Transportation is about movement. Each movement of people or goods has a starting and ending point and follows a route. The size of such movements, as well as the potential demands for future travel activity are usually represented as origin-to-destination (O-D) movements, or flows, between pairs of places, while the supply of transportation facilities and services is represented by nodes and links in a multimodal transportation network that reflects the physical connectivity between these places. Links in this representation may be sections of highway, rail lines, waterways, pipelines, or bicycle, pedestrian, or air routes, while nodes may be highway intersections, airports and railway stations, intermodal terminals, or other locations where links terminate or converge. Together, these links and nodes constitute a transportation network over which people and goods travel.
This necessarily spatial view of the transportation system can be viewed at a number of different levels of resolution: from intercity flows for which metropolitan areas are the nodes of interest and for which annual trade or personal travel volumes represent the O-D flows of interest; to vehicle, pedestrian, and cyclist movements in localized geographic areas in which local streets and bike paths are the links, and street intersections are the nodes. The spatial representation of transportation movements also presents some unique challenges for data collection and analysis. This is especially the case when trying to combine data collected at different regional scales as well as at different levels of spatial resolution.
Transportation data are collected for many purposes, including operational needs, project evaluation, local and regional planning, national planning and policy formulation, and performance measurement. Some of these data are within the purview of federal entities, but the separation between federal, state, and local roles is not always clear. Aggregating local data to get a complete national picture is impossible if data are not gathered in all relevant jurisdictions or if they are collected in different ways by different agencies. For example, while local truck movements are best collected at the local or metropolitan area level, data on interregional or "through" truck movements are more easily collected at the federal or statewide level. Individual metropolitan areas encounter problems in gathering such data, because all origins and destinations will not be within their jurisdiction.
Data gathered by the federal government, however, often require sampling to obtain cost-effective national or regional statistics. As a result, these data sometimes lack the detailed geographic specificity or complete within-region coverage needed by local planners and policymakers. This is particularly true for O-D-specific, long-distance highway traffic, including long-haul truck traffic. Sampling has an important role to play here in obtaining representative data at reasonable cost.
There is also a temporal dimension to be considered in this spatial data collection. Not only average freight and passenger flows but also the day-to-day and season-to-season variability in these volume measures need to be known if the level of service being provided by the transportation network is to be properly understood. The same is true for the travel costs associated with these movements, with day-to-day reliability in transit times on congested parts of today's transportation network playing an important role in both the selection of routes and the determination of O-D-specific travel costs.