The goal of this paper is to present an end-to-end, data-driven framework to control Autonomous Mobility-on-Demand systems (AMoD, i.e. fleets of self-driving vehicles). We first model the AMoD system using a time-expanded network, and present a formulation that computes the optimal rebalancing strategy (i.e., preemptive repositioning) and the minimum feasible fleet size for a given travel demand. Then, we adapt this formulation to devise a Model Predictive Control (MPC) algorithm that leverages short-term demand forecasts based on historical data to compute rebalancing strategies. We test the end-to-end performance of this controller with a state-of-the-art LSTM neural network to predict customer demand and real customer data from DiDi Chuxing: we show that this approach scales very well for large systems (indeed, the computational complexity of the MPC algorithm does not depend on the number of customers and of vehicles in the system) and outperforms state-of-the-art rebalancing strategies by reducing the mean customer wait time by up to to 89.6%.
Nonlinear Model Predictive Control with Single-Shooting Method for Autonomous Personal Mobility Vehicle
Research Center for Smart Mechatronics, National Research and Innovation Agency (BRIN)（国家研究与创新署智能机电一体化研究中心）；Diponegoro University（迪波内戈罗大学）；Osaka University（大阪大学）
Data-Driven Distributionally Robust Electric Vehicle Balancing for Autonomous Mobility-on-Demand Systems under Demand and Supply Uncertainties
University of Connecticut（康涅狄格大学）；University of Illinois, Chicago（伊利诺伊大学芝加哥分校）；Florida State University（佛罗里达州立大学）；Rutgers University（罗格斯大学）；University of Virginia（弗吉尼亚大学）
Reproducibility in the Control of Autonomous Mobility-on-Demand Systems
Massachusetts Institute of Technology（麻省理工学院）；Stanford University（斯坦福大学）；Google DeepMind（谷歌DeepMind）；Technical University of Denmark（丹麦技术大学）；Technical University of Munich（慕尼黑工业大学）；ETH Zurich（苏黎世联邦理工学院）
