A Survey of Traffic Data Visualization

1. A Survey of Traffic Data Visualization
Published in 2015/ IEEE Transactions on Visualization and Computer Graphics
Wei Chen, Fangzhou Guo, and Fei-Yue Wang

2. 1
Background
• Traffic datasets are generally high-dimensional or spatial-temporal
 Information visualization and visual analytics
• Tasks of existing traffic data analysis applications
 Visual monitoring of traffic situations
 Pattern discovery and clustering
 Situation-aware exploration and prediction
 Route planning and recommendation
Conceptual pipeline of traffic data visualization

3. 2
Traffic Data
• Working modes of sensors
 Location-Based
• Record location of an object upon entering the sensor range (Video monitoring device)
 Activity-Based
• Record when a certain activity is carried (GSM locations)
 Device-Based
• Recorded by a device carried by an object (GPS device)
• Forms of data
 Trajectory
• Movement directions / speed
• Change of direction / speed
 Incident logs

4. 3
Data Preprocessing
• Data cleaning
 Errors, outliers and conflicting values should be cleaned
• Auditing data to find discrepancies
• Choosing transformations to fix such discrepancies
• Applying the transformations
• Data matching
 Map matching, aligning a sequence of observed with road network
• Data organization
 Well-organized database supporting interactive query
• Data aggregation
 Reducing the data size and providing convenience in subsequent analysis
 Basic operations: spatial (S), temporal (T), directional (D), attribute (A)
• SxT, SxTxA, SxSxTxT, SxTxD, SxS

5. 4
Visualization of Time
• Emphasize on the display of trend, periodicity, and abnormality of data along the
time axis
 Linear Time
• Time is represented along the X-axis
• Another variate is represented along the Y –axis
• Line charts, stacked graph (e.g. ThemeRiver)
 Periodic Time
• Iterations of seasons, weeks, and days
• Show periodic patterns
• Low space efficiency
 Branching Time
• A visual metaphor, Storylines
• Depict the progress, joining, branching, and disappearance of a specific event
• Traffic data has not yet been visualized in this way

6. 5
Visualization of Spatial Properties
• Based on the aggregation level of location information, categorized as follows:
 Point-Based Visualization (no aggregation)
• Considers samples of traffic information as individual discrete dots
• Intuitively show the position of objects at a certain time point
• E.g. Trains of Data project
• Dot size: passenger number, color: delay level
• Advantages
• Observe the states of every points
• Show distribution of vehicles
• Disadvantages
• With the large number of objects, hard to understand
• Inefficient in showing continuous information

7. 6
Visualization of Spatial Properties
• Based on the aggregation level of location information, categorized as follows:
 Line-Based Visualization (first-order aggregation)
• Display traffic trajectories, roadmaps in a large-scale region, or traffic flow in a distributed
network
• Semantic mining of trajectories, trajectory clustering, and route recommendation
• Transformed into other forms or simplified using topological and geometric algorithms
• E.g. Edge Bundling
• Transforms and groups similar edges into bundles
• Reduce the clutter problem
• Difficult to recognize the actual direction of connection
• Guo et al. estimates the flow density for each pair of locations by a vector-based density
model
• KDE can be applied by indicating the density of trajectories using color (rather than twisting
and clustering edges)
geometry-based
skeleton-based
D. Guo and X. Zhu, “Origin–destination flow data smoothing and mapping,” IEEE Trans. Vis. Comput. Graphics,
vol. 20, no. 12, pp. 2043–2052, Dec. 2014.

8. • Based on the aggregation level of location information, categorized as follows:
 Region-Based Visualization (second-order aggregation)
• Shows the traffic situation based on individual regions
• Traffic data are aggregated into regions based on predetermined rules
• E.g. Zeng et al. visualizes the interchange patterns using a radial metaphor
• Reveal macro patterns in traffic data
• Inadequate for analyzing micro patterns
• Spatio-Temporal Visualization
 Space-Time-Cube (STC): 3D trajectory is visualized in a 3D coordinate system
• X-axis and the Y -axis is used for mapping spatial geographic information
• Z-axis represents the time axis
7
Visualization of Spatial Properties
W. Zeng, C.-W. Fu, S. M. Arisona, and H. Qu, “Visualizing interchange patterns in massive movement data,”
Comput. Graph. Forum, vol. 32, no. 3. pp. 271–280, Jun. 2013.

9. • Various attributes in addition to spatial and temporal information is as follows:
 Numerical Properties
• Continuous variables that represent quantitative values of data objects
• E.g. velocity, acceleration, weight and so on
• Histogram is a good choice for visualization
 Categorical Properties
• Discrete variables that describe the state of data objects
• E.g. directions, vehicle types and incident types
• Color mapping for visualization
 Textual Properties
• Words, lexical information, or logs that describe extra information about the traffic
• E.g. vehicle names involved in an incident, point of interests, and so on
• Text-based visualization techniques like TagCloud and Wordle can be employed to show a set
of words
8
Visualization of Multiple Properties

10. • Standard STC can be enhanced
 GeoTime [14]
• Adds objects and events in the STC
• Each event is added to the track and placed around a corresponding time node
 Stacking-based STC [15]
• The latter method stacks multiple trajectories along the Z-axis, and visualizes them
as stacked bands to depict velocity
 Enhanced multivariate data visualization techniques
• Similarity lens [11]
• Show the distribution of taxis, average speed of taxis and pick-up/drop-off activities
on roads
• Euclidean distance against the average are simultaneously
• Trip view [16]
• Show temporal, spatial, statistical and other attributes of taxi trajectory data
• Interchange circos diagram (ICD) showing junction node, flow volume, direction
of flow and statistics of flow volume. [17]
9
Spatio-Temporal Visualization and Properties
[14] T. Kapler and W. Wright, “Geotime information visualization,” Inf. Vis., vol. 4, no. 2, pp. 136–146,Summer 2005.
[15] C. Tominski, H. Schumann, G. Andrienko, and N. Andrienko, “Stackingbased visualization of trajectory attribute data,” IEEE Trans. Vis. Comput.
[17] H. Liu et al., “Visualanalysisof route diversity,” in Proc. IEEE Conf. VisualAnal. Sci. Technol., 2011,pp. 171–180
[18] W. Zeng, C.-W. Fu, S. M. Arisona,and H. Qu, “Visualizing interchange patterns in massivemovement data,” Comput. Graph. Forum, vol. 32, no. 3. pp. 271–280, Jun. 2013.

11. 10
Visual Analysis of Traffic Data

12. • Exploring traffic information
• Ferreira et al. proposed a new model that allows the user to visually query taxi trips
[2]
 3 types of query constraints: spatial, temporal and attribute constraints
 Map view: displays the query results and specifies the spatial constraints
 Time selection widget: specifies the temporal constraints
 Data summary view: shows the information associated with the results of the queries
• Wang et al. evaluates the real traffic situations based on taxi trajectory data [2]
 A road-based query model and a hash-based data structure are proposed to support
dynamic query for trajectories
• Wang et al. presented a visual analytics system to explore sparse traffic data [3]
 Local animation and aggregation techniques are employed to address the uncertainty
problem in sparse data
11
Situation-Aware Exploration and Prediction
[1] N. Ferreira, J. Poco, H. T. Vo, J. Freire, and C. T. Silva, “Visualexploration of big spatio-temporal urban data: A study of New York City taxi trips,” IEEE Trans. Vis. Comput. Graphics, vol. 19, no. 12, pp. 2149–2158, Dec. 2013.
[2] F. Wang et al., “A visual reasoningapproach for data-driven transport assessment on urban road,” in Proc. IEEE Conf. Visual Anal. Sci. Technol., 2014, pp. 103–112.
[3] Z. Wang et al., “Visualexploration of sparse traffic trajectory data,” IEEE Trans. Vis. Comput. Graphics,vol. 20, no. 12, pp. 1813–1822,Dec. 2014.

13. • Detecting patterns in object movement and clustering the trajectories
• Schreck et al. proposed a visual-interactive monitoring and control framework [4]
 The combination of automatic data analysis and human expert supervision
 User can monitor and control the SOM (Kohonen feature map) clustering process and obtain appropriate
cluster results
• Andrienko et al. and Rinzivillo et al. apply the OPTICS algorithm to cluster the trajectory data [5, 6]
 Refine clustering result by interactive visualization and manipulation (excluding, making, and dividing)
• Triple Perspective Visual Trajectory Analytics (TripVista) is an interactive visual analytics system for
exploring and analyzing
 3 Views:
• Traffic (spatial information)
• ThemeRiver (directional information)
• PCP (displaying multidimensional data)
12
Pattern Discovery and Clustering
[4] T. Schreck, J. Bernard, T. Von Landesberger, and J. Kohlhammer, “Visualcluster analysis of trajectory data with interactive Kohonen maps,” Inf. Vis., vol. 8, no. 1, pp. 14–29,Jan. 2009.
[5] G. Andrienko et al., “Interactive visualclustering of large collections of trajectories,” in Proc. IEEE Symp. VisualAnal. Sci. Technol., 2009,pp. 3–10.
[6] S. Rinzivillo et al., “Visually driven analysisof movement data by progressive clustering,” Inf. Vis.,vol. 7, no. 3/4, pp. 225–239,Jun. 2008.
[7] H. Guo, Z. Wang, B. Yu, H. Zhao, and X. Yuan, “TripVista: Triple perspective visual trajectory analytics and its application on microscopic traffic data at a road intersection,” in Proc. IEEE Pac. Vis. Symp., 2011,pp. 163–170.

14. • Traffic monitoring focuses on investigating traffic incidents
• Incident Cluster Explorer (ICE) is an application that studies
transportation incident datasets [8]
 Geospatial visualization (map), histogram, 2D plots, and PCP are integrated
 Incidents map in two modes of icon and heatmap
• Traffic Origins system [9]
 Emphasize the start and the end of an incident
 When incident occurs, circle appears and when incident ends, it fades out
• AIVis is a system that monitors traffic situations in road tunnels [10]
 Traffic (spatial information)
• Automatically detects incidents from video sequence
• Includes future view, present view, history view, temporal overview, and
additional windows
13
Visual Monitoring of Traffic Situations
[8] M. L. Pack, K.Wongsuphasawat, M. VanDaniker, and D. Filippova, “Icevisual analytics for transportation incident datasets,” in Proc. IEEE Int. Conf. Inf. Reuse Integr., 2009,pp. 200–205.
[9] A. Anwar, T. Nagel, and C. Ratti, “Traffic origins: A simple visualization technique to support traffic incident analysis,” in IEEE Pac. Vis. Symp., 2014,pp. 316–319.
[10] H. Piringer, M. Buchetics, and R. Benedik, “AlVis: Situation awareness in the surveillance of road tunnels,” in Proc. IEEE Conf. VisualAnal. Sci. Technol., 2012,pp. 153–162.

15. • T-Watcher [11]
 An interactive visual analytics system for monitoring and analyzing
complex traffic situations
 Region view, road view, and vehicle view
• The transportation incident management explorer (TIME) [12]
 A system that combines temporal and spatial data with incident logs
 Communications, variable message signs, responders, lane status, traffic
speed and traffic volumes
• Wang et al. presented a visual analytics system for traffic jams [13]
 Traffic jams are automatically detected by setting a threshold for road
speed
 Includes spatial view (traffic jam), road speed view(speed pattern), graph
list view, graph projection view (topological relationships), multifaceted
filter view (dynamic query tool)
14
Visual Monitoring of Traffic Situations
[11] J. Pu, S. Liu, Y. Ding, H. Qu, and L. Ni, “T-Watcher: A new visualanalytic system for effective traffic surveillance,” in Proc. IEEE 14th Int. Conf. MDM, 2013,vol. 1, pp. 127–136.
[12] M. VanDaniker, “Visualizing real-time and archived traffic incident data,” in Proc. IEEE Int. Conf. Inf. Reuse Integr., 2009, pp. 206–211.
[13] Z. Wang, M. Lu, X. Yuan, J. Zhang, and H. V. D. Wetering, “Visual traffic jam analysisbased on trajectory data,” IEEE Trans. Vis. Comput. Graphics, vol. 19, no. 12, pp. 2159–2168,Dec. 2013.

16. 15
Future Work
• In paper,
 Few works supporting visual analysis of big traffic data are available
 Analysis in situation-aware and immersive environments are promising
directions
 Designing and implementing systems using on-line and streaming data may be a
potential research direction
• Video surveillance
• Social Media
• In our research,
 Combining different views appropriately for the traffic analysis is the main part
• Integrating current situation on the map and the prediction for the possible congestion
• Analysis using charts
 Defining the number of variates