Analysis of mode choice affects from the introduction of Doha Metro using machine learning and statistical analysis

The aim of this study was to investigate the possible influences of the operation of the new Doha Metro on the travel mode choice behavior in Doha City, Qatar. Revealed preference (RP) and stated preference (SP) survey questionnaires were designed to collect the necessary data. The questions conside...

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محفوظ في:
التفاصيل البيبلوغرافية
المؤلف الرئيسي: Ammar, Abulibdeh (author)
التنسيق: article
منشور في: 2023
الموضوعات:
الوصول للمادة أونلاين:http://dx.doi.org/10.1016/j.trip.2023.100852
https://www.sciencedirect.com/science/article/pii/S2590198223000994
http://hdl.handle.net/10576/55720
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author Ammar, Abulibdeh
author_facet Ammar, Abulibdeh
author_role author
dc.creator.none.fl_str_mv Ammar, Abulibdeh
dc.date.none.fl_str_mv 2023-03-31
2024-06-02T08:04:25Z
dc.format.none.fl_str_mv application/pdf
dc.identifier.none.fl_str_mv http://dx.doi.org/10.1016/j.trip.2023.100852
Abulibdeh, A. (2023). Analysis of mode choice affects from the introduction of Doha Metro using machine learning and statistical analysis. Transportation Research Interdisciplinary Perspectives, 20, 100852.
https://www.sciencedirect.com/science/article/pii/S2590198223000994
http://hdl.handle.net/10576/55720
20
2590-1982
dc.language.none.fl_str_mv en
dc.publisher.none.fl_str_mv Elsevier
dc.rights.none.fl_str_mv http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
dc.subject.none.fl_str_mv Machine learning
FIFA World Cup 2022
Travel behavior
Trip characteristic
Stated preference survey
Statistical analysis
dc.title.none.fl_str_mv Analysis of mode choice affects from the introduction of Doha Metro using machine learning and statistical analysis
dc.type.none.fl_str_mv Article
info:eu-repo/semantics/publishedVersion
info:eu-repo/semantics/article
description The aim of this study was to investigate the possible influences of the operation of the new Doha Metro on the travel mode choice behavior in Doha City, Qatar. Revealed preference (RP) and stated preference (SP) survey questionnaires were designed to collect the necessary data. The questions considered different trip conditions and socioeconomic factors of travelers. Three different mode choices were considered in this study: private cars, taxi services, and metro. Two statistical models and one machine learning model were used to analyze the current and future mode choices: discrete choice binary logit (BL) and multinomial logit (MNL) models as well as extreme gradient boosting (XGBoost). Furthermore, the SHapley Additive exPlanations (SHAP) method was used to rank the input features based on their importance according to the mean SHAP value. The results showed that the XGBoost model outperforms the other two models in terms of predicting the travel mode choice as well as in terms of its accuracy. The results showed that various trip characteristics are significant in determining the mode choice, including the number of travelers and bags, journey time, and reimbursement of parking fees. Furthermore, different socioeconomic characteristics proved to be significant for the current and future mode choices, including nationality, income, age, employment status, and vehicle ownership.
eu_rights_str_mv openAccess
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id qu_f3e43d4c2e2f5dcd186b596c664d2502
identifier_str_mv Abulibdeh, A. (2023). Analysis of mode choice affects from the introduction of Doha Metro using machine learning and statistical analysis. Transportation Research Interdisciplinary Perspectives, 20, 100852.
20
2590-1982
language_invalid_str_mv en
network_acronym_str qu
network_name_str Qatar University repository
oai_identifier_str oai:qspace.qu.edu.qa:10576/55720
publishDate 2023
publisher.none.fl_str_mv Elsevier
repository.mail.fl_str_mv
repository.name.fl_str_mv
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rights_invalid_str_mv http://creativecommons.org/licenses/by/4.0/
spelling Analysis of mode choice affects from the introduction of Doha Metro using machine learning and statistical analysisAmmar, AbulibdehMachine learningFIFA World Cup 2022Travel behaviorTrip characteristicStated preference surveyStatistical analysisThe aim of this study was to investigate the possible influences of the operation of the new Doha Metro on the travel mode choice behavior in Doha City, Qatar. Revealed preference (RP) and stated preference (SP) survey questionnaires were designed to collect the necessary data. The questions considered different trip conditions and socioeconomic factors of travelers. Three different mode choices were considered in this study: private cars, taxi services, and metro. Two statistical models and one machine learning model were used to analyze the current and future mode choices: discrete choice binary logit (BL) and multinomial logit (MNL) models as well as extreme gradient boosting (XGBoost). Furthermore, the SHapley Additive exPlanations (SHAP) method was used to rank the input features based on their importance according to the mean SHAP value. The results showed that the XGBoost model outperforms the other two models in terms of predicting the travel mode choice as well as in terms of its accuracy. The results showed that various trip characteristics are significant in determining the mode choice, including the number of travelers and bags, journey time, and reimbursement of parking fees. Furthermore, different socioeconomic characteristics proved to be significant for the current and future mode choices, including nationality, income, age, employment status, and vehicle ownership.Open Access funding provided by the Qatar National Library.Elsevier2024-06-02T08:04:25Z2023-03-31Articleinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://dx.doi.org/10.1016/j.trip.2023.100852Abulibdeh, A. (2023). Analysis of mode choice affects from the introduction of Doha Metro using machine learning and statistical analysis. Transportation Research Interdisciplinary Perspectives, 20, 100852.https://www.sciencedirect.com/science/article/pii/S2590198223000994http://hdl.handle.net/10576/55720202590-1982enhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:qspace.qu.edu.qa:10576/557202024-07-23T15:53:53Z
spellingShingle Analysis of mode choice affects from the introduction of Doha Metro using machine learning and statistical analysis
Ammar, Abulibdeh
Machine learning
FIFA World Cup 2022
Travel behavior
Trip characteristic
Stated preference survey
Statistical analysis
status_str publishedVersion
title Analysis of mode choice affects from the introduction of Doha Metro using machine learning and statistical analysis
title_full Analysis of mode choice affects from the introduction of Doha Metro using machine learning and statistical analysis
title_fullStr Analysis of mode choice affects from the introduction of Doha Metro using machine learning and statistical analysis
title_full_unstemmed Analysis of mode choice affects from the introduction of Doha Metro using machine learning and statistical analysis
title_short Analysis of mode choice affects from the introduction of Doha Metro using machine learning and statistical analysis
title_sort Analysis of mode choice affects from the introduction of Doha Metro using machine learning and statistical analysis
topic Machine learning
FIFA World Cup 2022
Travel behavior
Trip characteristic
Stated preference survey
Statistical analysis
url http://dx.doi.org/10.1016/j.trip.2023.100852
https://www.sciencedirect.com/science/article/pii/S2590198223000994
http://hdl.handle.net/10576/55720