Abstract
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 considered “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.
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Acknowledgements
We thank all the domain experts interviewed in this study. We also thank the reviewers for their comments and suggestions. This work was 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), and in part by Guangdong Key Program (2021QN02X794).
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Zezheng Feng received his B.E. degree from Northeastern University (NEU), China, in 2017, and his M.S. degree (with distinction) from Loughborough University, UK, in 2018. He is currently a Ph.D. candidate in the Department of Computer Science and Engineering (CSE) at the Hong Kong University of Science and Technology (HKUST) and sponsored by a joint Ph.D. program between HKUST and Southern University of Science and Technology (SUSTech). His recent research interests include visualization and visual analytics, explainable artificial intelligence (XAI), and urban computing. For more information, please visit https://jerrodfeng.github.io/
Yifan Jiang is working toward an M.S. degree in computer science from USC. He received his B.E. degree from the Southern University of Science and Technology in 2021. His research interests are machine learning explanation, natural language processes, commonsense reasoning, and data visualization.
Hongjun Wang is working toward an M.S. degree in computer science and technology from the Southern University of Science and Technology, China. He received his B.E. degree from the Nanjing University of Posts and Telecommunications, China, in 2019. His research interests are broadly in machine learning, urban computing, explainable AI, data mining, and data visualization.
Zipei Fan is a project lecturer at the University of Tokyo. He graduated from School of Computer Science and Engineering of Beihang University in 2012 and received his master and doctoral degrees on civil engineering from the University of Tokyo in 2014 and 2017 respectively. His research interests include data mining, Internet of Things, machine learning, and applications on smart city. He has published more than 40 papers in journals and conferences including TKDE, IMWUT/UbiComp, WWWJ, IJCAI, CIKM, SIGSPATIAL, etc., and have been invited as reviewers for conferences and journals such as IJCAI, AAAI, ECML, TKDE, UbiComp, Transactions on Mobile Computing (TMC), WWWJ, Transactions on Big Data (TBD), etc.
Yuxin Ma is a tenure-track assistant professor in the Department of Computer Science and Engineering, Southern University of Science and Technology (SUSTech), China. He received his B.Eng. and Ph.D. degrees from Zhejiang University. Before joining SUSTech, he worked as a postdoctoral research associate in VADER Lab, CIDSE, Arizona State University. His primary research interests are in the areas of visualization and visual analytics, focusing on explainable AI, high-dimensional data, and spatiotemporal data.
Shuang-Hua Yang received his B.S. degree in instrument and automation and his M.S. degree in process control from the China University of Petroleum (Huadong), Beijing, China, in 1983 and 1986, respectively, and his Ph.D. degree in intelligent systems from Zhejiang University, Hangzhou, China, in 1991. He is currently the director of the Shenzhen Key Laboratory of Safety and Security for Next Generation of Industrial Internet at the Southern University of Science and Technology, China, and also the head of Department of Computer Science at the University of Reading, UK. His research interests include cyber-physical systems, the Internet of Things, wireless network-based monitoring and control, and safety-critical systems. He is a Fellow of IET and InstMC, UK. He is also an associate editor of IET Cyber-Physical Systems: Theory and Applications.
Huamin Qu is a professor in the Department of Computer Science and Engineering (CSE) at the Hong Kong University of Science and Technology (HKUST) and also the director of the interdisciplinary program office (IPO) of HKUST. He obtained his B.S. degree in mathematics from Xi’an Jiaotong University, China, his M.S. and Ph.D. degrees in computer science from the Stony Brook University. His main research interests are in visualization and human–computer interaction, with focuses on urban informatics, social network analysis, E-learning, text visualization, and explainable artificial intelligence (XAI). For more information, please visit http://huamin.org/
Xuan Song received his Ph.D. degree in signal and information processing from Peking University in 2010. In 2017, he was selected as an Excellent Young Researcher of Japan MEXT. In the past ten years, he led and participated in many important projects as a principal investigator or primary actor in Japan, such as the DIAS/GRENE Grant of MEXT, Japan; Japan/US Big Data and Disaster Project of JST, Japan; Young Scientists Grant and Scientific Research Grant of MEXT, Japan; Research Grant of MLIT, Japan; CORE Project of Microsoft; Grant of JR EAST Company and Hitachi Company, Japan. He served as associate editor, guest editor, area chair, program committee member, or reviewer for many famous journals and top-tier conferences, such as IMWUT, IEEE Transactions on Multimedia, WWW Journal, Big Data Journal, ISTC, MIPR, ACM TIST, IEEE TKDE, UbiComp, ICCV, CVPR, ICRA, etc.
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Feng, Z., Jiang, Y., Wang, H. et al. TrafPS: A shapley-based visual analytics approach to interpret traffic. Comp. Visual Media (2024). https://doi.org/10.1007/s41095-023-0351-7
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DOI: https://doi.org/10.1007/s41095-023-0351-7