rafPS:A shapley-based visual analytics approach to interpret traffic  

作　　者：Zezheng Feng Yifan Jiang Hongjun Wang Zipei Fan Yuxin Ma Shuang-Hua Yang Huamin Qu Xuan Song 

机构地区：[1]Department of Computer Science and Engineering,Southern University of Science and Technology,Shenzhen 18055,China [2]Department of Computer Science and Engineering,Hong Kong University of Science and Technology,Hong Kong,China [3]Department of Computer Science,University of Reading,Berkshire RG66AH,UK [4]Center for Spatial Information Science,the University of Tokyo,Tokyo 113-00331,Japan [5]Shenzhen Key Laboratory of Safety and Security for Next Generation of Industrial Internet,Southern University of Science and Technology,Shenzhen 518055,China

出　　处：《Computational Visual Media》2024年第6期1101-1119,共19页计算可视媒体(英文版)

基　　金：supported in part by a Grant in-Aid for Scientific Research B(22H03573)of the Japan Society for the Promotion of Science(JSPS);in part by the National Natural Science Foundation of China(92067109,61873119);in part by Shenzhen Science and Technology Program(ZDSYS20210623092007023,GJHZ20210705141808024);in part by Guangdong Key Program(2021QN02X794)。

摘　　要：Recent achievements in deep learning(DL)have demonstrated its potential in predicting traffic flows.Such predictions are beneficial for understanding the situation and making traffic control decisions.However,most state-of-the-art DL models are consi-dered“black boxes”with little to no transparency of the underlying mechanisms for end users.Some previous studies attempted to“open the black box”and increase the interpretability of generated predictions.However,handling complex models on large-scale spatiotemporal data and discovering salient spatial and temporal patterns that significantly influence traffic flow remain challenging.To overcome these challenges,we present TrafPS,a visual analytics approach for interpreting traffic prediction outcomes to support decision-making in traffic management and urban planning.The measurements region SHAP and trajectory SHAP are proposed to quantify the impact of flow patterns on urban traffic at different levels.Based on the task requirements from domain experts,we employed an interactive visual interface for the multi-aspect exploration and analysis of significant flow patterns.Two real-world case studies demonstrate the effectiveness of TrafPS in identifying key routes and providing decision-making support for urban planning.

关 键 词：data visualization model interpretation urban planning urban visual analytics 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]