Maximizing Truck Platooning Participation with Preferences, Inclusion, and Privacy Preservation

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This project will create novel approaches to maximizing participation of trucks in platooning, which refers to trucks traveling in groups with small inter-truck separation to reduce aerodynamic drag. The trucking sector has been heavily investing in platooning technologies in recent years, driven primarily by the significant benefits of truck energy use reduction. This project will investigate the fundamental issue of promoting widespread truck platooning while accounting for 1) individual truck preferences, given the high fragmentation of the US trucking sector; 2) inclusion of trucks of different origins, destinations, and planned departure time in forming platoons; and 3) preservation of truck information privacy. Results from the research, which will be produced in collaboration with the industry, will help inform truck platooning implementation in the US toward reaping the maximum benefits. The research also aligns with the growing societal emphasis on individual member well-being, inclusiveness, privacy, and cybersecurity. Diverse student groups will be exposed to the subject of truck platooning and technology-empowered smart mobility in general through systematic education and outreach efforts. The goal of this project is to establish a theoretical foundation for maximizing truck platooning participation taking into consideration truck preferences, inclusion, and privacy preservation. An interactive process between trucks and a platooning platform is first devised to facilitate construction of the preference list of platooning partners for each truck. The research then investigates two approaches for platooning participation maximization: one centers on stably partitioning trucks into parties of cycled preferences; the other resorts to integer programming with exploration of half-integrality of the extreme points for efficient solution methods. Multiple inclusion measures are further developed and integrated into the platooning participation maximization. To preserve privacy while seeking platooning opportunities, an encrypted computing design is conceived and examined for constructing truck preference lists. The core of the design is a two-cloud architecture with secret key splitting, ciphertext re-encryption, and obfuscation which leverages the additive homomorphic property of the chosen cryptosystem. Disaggregate truck flows based on real-world freight data will be constructed and used to test and evaluate the different research components at varying geographical scales. The results from this project will not only contribute to enriching the truck platooning literature but lend insights to several other emerging mobility systems that bear operational similarities.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.