Knotted-line: A Visual explorer for uncertainty in transportation system

Abstract Public transport system(PTS) is a complex system, and there are various uncertainties caused by traffic congestion, varying departure interval of buses, etc. In this paper, taking bus as an example, a novel visualization model knotted-line for transportation system uncertainties is proposed. The knotted-line visualization model consists of three parts, the violin plot, multi-layer ring and risk indicator. The violin plot shows the uncertainty of passengers’ waiting time. The multi-layer ring describes the uncertainty of bus arrival time. The risk indicator expresses the possibility of passenger spending time on the trip. A user study is conducted to evaluate the proposed visualization method. The results show that the proposed knotted-line visualization model is helpful for passengers to understand the uncertainty in transportation system and help users to make decisions.

[1]  R. J. Moffat,et al.  Contributions to the Theory of Single-Sample Uncertainty Analysis , 1982 .

[2]  Hong Thi Nguyen,et al.  Visualization of spatio-temporal data of bus trips , 2012, ICCA 2012.

[3]  Sean A. Munson,et al.  When (ish) is My Bus?: User-centered Visualizations of Uncertainty in Everyday, Mobile Predictive Systems , 2016, CHI.

[4]  Alan Borning,et al.  OneBusAway: results from providing real-time arrival information for public transit , 2010, CHI.

[5]  Cláudio T. Silva,et al.  Visual Exploration of Big Spatio-Temporal Urban Data: A Study of New York City Taxi Trips , 2013, IEEE Transactions on Visualization and Computer Graphics.

[6]  Paul Rosen,et al.  From Quantification to Visualization: A Taxonomy of Uncertainty Visualization Approaches , 2011, WoCoUQ.

[7]  Robert M. Edsall,et al.  The Influence of Uncertainty Visualization on Decision Making: An Empirical Evaluation , 2006 .

[8]  Bruno O. Shubert,et al.  Random variables and stochastic processes , 1979 .

[9]  Georg Fuchs,et al.  Visualization of Delay Uncertainty and its Impact on Train Trip Planning: A Design Study , 2017, Comput. Graph. Forum.

[10]  V. Sundarapandian,et al.  Probability, Statistics and Queueing Theory , 2009 .

[11]  S. Joslyn,et al.  Decisions With Uncertainty: The Glass Half Full , 2013 .

[12]  Markus A. Maier,et al.  Color psychology: effects of perceiving color on psychological functioning in humans. , 2014, Annual review of psychology.

[13]  Alex Pang,et al.  Visualizing Uncertainty in Geo-spatial Data , 2001 .

[14]  Martin Steinert,et al.  Displayed Uncertainty Improves Driving Experience and Behavior: The Case of Range Anxiety in an Electric Car , 2015, CHI.

[15]  Edward T. Cokely,et al.  Science Current Directions in Psychological , 2010 .

[16]  Fei-Yue Wang,et al.  Data-Driven Intelligent Transportation Systems: A Survey , 2011, IEEE Transactions on Intelligent Transportation Systems.

[17]  Siyuan Liu,et al.  T-Watcher: A New Visual Analytic System for Effective Traffic Surveillance , 2013, 2013 IEEE 14th International Conference on Mobile Data Management.

[18]  M. Binford,et al.  Calculation and uncertainty analysis of 210Pb dates for PIRLA project lake sediment cores , 1990 .

[19]  Song Wang,et al.  BVis: urban traffic visual analysis based on bus sparse trajectories , 2018, J. Vis..

[20]  Wei Zeng,et al.  Visualizing Mobility of Public Transportation System , 2014, IEEE Transactions on Visualization and Computer Graphics.

[21]  Alex T. Pang,et al.  Approaches to uncertainty visualization , 1996, The Visual Computer.

[22]  Daniel Weiskopf,et al.  Bubble Treemaps for Uncertainty Visualization , 2018, IEEE Transactions on Visualization and Computer Graphics.

[23]  Tamara Munzner,et al.  A Multi-Level Typology of Abstract Visualization Tasks , 2013, IEEE Transactions on Visualization and Computer Graphics.