Abstract
As modern society develops, cities become the major living places where people are living in. This phenomenon leads to the high population density in cities, which causes many problems including urban congestion. To better analyze the traffic, models for trajectory data visualization have come up by researchers. Although there have been a lot of studies on trajectory visualization, some studies have performed poorly in terms of user experience. Therefore, in this paper, an improvement in one existing visualization method will be proposed to a more perceptional way to visualize, giving the end-users a clearer perception. Technically, the data used in this research is collected from the existing method, including vehicle speed, traffic direction, location of the vehicle, and traffic volume. Further, the color system used is of better perception and the visualization method is based on JavaScript Object Notation (JSON) and Google Map. With these methods, a final prototype with high effectiveness and better perception will be proposed. Moreover, an evaluation of user study is designed to test user acceptance. By using the improved traffic visualization, drivers will have a better user experience and it is more efficient for traffic observers to analyze the macro traffic.
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References
Dunlu, P., Lidong, C., & Wenjie, X. (2011). Using JSON for data exchanging in web service applications. Journal of Computational Information Systems, 7(April). Retrieved from https://www.researchgate.net/publication/265874991_Using_JSON_for_Data_Exchanging_in_Web_Service_Applications.
Huang, X., Zhao, Y., Yang, J., Zhang, C., Ma, C., & Ye, X. (2015). TrajGraph: A graph-based visual analytics approach to studying. IEEE Transactions on Visualization and Computer Graphics, 22(1), 160–169.
Mazza, R. (2009). Introduction to information visualization (p. 106). Springer Science & Business Media.
Munzner, T. (2018). Visualization analysis and design. In Visualization analysis and design.https://doi.org/10.1201/b17511.
University of Lowa. (n.d.). Perception and visualization. Retrieved from Statistics & Actuarial Science University of Lowa: https://homepage.divms.uiowa.edu/~luke/classes/STAT4580/percep.html.
Wang, Z., Ye, T., Lu, M., & Yuan, X. (2014). Visual exploration of sparse traffic trajectory data. IEEE Transactions on Visualization and Computer Graphics, 1813–1822.
Yuan, X., & Wang, Z. (2014). Urban trajectory timeline visualization. In 2014 international conference on big data and smart computing, BIGCOMP 2014 (pp. 13–18). https://doi.org/10.1109/BIGCOMP.2014.6741397.
Zeng, W., Fu, C.-W., Arisona, S. M., Erath, A., & Qu, H. (December 2014). Visualizing mobility of public transportation system. IEEE Transactions on Visualization and Computer Graphics, 1833–1842.
Acknowledgement
This work was supported by the Xiamen University Malaysia Research Fund (XMUMRF) (Grant No: XMUMRF/2019-C3/IECE/0007).
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Yuchen, L., Zijie, W., Qianling, H., Mehmood, R.M. (2022). A Novel Method of Trajectory Data Visualization to Analyze the Current Traffic Situation in Smart Cities. In: Abdalla, H., Rodrigues, H., Gahlot, V., Salah Uddin, M., Fukuda, T. (eds) Resilient and Responsible Smart Cities. Advances in Science, Technology & Innovation. Springer, Cham. https://doi.org/10.1007/978-3-030-86499-6_9
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