Visualizations are tools to summarize an extensive amount of data into a more easily comprehensible set of information. This data can come from multiple sources. Data may be directly collected by the MPO utilizing in-house staff and resources or temporary personnel, or through consultants. For instance, many MPOs conduct travel time studies, traffic counts, or intersection studies. Data may also be gathered by the MPO from external, secondary sources that collect data for a different main purpose. For instance, data may include transit operations data from transit providers or archived ITS data from traffic flow detectors used by transportation operations organizations. Some of these sources may actually be more useful, easier to gather, more detailed, or less costly than directly-collected data would be.
It is also important to note another distinction in the information used in creating visualizations. Most MPOs use observed data while some also use simulated or forecasted information. Observed data may not always be available for the desired analysis but there may be similar or related types of information from the travel demand modeling system, which can lead an MPO to rely on modeled information instead of observed data. When the CMP is dealing with future scenarios, modeling of information is necessary. When dealing with recent or past conditions, observed data is often more accepted by decision makers. Many of the visualization tools can be used either with observed data or modeled information, but it is important to be aware that the public or decision-makers may mistake modeled data for visualizations of "observed" data.
At many MPOs the collection and gathering of data lies at the heart of CMP activities. These data activities take many forms, ranging from the manual collection of speed data through travel-time runs to the gathering of vast data repositories available through Intelligent Transportation System (ITS) and Advanced Transportation Management System (ATMS) activities. In many cases, data is gathered from existing data sources at partner agencies, such as crash databases from the state police or state DOT. As noted above, MPOs also tend to use results from modeling simulations as part of the CMP, due in part to the fact that historically, the communication of the results of forecasts of future congestion conditions has been a major part of the activities of MPOs. There can sometimes be a very large amount of such modeled information, depending on the complexity of the model and the number of scenarios tested using the model. One major challenge for MPOs is how to organize, interpret, and use the large volumes of data and processed information that are available for use. Visualization can be an effective tool for doing this, and then for presenting the most pertinent information to the public. This section focuses on the different data types that are used in CMP visualizations.
There are many types of data that can be used in visualizations as part of the CMP process. Data types are often differentiated or categorized according to the source or underlying nature of the data. The following list is not exhaustive, but includes several common types of data that are used in CMP-related visualizations by MPOs.
Volume-based data on the quantity of system use: Volume, expressed either as Annual Average Weekday Daily Traffic (AAWDT) or Vehicle-Miles-of-Travel (VMT), is a widely-available dataset at most MPOs around the nation. Volume per se is not a true measure of congestion; rather it is a measure of the quantity of use, while congestion is a measure of the quality of use of the system. Nevertheless, volume can, in certain circumstances, be an effective proxy measure of congestion. This is the type of data typically used in summary analyses of regional congestion. Some MPOs also collect detailed traffic counts—including such items as vehicle classification, time-of-day (typically in 15 minute increments), and turning movements—as part of detailed CMP analyses at problem locations.
Aerial photography data: Aerial photographs can be used as a source of data showing the number and density of vehicles along a corridor at any given time. When conducted iteratively, the photographs can provide data on average conditions in the corridor. This information is typically used for corridor-level analyses of recurring congestion. For freeway uninterrupted flow conditions, the density of traffic (the number of vehicles per mile per lane) is the preferred measure of congestion in accordance with the Highway Capacity and Quality of Service Manual. Several MPOs contract with private sector vendors to periodically carry out such corridor sampling, as well as to prepare region-wide summaries that aggregate the observed data from the individual corridors.
Speed and travel time data: Travel time and speed samples are conducted by many MPOs as part of the CMP process to directly observe congested conditions. These are generally conducted using GPS technology to measure link-speeds rather than spot speeds as spot speeds have considerable variation, particularly on arterials. The use of GPS technology also allows easy transfer of the data into a Geographic Information System (GIS) program. Such speed data studies can also be conducted using on-board diagnostics programs (necessary in some locations with poor GPS reception) or manually using a stopwatch and tape recorder and/or pencil and paper. This information is typically used for corridor-level analyses of recurring congestion. In addition, some MPOs prepare region-wide summaries that aggregate the observed data from the individual corridors.
Archived ITS data: Various operations-related data can be gathered and obtained from Intelligent Transportation Systems (ITS), such as information on combinations of spot-speeds, volumes, and percent lane-occupancy. The particular information available depends on the detector technology and installation set-up. Estimates of vehicle lengths and lane-occupancy can be used to calculate average spot-speed for short-duration time intervals, sometimes as short as every 20 to 30 seconds. Probe-based data from monitoring the flow of electronic toll-tags are also used in some MPO areas. In addition, data on incident response and the impact of such non-recurring congestion is often used as a source of data on travel time reliability.
ITS data is typically only available on a subset of roads within an area, usually a portion of the freeway network. Some arterial-based data is beginning to become available. There are several private-sector companies that have recently begun using various types of probe vehicles to estimate link travel times and speeds every two to three minutes in many metropolitan areas. These companies are marketing that data source to governmental agencies; some MPOs are beginning to experiment with the use of that type of data to visualize congestion patterns, including variability and reliability. These purchased data sources are also beginning to provide much more widespread coverage of the freeway system as well as major and some minor arterial roads
High-Occupancy Vehicle (HOV)/High-Occupancy Toll (HOT) lane data: Some HOV and HOT networks, especially on HOT lanes that use variable congestion pricing, have data on lane usage and the peak-period congestion characteristics of the facility. The data may be gathered from ITS flow detectors or directly collected by a MPO from their own sampling of traffic on those facilities.
Transit data: A wide range of transit data may be available from transit agencies, including boarding and alighting statistics, total ridership, on-time performance, and transit vehicle capacity. Archived Automatic Vehicle Location (AVL) data can be especially useful for examining the impact congestion has on on-time performance. Several MPOs also track the usage of park-and-ride facilities as part of the CMP.
Bicycle/pedestrian data: Many MPOs collect data on the location and condition of bicycle/pedestrian facilities, such as sidewalks, bicycle lanes, and off-road paths. Some MPOs are also beginning to collect data on bicycle and pedestrian facility usage, through both manual and automated count methods.
Crash data: Many MPOs use crash data as a method of determining locations where non-recurring congestion due to incidents is more likely to occur. The data is typically provided by the state police or state DOT, but is sometimes also from local sources. Displays and tabular and chart summaries of such data can be a useful supplement to the congestion-based displays.
Data types are often differentiated or categorized according to the underlying nature of the data. One such important aspect of each of these data types to make note of is the distinction between spatial data (what happens at a given location) and temporal data (what happens at a given time or over a period of time). It is generally understood that congestion of component parts of our transportation systems is often very site- or "spatial-specific" as well as time- or "temporal-specific". Visualization can be an effective means of integrating spatially- and temporally-based data features into a single more understandable display of congestion.
Micro-simulation models used by some MPOs in their analyses of congestion conditions can generate animated simulations that represent the spatial-temporal extent of the build-up and then the dispersion of congestion over an area or along a corridor, as well as the concurrent duration of the congested conditions over time. Static displays, such as time-distance congestion contour displays based upon observed data have long been used in operational planning and are beginning to be used by MPOs in CMP activities.