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Research Brief: Automation, Electrification, and Shared Mobility in Freight

Author

Listed:
  • Jaller, Miguel
  • Fulton, Lewis
  • Otero, Carlos
  • Pourrahmani, Elham

Abstract

This study aims to provide an overview of the different technologies of electrification, automation and shared mobility (3Rs) in freight transportation for the long-haul and last mile. The study provides a view of their penetration status and explores the potential, challenges, and unintended consequences of their arrival in the transportation system. An overview is also provided of the required changes and the driving forces needed to foster the 3Rs in freight. The results can help inform the development of the next phases and future steps, plans, and policies or initiatives needed for the next generation of the freight transportation system, especially in urban areas. This research brief summarizes those findings.

Suggested Citation

  • Jaller, Miguel & Fulton, Lewis & Otero, Carlos & Pourrahmani, Elham, 2020. "Research Brief: Automation, Electrification, and Shared Mobility in Freight," Institute of Transportation Studies, Working Paper Series qt17v510z7, Institute of Transportation Studies, UC Davis.
    • Handle: RePEc:cdl:itsdav:qt17v510z7
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    Citations

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    Cited by:

    1. Tengilimoglu, Oguz & Carsten, Oliver & Wadud, Zia, 2023. "Infrastructure requirements for the safe operation of automated vehicles: Opinions from experts and stakeholders," Transport Policy, Elsevier, vol. 133(C), pages 209-222.
    2. Ostermeier, Manuel & Heimfarth, Andreas & Hübner, Alexander, 2023. "The multi-vehicle truck-and-robot routing problem for last-mile delivery," European Journal of Operational Research, Elsevier, vol. 310(2), pages 680-697.
    3. Pourrahmani, Elham & Jaller, Miguel, 2021. "Crowdshipping in last mile deliveries: Operational challenges and research opportunities," Socio-Economic Planning Sciences, Elsevier, vol. 78(C).
    4. Raj Bridgelall & Ryan Jones & Denver Tolliver, 2023. "Ranking Opportunities for Autonomous Trucks Using Data Mining and GIS," Geographies, MDPI, vol. 3(4), pages 1-18, December.

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