TrafPS: A Shapley-based Visual Analytics Approach to Interpret Traffic

arXiv:2403.04812v1 Announce Type: new
Abstract: Recent achievements in deep learning (DL) have shown its potential for predicting traffic flows. Such predictions are beneficial for understanding the situation and making decisions in traffic control. However, most state-of-the-art DL models are considered "black boxes" with little to no transparency for end users with respect to the underlying mechanisms. Some previous work tried to "open the black boxes" and increase the interpretability of how predictions are generated. However, it still remains challenging to …

abstract analytics art arxiv black boxes control cs.hc cs.lg decisions deep learning end users however making predictions state traffic transparency type understanding visual visual analytics