Understanding and Modeling Middle-Mile Logistics Automation [supporting datasets]

Details

• Creators:
  Zou, Bo ; Kawamura, Kazuya ; Sriraj, P.S. ; Qiu, Ziyu ; Lauster, Eamon ; Jin, Mingzhou
• Corporate Creators:
  Center for Freight Transportation for Efficient and Resilient Supply Chain (FERSC) Tier 1 University Transportation Center (UTC)
• Corporate Contributors:
  Illinois. Department of Transportation ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology ; United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Federal Highway Administration
• Subject/TRT Terms:
  Automation Data Files Drayage Interviewing Literature Reviews Logistics Optimization Resilience (Materials) Surveys System Design Trucks

  More +
• DOI:
  https://doi.org/10.5281/zenodo.13835810
• Resource Type:
  Dataset
• Right Statement:
  Open Access; https://creativecommons.org/licenses/by/4.0/
• Geographical Coverage:
  United States
• Corporate Publisher:
  Center for Freight Transportation for Efficient & Resilient Supply Chain
• Abstract:
  Middle-mile logistics, particularly drayage which deals with short-distance movements between major transportation facilities in proximity, presents a critical component in the national supply chain. Despite its short distances, drayage incurs a disproportionately large share in the overall shipping cost. The emergence of vehicle automation offers exciting opportunities to improve the efficiency, resiliency, and sustainability of drayage operations, but has received limited attention. To fill this gap, this research adopts a mix of qualitative investigation and quantitative modeling approaches to study the prospects of truck automation in drayage operations. On the qualitative side, we combine harvesting the information from literature and interviewing stakeholders in the field, to understand the challenges and future development process of vehicle automation for drayage operations. On the quantitative side, a mathematical model is developed to seek optimal container and truck flows that minimizes system total cost under varying fleet composition scenarios. We find that vehicle automation would bring significant benefits to drayage operations including timelier movements of containers. The optimal fleet size could be much larger with autonomous trucks. On the other hand, an autonomous driving technology to be adopted by middle-mile freight must demonstrate significant capabilities in terms of safety standard, cost competitiveness, meeting real-world needs, public acceptance, and operation under a proper regulatory environment. A thorough process of testing, incremental deployment, campaigns for public acceptance of ADS on the roads, and the avoidance of over promising will be needed to gain acceptance and support of the stakeholder groups as well as the public while deploying vehicle automation in drayage operations.
• Content Notes:
  This item is made available under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) license https://creativecommons.org/licenses/by/4.0/. Use the following citation: Qiu, Z. (2024). Understanding and Modeling Middle-Mile Logistics Automation [Data set]. Zenodo. https://doi.org/10.5281/zenodo.13835810
• Format:
  ZIP
• Funding:
  69A3552348338
• Collection(s):
  University Transportation Centers
• Main Document Checksum:
  urn:sha-512:4a5d1d0159bd5c309b2d457acc36ccd9590ed069fefbb8de065045264caf5e31c0fe87bff1534492e0cdbaf63f2fe903ee4ce4ba461aaf36f607a44f5b823a4b
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/87437/dot_87437_DS1.zip
• File Type:
  [ZIP - 25.86 KB ]