How can I use these data?
Data within the National Transportation Noise Map represent potential noise levels across the nation for an average annual day for the specified year. These data are intended to facilitate the tracking of trends in transportation-related noise by mode and collectively at a national scale over time, as additional maps are released. These maps can also be used to identify areas to focus additional research.
These maps are based on simplified noise modeling and cannot be used to evaluate noise levels in individual locations or at specific times and
should not be used for regulatory purposes.
Why was the 24-hr LAeq metric selected?
With the intention of the National Transportation Noise Map to provide a picture of yearly transportation noise, the 24-hr LAeq noise metric was selected because it represents a daily average level for the included transportation noise sources.
Additionally, the 24-hour LAeq metric is not currently used in United States noise regulations, which allows for additional distance between the intended use for these data (to track trends) from inappropriate usage of the data (these data cannot be used for regulatory purposes or compared to regulatory or environmental impact criteria).
How is the modeling that is done at a national scale different from detailed noise modeling done for local projects?
The modeling that is done as part of this effort is simplified so it can be applied at the national level. Characteristics such as ground type, roadway pavement type, and weather are not considered at a local level; they are held constant across all geographic areas. Shielding from terrain and other sources such as buildings are not included, which may result in over-predicted noise levels in areas near noise barriers, buildings, or areas with natural shielding features.
Assumptions that are associated with the noise modeling for each mode are detailed in the documentation.
How can these maps help refine the datasets that are used to produce these maps?
Since this effort requires consistent data across the nation, inconsistencies in the datasets may be identified when comparing datasets at the national scale. Agencies that are responsible for these datasets are notified of any inconsistencies that are discovered through this effort so datasets can be refined to be more consistent across the nation.
What are the major assumptions in the noise modeling for each mode?
Assumptions that are associated with the noise modeling for each mode are detailed in the documentation. (LINK)
Are helicopters included in the aviation noise modeling?
No, helicopters are not currently included in the aviation noise modeling.
Are military aircraft included in the aviation noise modeling?
Airports with exclusively military operations are excluded, however military operations at joint-use or commercial airports are included.
What vehicle types are included in the road noise modeling?
Automobiles, medium trucks, and heavy trucks are included in the road noise modeling.
What railway sources are included in the passenger rail noise modeling?
Commuter rail mainline, high-speed electric, light rail, heavy rail and streetcars*, along with commuter rail horns at highway-rail grade crossings, are included in the rail noise modeling.
*Note that rail systems that have streetcar components that are included in the dataset as light rail systems are included, but systems that are exclusively for streetcars are not currently included.
Are freight trains included in the rail noise modeling?
No, freight trains, along with activities associated with freight rail yards, are not currently included in the noise modeling.
How will I know if there are discrepancies in source data between years?
Discrepancies in data between years can look like significant changes in noise on the map; for example a change in coverage in available data could appear as an increase or a decrease in noise level. The documentation lists known data discrepancies (if any) between the modeled years so they are not misinterpreted as changes in noise level.