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final report

Traffic Congestion and Reliability: Linking Solutions to Problems

A. Data Sources

Data Sources Used in This Report

This report draws on several current efforts to produce a composite of the national congestion picture. These include the following efforts.

The Urban Mobility Study (UMS) has been in existence since 1982 and is sponsored by a consortium of state DOTs and private interest groups.1 The study is conducted by the Texas Transportation Institute. The UMS tracks congestion patterns in the 75 of the largest metropolitan areas and has been instrumental as both a source of trend information and development of the concepts and metrics for congestion monitoring, for example, the widely used Travel Time Index is an innovation of the UMS. The UMS relies on the Highway Performance Monitoring System (HPMS) as it source of information. It uses the average annual daily traffic (AADT) and number of lanes data in HPMS as a basis for its estimates; these are then translated into congestion metrics using predictive equations that have been developed and tested specifically for the UMS. Beginning in 2002, the UMS is also considering the positive effects that operational strategies have on system performance; these are accounted for as adjustments to the base performance predicted by AADT and number of lanes. The UMS has widespread visibility both within the transportation profession as well as with the general public; annual release of the UMS report generates a significant amount of media coverage.

The Urban Congestion Report (UCR) sponsored by FHWA is an effort that yields a monthly snapshot of roadway congestion in 10 urban areas and three national composite measures. UCR utilizes efficient, automated data collection procedures (colloquially known as "screen scraping" or "web mining") to obtain travel time directly from traveler information web sites and archives them at five-minute intervals on the weekdays when these services are available. Since a monthly report can be rapidly constructed (within 10 working days) UCR serves as an early warning system for changes in urban roadway congestion. Concurrent with the travel time data collection, other UCR acquisition programs obtain web-based data on weather conditions, traffic incidents, and work zone activity. This allows the UCR monthly report to include not only congestion level, but a range of possible contributing factors. A one-page overview tells the congestion story each month in a graphical manner for the analyst or administrator wanting a timely composite overview of congestion trends on a month-to-month basis.

The Mobility Monitoring Program (MMP) sponsored by FHWA calculates system performance metrics based on data archived at traffic management centers (TMCs).2 These data are highly detailed measurements from roadway surveillance equipment installed for operational purposes; data from spot locations (volumes and speeds) are used as well as travel time estimates from probe vehicles (where available). For each participating city, the MMP develops congestion metrics at both the corridor and area levels; 23 cities participated in 2002 and close to 30 will be analyzed for calendar year 2003. Early work from this project has provided a basis for measuring travel time reliability; the Buffer Index used by the UCR and several state efforts was first defined by the MMP, but was based on a concept identified precursor studies to the UCR effort. Beginning with 2002, traffic incident data is being collected from TMCs where these data exist. Also, continuous traffic data from signalized highways is being explored as a potential source for system performance monitoring. The concepts, performance measures, and data analysis techniques developed and used in the MMP are being considered for adoption and implementation by several state and local agencies. A few of these agencies have contacted the project team to request technical assistance or additional detailed information on performance monitoring or operations data archiving. Specifically, one of the two primary objectives of the Mobility Monitoring Program was to provide incentives and technical assistance for the implementation of data archiving systems to support performance monitoring. Several examples of these technology transfer and implementation activities are:

The Intelligent Transportation Infrastructure Program (ITIP) is an ongoing program designed to enhance regional surveillance and traffic management capabilities in up to 21 metropolitan areas while developing an ability to measure operating performance and expanding traveler information through a public/private partnership involving the FHWA, participating State and local transportation agencies, and Mobility Technologies. Under this partnership, Mobility Technologies is responsible for deploying and maintaining traffic surveillance devices, and integrating data from these devices with existing traffic data to provide a source of consolidated real-time and archived data for the participating metropolitan areas. Deployment has been completed in Philadelphia, Pittsburgh, Chicago, and Providence, and is under way in Boston, Tampa, San Diego, the Washington D.C. region, Phoenix, Los Angeles and San Francisco. Negotiations are currently active in 10 additional cities.

Part of ITIP is the production of performance measures on a routine basis. The metrics used to report performance are based on those in the Mobility Monitoring Program: annual person-hours of delay, percent congested travel, travel rate index, and buffer index. Performance measure reports are to be provided to the U.S. DOT on a monthly and annual basis. The monthly reports for each metropolitan area will be based on monthly data and will be presented with similar content, and in a format, consistent with the "city summary reports" that are part of the Mobility Monitoring Program.

The Freight Analysis Framework (FAF) sponsored by FHWA is a tool set developed to estimate trade flows on the Nation's infrastructure, seeking to understand the geographic relationships between local flows and the Nation's overall transportation system. The framework will help identify areas of improvement to increase freight mobility, including highlighting regions with mismatched freight demand and system capacity, and encouraging the development of multistate and regional approaches to improving operations.

The FAF examines transportation for four key intermodal modes: highway, railroad, water, and air. A comprehensive database for different modes was developed from various government and private sector databases. To evaluate the effect of anticipated volumes on the network, the FAF includes economic forecasts for the years 2010 and 2020, assigned to the network and linked to transportation infrastructure databases. Current work in the FAF concentrates of truck flows on the highway system, and this is the information borrowed for this Report.

The Travel Times in Freight Significant Corridors project undertaken by FHWA develops truck travel times and other performance measures in major intercity corridors that are heavily used by trucks. The study is prototyping the measurement of travel times using satellite tracking of selected trucks. Truck travel times indicate how well the intercity highway network is performing for all travelers. Data from this study can also be used to calibrate network assignment models and to understand the level of truck activity by time-of-day.

The American Highway Users Alliance (AHUA) National Bottleneck Study was a privately sponsored effort to identify the worst traffic bottlenecks in the country and to estimate the benefits of improving them.3 The study surveyed state DOTs to identify their worst bottlenecks, then applied modeling methods used by FHWA in the Highway Economic Requirements System model to estimate delay, safety impacts, fuel consumption, and emissions. The study also examined the effect of improving the bottlenecks, using actual improvement plans where available.

Additional Data Sources

Other data sources besides those mentioned above can be used to monitor and measure congestion. The above sources are focused on measuring congestion conditions on highways. Another way to approach congestion measurement is to track how users experience entire trips, from their origin to their destination. This has been traditionally done through the use of surveys, but emerging technologies may allow trip-tracking on a real-time basis.

At the national level, the National Household Travel Survey (NHTS) provides a long history for trends in congestion. The NHTS is the nation's inventory of daily and long-distance travel. The survey includes demographic characteristics of households, people, vehicles, and detailed information on daily and longer-distance travel for all purposes by all modes. NHTS survey data are collected from a sample of U.S. households and expanded to provide national estimates of trips and miles by travel mode, trip purpose, and a host of household attributes.

The daily travel surveys were conducted in 1969, 1977, 1983, 1990, and 1995. This data series provides a rich source of detailed information on personal travel patterns in the U.S. Longer-distance travel was collected in 1977 and 1995. The 2001 NHTS collects both daily and longer-distance trips in one survey.




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