No abstract

Transportation systems are being monitored at an unprecedented scope, which is resulting in tremendously detailed traffic and incident databases. Although the transportation community emphasizes developing standards for storing these incident data, little effort has been made to design appropriate visual analytics tools to explore the data, extract meaningful knowledge, and represent results. Analyzing these large multivariate geospatial data sets is a nontrivial task. A novel, web-based, visual analytics tool called Fervor is proposed as an application that affords sophisticated, yet user-friendly, analysis of transportation incident data sets. Interactive maps, histograms, two-dimensional plots, and parallel coordinates plots are four featured visualizations that are integrated to allow users to interact simultaneously with and see relationships among multiple visualizations. Using a rich set of filters, users can create custom conditions to filter data and focus on a smaller data set. However, because of the multivariate nature of the data, finding interesting relationships can be a time-consuming task. Therefore, a rank-by-feature framework has been adopted and further expanded to quantify the strength of relationships among the different fields describing the data. In this paper, transportation incident data collected by the Maryland State Highway Administration's CHART program are used; however, the tool can be easily modified to accept other transportation data sets.

The sheer volume of data associated with a single transportation incident is difficult to fully comprehend, and existing incident management systems often fail to equip users with the necessary tools to make sense of this data. The consequences of delayed decision making, misinterpreting data, or even overlooking pertinent information can be life threatening. A tool that gives the incident manager the ability to grasp the entire picture of an incident in a minimal amount of time is essential to facilitate quick and accurate decision making.The Transportation Incident Management Explorer (TIME) provides for the visualization of real-time and historic traffic management center incident data. All data pertinent to an incident can be viewed in a compact graphical overview. Temporal data are rendered as timelines where color and length convey information that would otherwise take significantly more space to display and more time to understand. The mouse can be used to scroll over elements in the timeline to glean additional information. Geospatial data are plotted on an interactive map, providing users freedom to explore regions affected by an incident. TIME reduces the chance of missing critical information, enables users to correlate events, and quickens the time needed to comprehend the many simultaneous events that occur or occurred during the management of an incident. This paper describes the use of the TIME tool when visualizing real-time and archived incident data collected and provided by the Maryland CHART, Washington, D.C., and Northern Virginia Traffic Management Centers.

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.