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Truck platooning in the U.S. national road network: A system-level modeling approach

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  • Noruzoliaee, Mohamadhossein
  • Zou, Bo
  • Zhou, Yan (Joann)

Abstract

Truck platooning enables a group of trucks to move close together, which helps reduce truck fuel use and increase effective road capacity. In this paper, a system-level equilibrium model is developed to characterize spontaneous truck platooning with coexistence of non-platooning vehicles in a network, by explicitly accounting for the interlocking relationship among platoon formation time, truck fuel saving, and increase in effective road capacity. To equilibrate the relationships, an algorithm is proposed which involves a diagonalization approach and a bush-based algorithm to solve decomposed subproblems. The condition of proportionality is imposed to obtain unique traffic flows for each class of vehicles on road links. In addition, a spatially constrained multivariate clustering technique is employed to construct origin/destination zones that are smaller than the coarse Freight Analysis Framework (FAF) zones, while maintaining reasonable computational burden for network traffic assignment. Model implementation in the U.S. shows that platooning could lead to 7.9% fuel saving among platoonable trucks in 2025 and a comparable increase in effective capacity of platoonable road links, which would account for 60% of rural interstate roads. The fuel saving and road capacity improvement translate into an annual cost reduction of $868 million for the U.S. intercity trucking sector and reduced road infrastructure investment needs worth $4.8 billion. Extensive sensitivity analysis further reveals that fuel saving of platoonable trucks increases with platoon size but decreases with inter-truck distance in a platoon. Fuel saving potential suggests that priority should be given to rural rather than urban roads in deploying platooning technologies. As expected, greater market penetration of platooning technologies means higher fuel saving and greater increase in effective road capacity.

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  • Noruzoliaee, Mohamadhossein & Zou, Bo & Zhou, Yan (Joann), 2021. "Truck platooning in the U.S. national road network: A system-level modeling approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 145(C).
  • Handle: RePEc:eee:transe:v:145:y:2021:i:c:s1366554520308425
    DOI: 10.1016/j.tre.2020.102200
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    Cited by:

    1. Liu, Zhiyuan & Zhang, Honggang & Zhang, Kai & Zhou, Zihan, 2023. "Integrating alternating direction method of multipliers and bush for solving the traffic assignment problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 177(C).
    2. Huailei Cheng & Yuhong Wang & Dan Chong & Chao Xia & Lijun Sun & Jenny Liu & Kun Gao & Ruikang Yang & Tian Jin, 2023. "Truck platooning reshapes greenhouse gas emissions of the integrated vehicle-road infrastructure system," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    3. Chen, Shukai & Wang, Hua & Meng, Qiang, 2021. "Autonomous truck scheduling for container transshipment between two seaport terminals considering platooning and speed optimization," Transportation Research Part B: Methodological, Elsevier, vol. 154(C), pages 289-315.
    4. Yifeng Han & Tomoya Kawasaki & Shinya Hanaoka, 2022. "The Benefits of Truck Platooning with an Increasing Market Penetration: A Case Study in Japan," Sustainability, MDPI, vol. 14(15), pages 1-15, July.
    5. Li, Qianwen & Li, Xiaopeng, 2022. "Trajectory planning for autonomous modular vehicle docking and autonomous vehicle platooning operations," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 166(C).
    6. Barua, Limon & Zou, Bo & Choobchian, Pooria, 2023. "Maximizing truck platooning participation with preferences," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 179(C).

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