Abstract
Urban traffic data records people’s individual movement and behavioral histories, which implies underlying human mobility patterns. How to effectively mine this pattern is the key to the building of a smart city. Traditional visual analysis methods only support exploration based on mobility patterns with fixed granularity, which limits the possibility of knowledge discovery at the beginning of analysis. However, the methods based on multi-granularity mobility patterns proposed in recent years have varying degrees of deficiencies in human perception or analysis efficiency. To balance the above problems, we design a comprehensive visual analysis system that supports both adaptive granularity refinement and complex semantic exploration of human mobility patterns. We introduce a hierarchical topic model H-NMF to extract multi-granularity traffic topics to capture mobility patterns with different levels of detail. Then, we design a progressive granularity refinement visualization method based on interactive Sankey diagram, which is more in line with the top-down cognitive process in human perception. The user can follow the visual cues provided by our system and gradually make decisions to obtain the desired granularity of traffic topics even for dataset without any prior knowledge. We further optimize the analysis efficiency of the refining process by taking the complete visual analysis process of traffic topics into consideration. The tensor decomposition model is used to extract the general spatio-temporal features of traffic topics to help users quickly obtain the basic semantics of traffic topics, which effectively reduces the analysis cost of users in the refinement. Besides, we design rich detail analysis visualizations to help users fully explore the semantics of traffic topics. All the above designs are well integrated in our visual analysis system which contains clear visualizations and user-friendly interactions. Finally, we verify the effectiveness of our method on real-world dataset with two case studies.
Graphical abstract
Similar content being viewed by others
References
Al-Dohuki S et al (2017) SemanticTraj: A new approach to interacting with massive taxi trajectories. IEEE Trans vis Comput Graphics 23(1):11–20
Andrienko G, Andrienko N, Chen W, Maciejewski R, Zhao Y (2017) Visual analytics of mobility and transportation: State of the art and further research directions. IEEE Trans Intell Trans Syst 18(8):2232–2249
Blei D, Ng A, Jordan M (2003) Latent Dirichlet allocation. J Mach Learn Res 3:993–1022
Cao N, Lin C, Zhu Q, Lin Y-R, Teng X, Wen X (2018) Voila: Visual anomaly detection and monitoring with streaming spatiotemporal data. IEEE Trans Visual Comput Graphics 24(1):23–33
Chai T, Draxler RR (2014) Root mean square error (RMSE) or mean absolute error (MAE)?–Arguments against avoiding RMSE in the literature. Geosci Model Develop 7(3):1247–1250
Chen W, Guo F, Wang F-Y (2015) A survey of traffic data visualization. IEEE TITS 16(6):2970–2984
Cressie N, Huang H-C (1999) Classes of nonseparable, spatio-temporal stationary covariance functions. J Am Stat Assoc 94(448):1330–1339
Cressie N, Shi T, Kang EL (2010) Fixed rank filtering for spatio-temporal data. J Comput Graph Stat 19(3):724–745
D. Liu, P. Xu and L. Ren, TPFlow: Progressive partition and multidimensional pattern extraction for large-scale spatio-temporal data analysis, IEEE Transactions on Visualization and Computer Graphics, vol 25 no 1 2019c.
D. Chu, D. A. Sheets, Y. Zhao, Y. Wu, J. Yang, M. Zheng, et al., “Visualizing hidden themes of taxi movement with semantic transformation”, Pacific Visualization Symposium (PacificVis) 2014 IEEE, pp 137-144 2014
Deerwester S, Dumais ST, Furnas GW, Landauer TK, Harshman R (1990) Indexing by latent semantic analysis. J Am Soc Inf Sci 41(6):391–407
Deng JS, Wang K, Hong Y, Qi JG (2009) Spatio-temporal dynamics and evolution of land use change and landscape pattern in response to rapid urbanization. Landsc Urban Plan 92(3):187–198
E. V. Bonilla, K. M. Chai, and C. Williams, “Multi-task gaussian process prediction. In Advances in neural information processing systems”, pp 153–160 2008
Gleicher M, Albers D, Walker R, Jusufi I, Hansen C, Roberts J (2011) Visual comparison for information visualization. Inf vis 10(4):289–309
H. Dai, Y. Tao and H. Lin, Visual analytics of urban transportation from a bike-sharing and taxi perspective, Proc VINCI, pp 1–8, 2019
Han SH, Chen L (1996) The relationship between global properties and local properties-global precedence. Adv Psychol Sci 4(1):36–41
Jin Z, Cao N, Shi Y, Wu W, Wu Y (2021) EcoLens: visual analysis of ecological regions in urban contexts using traffic data. J Visualization 24(2):349–364
K. Da, P. Haesun, “Fast rank-2 nonnegative matrix factorization for hierarchical document clustering”, in: 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, pp 11-14 2013
Kolda TG, Bader BW (2009) Tensor decompositions and applications. SIAM Rev 51(3):455–500
Lee DD, Sebastian SH (2001) Algorithms for non-negative matrix factorization. Neural information processing systems, ACM, pp 556–562
Liu K, Gao S, Lu F (2019) Identifying spatial interaction patterns of vehicle movements on urban road networks by topic modelling. Comput Environ Urban Syst 74:50–61
Liu H, Jin S, Yan Y, Tao Y, Lin H (2019b) Visual analytics of taxi trajectory data via topical sub-trajectories. Vis Informatics 3(3):140–149
Liu D, Weng D, Li Y, Bao J, Zheng Y, Qu H et al (2017) Smartadp: Visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE TVCG 23(1):1–10
Liu L, Zhang H, Liu J, Liu S, Chen W, Man J (2021) Visual exploration of urban functional zones based on augmented nonnegative tensor factorization. J Visualization 24(2):331–347
Liu L, Zhang H, Liu J, Man J, Visual analysis of traffic data via spatio-temporal graphs and interactive topic modeling. J Visual pp 141–160 2019a
Navon D (1977) Forest before trees: The precedence of global features in visual perception. Cogn Psychol 9:353–383
Puissant A, Hirsch J, Weber C (2005) The utility of texture analysis to improve per-pixel classification for high to very high spatial resolution imagery. Int J Remote Sens 26(4):733–745
R. Krueger, D. Thom and T. Ertl, Visual analysis of movement behavior using web data for context enrichment. IEEE Pacific Visualization Symposium, pp 193–200 2014
Rubayyi Alghamdi and Khalid Alfalqi, “A Survey of Topic Modeling in Text Mining”, International Journal of Advanced Computer Science and Applications, vol 6 no 1 2015
Salton G, Yang CS, Yu CT (1975) A theory of term importance in automatic text analysis. J Am Soc Inf Sci 26(1):33–44
Seto KC, Fragkias M (2005) Quantifying spatiotemporal patterns of urban land-use change in four cities of china with time series landscape metrics. Landscape Ecol 20(7):871–888
Shi L, Huang C, Liu M, Yan J, Jiang T, Tan Z, Hu Y, Chen W, Zhang X (2020) UrbanMotion: Visual analysis of metropolitan-scale sparse trajectories. IEEE Trans vis Comput Graph 27(10):3881–3899
Shi X, Lv F, Seng D, Xing B, Chen B (2019a) Visual exploration of mobility dynamics based on multi-source mobility datasets and poi information. J Visualization 22(6):1209–1223
Shi X, Lv F, Seng D, Xing B, Chen J (2019b) Exploring the evolutionary patterns of urban activity areas based on origin-destination data. IEEE Access 7:20416–20431
T. Hofmann, Probabilistic latent semantic indexing. In: 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, ACM pp 50–57 1999
Tang Y, Sheng F, Zhang H, Shi C, Qin X, Fan J (2018) Visual analysis of traffic data based on topic modeling (chinavis 2017). J vis 21(4):661–680
W. Wu, Y. Zheng, N. Cao, H. Zeng, B. Ni, H. Qu, and L. M.Ni, Mobiseg: Interactive region segmentation using heterogeneous mobility data. In: IEEE Pacific Visualization Symposium. IEEE 2017
Wang G (2017) DGCC: data-driven granular cognitive computing. Granular Comput 2(4):1–13
Weng D, Zhu H, Bao J, Zheng Y, Wu Y. Homefinder revisited: Finding ideal homes with reachability-centric multi-criteria decision making. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems 2018 (pp 1-12)
Witold Pedrycz, Granular Computing: analysis and Design of Intelligent Systems. CRC Press 2013
Yan Y, Tao Y, Xu J, Ren S, Lin H (2018) Visual analytics of bike-sharing data based on tensor factorization. J vis 3:495–509
Yuan J, Chen C, Yang W, Liu M, Xia J, Liu S (2020) A survey of visual analytics techniques for machine learning. Comput vis Med 1:3–6
Yuan J, Zheng Y, Xie X. Discovering regions of different functions in a city using human mobility and POIs. In: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining 2012 (pp 186-194)
Zhou Z, Yu J, Guo Z, Liu Y (2018) Visual exploration of urban functions via spatio-temporal taxi OD data. J vis Lang Comput 48:169–177
Acknowledgements
This work was supported by the National Natural Science Foundation of China (Grant No.61972355 and Grant No. 72192823).
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Tao, Y., Tang, Y. Progressive visual analysis of traffic data based on hierarchical topic refinement and detail analysis. J Vis 26, 367–384 (2023). https://doi.org/10.1007/s12650-022-00879-y
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12650-022-00879-y