SHapley Additive exPlanations for Explaining Artificial Neural Network Based Mode Choice Models

The black-box nature of Artificial Neural Network (ANN) based transportation models continues to evade their practical application despite their formidable prediction abilities. The purpose of this study is to address the 'black-box’ issue of ANN-based mode choice models utilizing SHapley Additive ExPlanations (SHAP). The SHAP approach is applied to an ANN-based mode choice model in order to explain the model's predictions and comprehend the impact of various variables on mode choice. The work also demonstrates how a detailed investigation of the Shapley explanations of misclassified examples can provide insights to improve the model. In addition, the effect of ANNs' lack of reproducibility on Shapley explanations is explored and reported. The study further demonstrates how transfer learning may be used to enhance model explanations for scenarios with fewer data points. The findings of this study indicate that SHAP can be useful for gaining meaningful insights into ANN-based models, encouraging their adoption in practice.