Development of a New VisionEval Land Use Model and Applications to Evaluate Climate Strategies in Oregon

Details

• Creators:
  Wang, Liming ; Gregor, Brian
• Corporate Creators:
  Portland State University ; Oregon System Analytics, LLC (OSA)
• Corporate Contributors:
  Oregon. Department of Transportation. Research Section ; United States. Department of Transportation. Federal Highway Administration
• Subject/TRT Terms:
  • Land use models
  • Strategic materials
  • Performance based specifications
  • Transportation planning
  • Land use planning
  • Evaluation
  • Climate
  • Machine learning
  • Databases
  • Data collection
  More +
• Publication/ Report Number:
  FHWA-OR-RD-26-12
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States ; Oregon
• Edition:
  Final Report
• Corporate Publisher:
  Oregon. Department of Transportation. Research Section
• Abstract:
  This project upgraded the land use module for the VisionEval framework to enhance the evaluation of integrated transportation and land use policies and their impacts. The project developed a new strategic land use model utilizing NHTS and Smart Location Database data from 2011 and 2019, applying advanced statistical and machine learning methods to predict land use changes. Key innovations included land use transition model and allocation models for dwelling units and employment, along with D3 (Urban Design) and D4 (Level of Transit Service) classification methods. Project deliverables comprised a new VELandUse R package, a comprehensive data package with enriched variables, and pilot applications demonstrating the model's effectiveness. These enhancements significantly improved ODOT's capabilities for detailed land use modeling and regional strategy evaluation, while providing user-friendly tools for scenario creation and policy evaluation.
• Format:
  PDF
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/91147/dot_91147_DS1.pdf
• File Type:
  [PDF - 12.24 MB]
• Collection(s):
  Artificial Intelligence Deployment Center of Excellence (AI COE) ; US Transportation Collection
• Main Document Checksum:
  urn:sha-512:e30513ee39c2700dda042ca8cff14d25097cfa6bb6b660c5e110abe0e2ebec0feb18200622d44b03e34f36abc8cbc17bfa36e8759ec4034aaa621bfa23fc702e