Decision makers in state transportation agencies typically manage budgets approaching or exceeding $1 billion. Historically, the data used to make investment decisions have been quite coarse and have been typically based on short-term volume counts fed into models to forecast performance. As a result, it is not uncommon for construction projects to address needs that were forecast to be a priority 5 to 10 years earlier, while more pressing congestion challenges go unmet. It is essential that long-term planning begins to be supplemented by more current performance measures. The emerging private-sector probe vehicle data obtained from commercial providers offer an opportunity to augment traditional forward-looking planning models with performance measures that reflect the conditions motorists are experiencing today. This paper proposes scalable, analytical probe data reduction techniques to create technically sound, yet visually intuitive, system-performance measures of current freeway conditions. These types of performance measure are increasingly used by high-level agency management to identify locations at which customers experience congestion, and the magnitude of that congestion, and to compare congestion on various highway corridors. These proposed performance measures can be used for policy-oriented decisions, such as the prioritization of capital program investments, the management of snow removal, and the scheduling of lane closures. The techniques are applied to seven Indiana Interstate highways, comprised of 1,886 directional miles. The Interstates span rural and urban sections that experience varying levels of recurring and non-recurring congestion as a result of winter weather and construction activity. Specific examples adjacent to the Indianapolis, Indiana; Louisville, Kentucky; and Chicago, Illinois, metropolitan areas are presented, along with the 10 most congested Interstate segments.
