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1. A-VRPD: Automating Drone-Based Last-Mile Delivery Using Self-Driving Cars

   https://doi.org/10.1109/TITS.2023.3266460  

   Imran, Navid Mohammad; Mishra, Sabyasachee; Won, Myounggyu (September 2023, IEEE Transactions on Intelligent Transportation Systems)

   Drone-based last-mile delivery is an emerging technology that uses drones loaded onto a truck to deliver parcels to customers. In this paper, we introduce a fully automated system for drone-based last-mile delivery through incorporation of autonomous vehicles (AVs). A novel problem called the autonomous vehicle routing problem with drones (A-VRPD) is defined. A-VRPD is to select AVs from a pool of available AVs based on crowd sourcing, assign selected AVs to customer groups, and schedule routes for selected AVs to optimize the total operational cost. We formulate A-VRPD as a Mixed Integer Linear Program (MILP) and propose an optimization framework to solve the problem. A greedy algorithm is also developed to significantly improve the running time for large-scale delivery scenarios. Extensive simulations were conducted taking into account real-world operational costs for different types of AVs, traveled distances calculated considering the real-time traffic conditions using Google Map API, and varying load capacities of AVs. We evaluated the performance in comparison with two different state-of-the-art solutions: an algorithm designed to address the traditional vehicle routing problem with drones (VRP-D), which involves human-operated trucks working in tandem with drones to deliver parcels, and an algorithm for the two echelon vehicle routing problem (2E-VRP), wherein parcels are first transported to satellite locations and subsequently delivered from those satellites to the customers. The results indicate a substantial increase in profits for both the delivery company and vehicle owners compared with the state-of-the-art algorithms. 

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2. ANALYZING EQUITY IN TRUCK-DRONE COOPERATIVE DELIVERY FOR RURAL AREAS

   Zhu, H; He, X; Wang, Z (January 2024, the 103rd Transportation Research Board Annual Meeting)

   Given the surge in rural logistics services and the disparities between urban and rural delivery services, a compelling necessity emerges to explore innovative drone-based delivery solutions. The challenges inherent in truck-drone delivery due to technological and physical barriers affect service quality for some rural customers, thus magnifying concerns about delivery fairness. To investigated delivery equity, we present a truck-drone cooperative delivery model to analyze rural customers’ accessibility to such innovative delivery technology. This model accommodates rural residents’ delivery preferences while optimizing truck routes. Drones are dispatched from designated trucks to serve customers within their flight distance. Our proposed heuristic algorithm, founded on graph-based truck-drone delivery preferences, solves this intricate problem efficiently. Numerical experiments underscore the efficacy of our approach, highlighting substantial reductions in delivery costs and an impressive 20% increase in drone deliveries on a large-scale network. Through sensitivity analyses exploring drone operational costs and flight distances–affected by government policies and technological advancements–we devise an equity metric that gauges the efficiency and accessibility of rapid rural delivery services under the truck-drone delivery framework. Our research contributes to equity analysis, addressing challenges faced by logistics companies and rural residents. Moreover, it bridges the gap between urban and rural logistics, fostering an inclusive and equitable delivery ecosystem benefiting all customers, regardless of their location. 

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3. Maximum Profit Facility Location and Dynamic Resource Allocation for Instant Delivery Logistics

   https://doi.org/10.1177/03611981221082574  

   Rajesh_Chauhan, Darshan; Unnikrishnan, Avinash; Boyles, Stephen_D (March 2022, Transportation Research Record: Journal of the Transportation Research Board)

   Increasing e-commerce activity, competition for shorter delivery times, and innovations in transportation technologies have pushed the industry toward instant delivery logistics. This paper studies a facility location and online demand allocation problem applicable to a logistics company expanding to offer instant delivery service using unmanned aerial vehicles or drones. The problem is decomposed into two stages. During the planning stage, the facilities are located, and product and battery capacity are allocated. During the operational stage, customers place orders dynamically and real-time demand allocation decisions are made. The paper explores a multi-armed bandit framework for maximizing the cumulative reward realized by the logistics company subject to various capacity constraints and compares it with other strategies. The multi-armed bandit framework provides about 7% more rewards than the second-best strategy when tested on standard test instances. A case study based in Portland Metro Area showed that multi-armed bandits can outperform the second-best strategy by more than 20%. 

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4. Planning a Trucking Research Consortium using Industry Customer Discovery and Innovation Ecosystem Mapping

   https://doi.org/10.18260/1-2--47846  

   Nalim, Mohamed Razi; Manthripragada, Nirmala Priyanka; Campbell, Cliff; Mishra, Sabya; Nicholas, Clayton (June 2024, ASEE Conferences)

   Trucking is a critical segment of the economy, supporting the supply chains of many other sectors, moving goods that account for about half of U.S. gross domestic product. However, it remains the least automated, least safe, most labor-intensive, most polluting of all transport modes. Automation, electrification, and interconnected operation of trucks could help resolve driver shortage, supply-chain disruption, delivery service delays, emissions, and road safety issues. This paper reports on the development of a plan to bring together the knowledge and business ecosystems related to trucking technologies into an industry-university consortium, while providing students at three universities with learning through interdisciplinary research. The consortium’s vision is to converge and apply knowledge in emerging technologies in connected, electrified, and automated freight trucking and public transport networks to advance agile logistics and mobility for all. With an impending award from the National Science Foundation for administrative costs, the consortium will bring together diverse private and public sector stakeholders to conduct research on commercial vehicles, freight supply chain networks, and pertinent information systems to accelerate the adoption of automated and electrified systems to transport people and goods efficiently and flexibly. It is particularly focused on attracting diverse students to be trained in trucking technologies, and on focusing research on societal and economic challenges related to trucking. 

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5. What Can My Car Tell Me? Consumer Perceptions of Transparency in Self-Driving Vehicles

   https://doi.org/10.1109/ICHMS56717.2022.9980645  

   Huff, Earl W.; Tucker, Natalie; Grady, Siobahn Day; Brinkley, Julian (November 2022, 2022 IEEE 3rd International Conference on Human-Machine Systems (ICHMS))

   Fully autonomous or “self-driving” vehicles are emerging mobility technology with potential benefits over conventional motor vehicles. Proponents argue that the widespread adoption of autonomous vehicles may save countless lives and millions of dollars annually by minimizing the likelihood of deadly vehicle crashes. However, widespread adoption of automated-driving technologies is required to realize such benefits, which research suggests, may be undermined by consumer concerns about vehicle operation transparency. Further, there is insufficient research into consumer perceptions of an autonomous vehicle’s communication and information-sharing behavior, which may impact their likelihood of purchasing one. We conducted a study using a 63-question internet-based survey distributed in the United States to licensed drivers 18 years of age and older (n=996) to examine consumer perceptions of autonomous vehicles across accountability, communication, information sharing, and concerns. Our findings show that consumer perceptions of the four dimensions vary significantly across race, gender, and ability. 

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