01154612

Component

https://doi.org/10.3141/2165-09

http://scholar.google.com...iker&publication_year=2010

Many transportation data sets are saturated with temporal information. Typical examples include data sets concerned with system monitoring, travel time, incident management, and many other temporally aligned features of intelligent transportation systems. Because time is a linear entity, transportation researchers typically plot their temporal data into visualizations that use techniques tailored to linear data sets, such as tables, line charts, and scatter plots. The patterns that temporal data exhibit over time are often more interesting than the linearity of the data, but conventional visualizations often fail to convey them effectively. The spiral graph is a data visualization technique that treats such patterns—and their deviations—as first-class citizens, by allowing for the efficient recognition of the regular cycles in the data. The spiral graph renders data along a temporal axis, which spirals outward at regular intervals. Individual data points are rendered as bands along the axis, creating visual clusters among datum that contribute to patterns. This paper introduces the spiral graph to the transportation community through a series of practical applications and demonstrates best practices to enable researchers to garner more information from their temporal data sets.

01220491

10-2706

English

pp 79-88

2010

Transportation Research Record: Journal of the Transportation Research Board

9780309142977

Print

Figures (8) ; References (17)

Best practices; Data displays; Data files; Intelligent transportation systems; Time intervals; Visualization

Spiral graphs; Temporal data; Transportation data

Data and Information Technology; Highways; Planning and Forecasting; I72: Traffic and Transport Planning

TRIS, TRB, ATRI

Jan 25 2010 11:19AM

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• Gaussian Processes for Short-Term Traffic Volume Forecasting
• Getis–Ord Spatial Statistics to Identify Hot Spots by Using Incident Management Data
• Integration of Traffic Simulation into Design Visualization: Case Study
• Investigating Regression to the Mean in Before-and-After Speed Data Analysis
• Modeling Injury Severity of Multiple Occupants of Vehicles: Copula-Based Multivariate Approach
• Multiple Applications of Multivariate Adaptive Regression Splines Technique to Predict Rear-End Crashes at Unsignalized Intersections
• Spatial Correlation in Multilevel Crash Frequency Models: Effects of Different Neighboring Structures