Advanced Behavioral Analysis of High Resolution Mobility Data

Details

• Creators:
  Zegras, Christopher
• Corporate Creators:
  Massachusetts Institute of Technology
• Corporate Contributors:
  New England University Transportation Center ; University Transportation Centers Program (U.S.) ; United States. Department of Transportation. University Transportation Centers (UTC) Program
• Subject/TRT Terms:
  • Behavior
  • Choice models
  • Logits
  • Multinomial logits
  • Econometric models
  • Neural networks
• Publication/ Report Number:
  MITR25-56
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Edition:
  Year 25 Final Report
• Corporate Publisher:
  New England University Transportation Center
• Abstract:
  This project is motivated by the possible value of integrating theory-based discrete choice models (DCMs) and data-driven neural networks. How to benefit from the strengths of both is the overarching question. We propose hybrid structures and strategies to flexibly represent taste heterogeneity and improve predictability while keeping model interpretability. Also, we utilize neural networks’ training machinery to speed up and scale up the estimation of Latent Class Choice Models (LCCMs).
• Format:
  PDF
• Funding:
  DTRT13-G-UTC31
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/43828/dot_43828_DS1.pdf
• File Type:
  [PDF - 280.88 KB]
• Collection(s):
  University Transportation Centers ; US Transportation Collection
• Main Document Checksum:
  urn:sha256:75ee9547485b899f58ab4f27d719d4807b720667a5fb70116491097ee8efe542