TCEVis: Visual analytics of traffic congestion influencing factors based on explainable machine learning

Abstract

Traffic congestion is becoming increasingly severe as a result of urbanization, which not only impedes people’s ability to travel but also hinders the economic development of cities. Modeling the correlation between congestion and its influencing factors using machine learning methods makes it possible to quickly identify congested road segments. Due to the intrinsic black-box character of machine learning models, it is difficult for experts to trust the decision results of road congestion prediction models and understand the significance of congestion-causing factors. In this paper, we present a model interpretability method to investigate the potential causes of traffic congestion and quantify the importance of various influencing factors using the SHAP method. Due to the multidimensionality of these factors, it can be challenging to visually represent the impact of all factors. In response, we propose TCEVis, an interactive visual analytics system that enables multi-level exploration of road conditions. Through three case studies utilizing actual data, we demonstrate that the TCEVis system offers advantages for assisting traffic managers in analyzing the causes of traffic congestion and elucidating the significance of various influencing factors.