Improving recreation opportunities and access to public lands through machine learning and transportation planning
Description
Increasing access to public lands for recreation starts with effective transportation planning. However, most states lack comprehensive transportation datasets, including traffic volume estimates on roads accessing public lands.
Detailed example
Temporal traffic estimates (2013-2023 minimally) across all state and federal roads, including recreational roads.
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
a) Pre-deployment – The use case is in a development or acquisition status.
Expected benefit
Temporal traffic estimates across all roads with Colorado will improve how FHWA allocates money and addresses needs for increasing access to public lands. These data will also improve CDOT and other federal agencies in their transplantation planning needs
Controls / human review
ATO: No; PIA: Not published