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

该项目将创造新的方法，最大限度地提高卡车在队列中的参与度，即卡车以较小的间隔成组行驶，以减少空气动力学阻力。近年来，卡车运输行业一直在大力投资车队技术，主要是由于卡车节能的显著优势。该项目将研究促进卡车车队广泛分布的基本问题，同时考虑到1)考虑到美国卡车运输业的高度分散，个人卡车偏好；2)将不同出发地、目的地和计划出发时间的卡车纳入队列；3)货车信息隐私的保护。该研究结果将与业界合作产生，将有助于为美国卡车车队的实施提供信息，以获得最大的效益。这项研究也与社会对个人福利、包容性、隐私和网络安全的日益重视相一致。通过系统的教育和推广工作，不同的学生群体将接触到卡车队列和技术驱动的智能移动的主题。该项目的目标是在考虑卡车偏好、包容性和隐私保护的情况下，为最大限度地提高卡车队列的参与度建立理论基础。首先设计了卡车与队列平台的交互流程，便于构建每辆卡车的队列伙伴偏好列表。然后，研究了车队参与最大化的两种方法：一种是将卡车稳定地划分为循环偏好的各方；另一种方法是利用整数规划，探索极值点的半整性，以获得有效的解方法。进一步开发了多种包容措施，并将其融入到队列参与最大化中。为了在寻找队列机会的同时保护隐私，设计了一种加密计算设计，用于构建卡车偏好列表。该设计的核心是一个具有密钥分割、密文再加密和混淆的双云架构，该架构利用了所选密码系统的加性同态特性。将构建基于真实货运数据的分解卡车流量，并用于在不同地理尺度上测试和评估不同的研究组成部分。这个项目的结果不仅有助于丰富卡车队列行驶的文献，而且为其他几个具有相似操作的新兴移动系统提供见解。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.