Shared Automated Vehicles (SAVs) Fleets companies are starting pilot projects nationwide. In 2020 in Fairfax Virginia it was announced the first Shared Autonomous Vehicle Fleet pilot project in Virginia. SAVs promise to improve quality of life. However, SAVs will also induce some negative externalities by generating excessive vehicle miles traveled (VMT), which leads to more congestions, energy consumption, and emissions. The excessive VMT are primarily generated via empty relocation process. Reinforcement Learning based algorithms are being researched as a possible solution to solve some of these problems: most notably minimizing waiting time for riders. But no research using Reinforcement Learning has been made about reducing parking space cost nor reducing empty cruising time. This study explores different \textbf{Reinforcement Learning approaches and then decide the best approach to help minimize the rider waiting time, parking cost, and empty travel
We propose a novel approach to optimize fleet management by combining multi-agent reinforcement learning with graph neural network. To provide ride-hailing service, one needs to optimize dynamic resources and demands over spatial domain. While the spatial structure was previously approximated with a regular grid, our approach represents the road network with a graph, which better reflects the underlying geometric structure. Dynamic resource allocation is formulated as multi-agent reinforcement learning, whose action-value function (Q function) is approximated with graph neural networks. We use…
Foster School of Business, University of Washington（华盛顿大学福斯特商学院）；Department of Operations Research and Financial Engineering, Princeton University（普林斯顿大学运筹学与金融工程系）
Truckload brokerages, a $100 billion/year industry in the U.S., plays the critical role of matching shippers with carriers, often to move loads several days into the future. Brokerages not only have to find companies that will agree to move a load, the brokerage often has to find a price that both the shipper and carrier will agree to. The price not only varies by shipper and carrier, but also by the traffic lanes and other variables such as commodity type. Brokerages have to learn about shipper and carrier response functions by offering a price and observing whether each accepts the quote. We…
Universitat Politècnica de València（瓦伦西亚理工大学）；Instituto Tecnológico de Informática (ITI)（信息技术研究所）；Instituto de Automática e Informática Industrial（自动化与工业信息研究所）
This paper proposes the use of an on-demand, ride hailed and ride-Shared Autonomous Vehicle (SAV) service as a feasible solution to serve the mobility needs of a small city where fixed route, circulator type public transportation may be too expensive to operate. The presented work builds upon our earlier work that modeled the city of Marysville, Ohio as an example of such a city, with realistic traffic behavior, and trip requests. A simple SAV dispatcher is implemented to model the behavior of the proposed on-demand mobility service. The goal of the service is to optimally distribute SAVs alon…
Smart Commander: A Hierarchical Reinforcement Learning Framework for Fleet-Level PHM Decision Optimization
School of Aerospace Engineering, Tsinghua University（清华大学航天航空学院）；College of Information and Control Engineering, Xi'an University of Architecture and Technology（西安建筑科技大学信息与控制工程学院）；Hangzhou International Innovation Institute, Beihang University（北京航空航天大学杭州国际创新研究院）；First Aircraft Institute of Aviation Industry Corporation of China（中国航空工业集团公司第一飞机设计研究院）；Science and Technology on Complex Aviation System Simulation Laboratory（复杂航空系统仿真重点实验室）
Order dispatching and driver repositioning (also known as fleet management) in the face of spatially and temporally varying supply and demand are central to a ride-sharing platform marketplace. Hand-crafting heuristic solutions that account for the dynamics in these resource allocation problems is difficult, and may be better handled by an end-to-end machine learning method. Previous works have explored machine learning methods to the problem from a high-level perspective, where the learning method is responsible for either repositioning the drivers or dispatching orders, and as a further simp…
This letter proposes traffic management for multiple automated mobile robots (AMRs) based on a layered cost map. Multiple AMRs communicate via a data distribution service (DDS), which is shared by topics in the same DDS domain. The cost of each layer is manipulated by topics. The traffic management server in the domain sends or receives topics to each of AMRs. Using the layered cost map, the new concept of prohibition filter, lane filter, fleet layer, and region filter are proposed and implemented. The prohibition filter can help a user set an area that would prohibit an AMR from trespassing. …
Separation Assurance between Heterogeneous Fleets of Small Unmanned Aerial Systems via Multi-Agent Reinforcement Learning
Department of Mechanical and Aerospace Engineering, George Washington University（机械与航空航天工程系，乔治华盛顿大学）；Department of Electrical and Computer Engineering, Purdue University（电气与计算机工程系，普渡大学）
Southeast University（东南大学）；University of Puerto Rico at Mayagüez（波多黎各大学马亚圭斯分校）；City University of Hong Kong（香港城市大学）
In the past decade, unmanned aerial vehicles (UAVs) have been widely used in various civilian applications, most of which only require a single UAV. In the near future, it is expected that more and more applications will be enabled by the cooperation of multiple UAVs. To facilitate such applications, it is desirable to utilize a general control platform for cooperative UAVs. However, existing open-source control platforms cannot fulfill such a demand because (1) they only support the leader-follower mode, which limits the design options for fleet control, (2) existing platforms can support onl…
Few-Shot Learning for Dynamic Operations of Automated Electric Taxi Fleets under Evolving Charging Infrastructure: A Meta-Deep Reinforcement Learning Approach
Department of Industrial Engineering, Tsinghua University（清华大学工业工程系）；School of Management Science and Engineering, Central University of Finance and Economics（中央财经大学管理科学与工程学院）
Royal Holloway, University of London（伦敦大学皇家霍洛威学院）；Université de Limoges（利摩日大学）；Edinburgh Napier University（爱丁堡纳皮尔大学）；Université de Bordeaux（波尔多大学）
Inter-connected objects, either via public or private networks are the near future of modern societies. Such inter-connected objects are referred to as Internet-of-Things (IoT) and/or Cyber-Physical Systems (CPS). One example of such a system is based on Unmanned Aerial Vehicles (UAVs). The fleet of such vehicles are prophesied to take on multiple roles involving mundane to high-sensitive, such as, prompt pizza or shopping deliveries to your homes to battlefield deployment for reconnaissance and combat missions. Drones, as we refer to UAVs in this paper, either can operate individually (solo m…
Department of Philosophy, UMass Lowell（马萨诸塞大学洛厄尔分校哲学系）
Stop-and-go traffic poses many challenges to tranportation system, but its formation and mechanism are still under exploration.however, it has been proved that by introducing Connected Automated Vehicles(CAVs) with carefully designed controllers one could dampen the stop-and-go waves in the vehicle fleet. Instead of using analytical model, this study adopts reinforcement learning to control the behavior of CAV and put a single CAV at the 2nd position of a vehicle fleet with the purpose to dampen the speed oscillation from the fleet leader and help following human drivers adopt more smooth driv…
GERAD & Department of Decision Sciences, HEC Montréal（蒙特利尔高等商学院决策科学系与GERAD联合机构）；Telfer School of Management, University of Ottawa（渥太华大学特尔弗管理学院）；Evovest（伊沃维斯特）
The problem of portfolio management represents an important and challenging class of dynamic decision making problems, where rebalancing decisions need to be made over time with the consideration of many factors such as investors preferences, trading environments, and market conditions. In this paper, we present a new portfolio policy network architecture for deep reinforcement learning (DRL)that can exploit more effectively cross-asset dependency information and achieve better performance than state-of-the-art architectures. In particular, we introduce a new property, referred to as \textit{a…
Reinforcement learning-based (RL-based) energy management strategy (EMS) is considered a promising solution for the energy management of electric vehicles with multiple power sources. It has been shown to outperform conventional methods in energy management problems regarding energy-saving and real-time performance. However, previous studies have not systematically examined the essential elements of RL-based EMS. This paper presents an empirical analysis of RL-based EMS in a Plug-in Hybrid Electric Vehicle (PHEV) and Fuel Cell Electric Vehicle (FCEV). The empirical analysis is developed in fou…
Reinforcement learning (RL) techniques have shown great success in many challenging quantitative trading tasks, such as portfolio management and algorithmic trading. Especially, intraday trading is one of the most profitable and risky tasks because of the intraday behaviors of the financial market that reflect billions of rapidly fluctuating capitals. However, a vast majority of existing RL methods focus on the relatively low frequency trading scenarios (e.g., day-level) and fail to capture the fleeting intraday investment opportunities due to two major challenges: 1) how to effectively train …
The decision to rollout a vehicle is critical to fleet management companies as wrong decisions can lead to additional cost of maintenance and failures during journey. With the availability of large amount of data and advancement of machine learning techniques, the rollout decisions of a supervisor can be effectively automated and the mistakes in decisions made by the supervisor learnt. In this paper, we propose a novel learning algorithm SUPAID which under a natural 'one-way efficiency' assumption on the supervisor, uses a rule mining approach to rank the vehicles based on their roll-out feasi…
Modern vehicle fleets, e.g., for ridesharing platforms and taxi companies, can reduce passengers' waiting times by proactively dispatching vehicles to locations where pickup requests are anticipated in the future. Yet it is unclear how to best do this: optimal dispatching requires optimizing over several sources of uncertainty, including vehicles' travel times to their dispatched locations, as well as coordinating between vehicles so that they do not attempt to pick up the same passenger. While prior works have developed models for this uncertainty and used them to optimize dispatch policies, …
The multi-source electromechanical coupling makes the energy management of fuel cell electric vehicles (FCEVs) relatively nonlinear and complex especially in the types of 4-wheel-drive (4WD) FCEVs. Accurate state observing for complicated nonlinear system is the basis for fantastic energy managing in FCEVs. Aiming at releasing the energy-saving potential of FCEVs, a novel learning-based robust model predictive control (LRMPC) strategy is proposed for a 4WD FCEV, contributing to suitable power distribution among multiple energy sources. The well-designed strategy based on machine learning (ML) …
