and 70.1% for both the training and test dataset, respectively. Through the feature selection technique, the most significant features that affect users mode choice are total travel time (TT), waiting time at a public transport stop (WT), region, walking distance from the last stop to destination (WD2), and walking distance from home to the nearest bus stop (WD1). Conclusions: The study has illustrated the efficacy of the optimised RF in predicting travel mode choice as well as identified the significant factors for the selection. The findings of the present study provide significant insight for policymakers to improve the performance of the public transportation system so that the users will benefit in terms of health and well-being from active commuting.
format Article
author Nur Fahriza, Mohd Ali
Ahmad Farhan, Mohd Sadullah
P.P. Abdul Majeed, Anwar
Mohd Azraai, Mohd Razman
Musa, Rabiu Muazu
author_facet Nur Fahriza, Mohd Ali
Ahmad Farhan, Mohd Sadullah
P.P. Abdul Majeed, Anwar
Mohd Azraai, Mohd Razman
Musa, Rabiu Muazu
author_sort Nur Fahriza, Mohd Ali
title The identification of significant features towards travel mode choice and its prediction via optimised random forest classifier : An evaluation for active commuting behavior
title_short The identification of significant features towards travel mode choice and its prediction via optimised random forest classifier : An evaluation for active commuting behavior
title_full The identification of significant features towards travel mode choice and its prediction via optimised random forest classifier : An evaluation for active commuting behavior
title_fullStr The identification of significant features towards travel mode choice and its prediction via optimised random forest classifier : An evaluation for active commuting behavior
title_full_unstemmed The identification of significant features towards travel mode choice and its prediction via optimised random forest classifier : An evaluation for active commuting behavior
title_sort identification of significant features towards travel mode choice and its prediction via optimised random forest classifier : an evaluation for active commuting behavior
publisher Elsevier Ltd
publishDate 2022
url http://umpir.ump.edu.my/id/eprint/42652/1/The%20identification%20of%20significant%20features%20towards%20travel%20mode%20choice.pdf
http://umpir.ump.edu.my/id/eprint/42652/2/The%20identification%20of%20significant%20features%20towards%20travel%20mode%20choice%20and%20its%20prediction%20via%20optimised%20random%20forest%20classifier_An%20evaluation%20for%20active%20commuting%20behavior_ABS.pdf
http://umpir.ump.edu.my/id/eprint/42652/
https://doi.org/10.1016/j.jth.2022.101362
https://doi.org/10.1016/j.jth.2022.101362
_version_ 1822924875225890816
score 13.232432

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