Measuring the Last-Mile: A Comprehensive Evaluation of Synthesis Approaches to Address Data Gaps for Local Freight Decision-Making (Phase 1)

Details

• Creators:
  Conway, Alison ; Grossberg, Michael ; Godse, Shradha Balasaheb ; Fardin, Fateme Rezapour
• Corporate Creators:
  City College of New York
• Corporate Contributors:
  National Center for Understanding Future Travel Behavior and Demand (TBD) ; United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology
• Subject/TRT Terms:
  Artificial Intelligence Case Studies Datasets Decision Making Evaluation First Mile And Last Mile Freight Handling Freight Transportation Local Transportation Machine Learning Mathematical Models Performance Quantitative Metrics

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• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Edition:
  Final Report: 2023-2025
• Corporate Publisher:
  United States. Department of Transportation. University Transportation Centers (UTC) Program
• Abstract:
  The purpose of this study is to comprehensively review the current landscape of freight data available for local agency decision-making, to identify existing data gaps and limitations that inhibit data-driven decision-making, and to identify relevant machine learning, mathematical modeling, and generative AI approaches to address these gaps. Specific project tasks included: a comprehensive review of both academic and practical freight data literature to identify commonly used freight data sources, common applications of freight data for agency decision-making, and specific performance metrics needed for different decision types; 2) a detailed case study of the New York City Department of Transportation – including a stakeholder workshop and a systematic review of seven agency and commercial data sources; 3) mapping of performance metrics vs. specific local agency data needs and vs. available data sources; 4) identification of critical data gaps and limitations; and 5) comprehensive review of applications of machine learning, mathematical modeling, and advanced generative modeling techniques to identify promising approaches to address persistent data gaps. Key outputs from this analysis include a comprehensive summary of freight data sources and their potential applications; evaluation matrices mapping detailed performance metrics to specific local freight-related decisions and mapping detailed performance metrics obtainable from existing data sources; and recommended approaches for freight data synthesis to address remaining gaps.
• Format:
  PDF
• Funding:
  69A3552344815 ; 69A3552348320
• Collection(s):
  University Transportation Centers
• Main Document Checksum:
  urn:sha-512:2c1e29f22f4e29cf2491dc8be0f9ed2e6daa7cc93062745bc376b7f56e9d17fbcfaa6e1c46b0ee6c3194a6318f03f8dfc7d60085c7549e196b4264ec0f3bcc22
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/91975/dot_91975_DS1.pdf
• File Type:
  [PDF - 2.28 MB ]