DOI · DOI-0104 · record R2652

Improving recreation opportunities and access to public lands through machine learning and transportation planning

Department of the Interior · USGS

Overview

Development stage: In development or acquisitionSource: a) Pre-deployment – The use case is in a development or acquisition status.
High-impact designation: Not high-impactSource: c) Not high-impact
Impact justification: Not reported
Start date: Not reported
Withheld from public reporting: NoSource: a) No

Mission

Topic area: Transportation
Operational functions: No function tag matched Derived by keyword rules; see methodology

Problem the AI is intended to solve

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.

Expected benefits

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

System outputs

Temporal traffic estimates (2013-2023 minimally) across all state and federal roads, including recreational roads.

Technology

AI classification: Classical / predictive MLSource: Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relatio…
System name(s): Not reported
Custom-developed code: No
Public source code: Code will be published at the end of the project and currently resides as private on USGS GitLab.

Sourcing

How it was built: Not reported
Vendor (as reported): Not reported
Vendors (standardized): None on the standard list

A vendor is the supplier named by the agency. It does not identify the underlying model or AI technology.

Data and privacy

Involves PII: No
Privacy Impact Assessment: Not reported
Authorization to Operate: No
Demographic features: k) None of the above
Training and evaluation data: Not reported
Federal Data Catalog: Not reported

Governance

0 of 8 minimum-practice questions answered. A blank answer means the agency reported nothing; it does not mean the practice is absent.

Pre-deployment testingNo answer reported

AI impact assessmentNo answer reported

Independent reviewNo answer reported

Ongoing monitoringNo answer reported

Operator trainingNo answer reported

Fail-safeNo answer reported

Appeal processNo answer reported

User and public consultationNo answer reported

Potential impacts and how they were identified

Not reported