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AuthorAlkhereibi, Aya
AuthorAbuZaid, Ali
AuthorWakjira, Tadesse
Available date2021-10-18T10:50:42Z
Publication Date2021
Publication NameQatar University Annual Research an Exhibition 2021 (quarfe)
CitationAlkhereibi A., AbuZaid A., Wakjira T., "Blue-collar workers' travel behavior modeling using exPlainable machine learning model: The case of Qatar", Qatar University Annual Research Forum and Exhibition (QUARFE 2021), Doha, 20 October 2021, https://doi.org/10.29117/quarfe.2021.0198
URIhttps://doi.org/10.29117/quarfe.2021.0198
URIhttp://hdl.handle.net/10576/24484
AbstractThis paper presents a novel study on the examination of explainable machine learning (ML) technique to predict the mode choice for communities with a majority of blue-collared workers. A total of 4875 trip records for 1050 blue-collared workers have been used to predict their travel mode choices based on 11 trips and socio-economic attributes. The data used in this paper are obtained from the Ministry of Transportation and Communication (MoTC), which targeted blue-collared workers as they represent 89% of the total population in the State of Qatar. A total of four ML models are evaluated to propose the best predictive model. The four models were examined using different performance metrics. The models' prediction results showed that the random forest (RF) model had the highest accuracy with a predictive accuracy of 0.97. Moreover, SHapley Additive exPlanation (SHAP) approach is used to investigate the significance of the input features and explain the output of the RF model. The results of SHAP analysis revealed that occupation level is the most significant feature that influences the mode choice followed by occupation section, arrival time, and arrival municipality.
Languageen
PublisherQatar University Press
SubjectMode choice
SubjectMachine learning
SubjectTransportation planning
SubjectTravel behavior, SHAP
TitleBlue-collar workers' travel behavior modeling using exPlainable machine learning model: The case of Qatar
TypePoster


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