Blue-collar workers' travel behavior modeling using exPlainable machine learning model: The case of Qatar

Abstract

This 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.