Leverage of Spiral Graph for Transportation System Data Visualization

Abstract

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.