论文标题
实时调度大型乘车共享系统：整合优化，机器学习和模型预测性控制
Real-Time Dispatching of Large-Scale Ride-Sharing Systems: Integrating Optimization, Machine Learning, and Model Predictive Control
论文作者
论文摘要
本文考虑了派遣大型实时乘车共享系统来解决许多城市所面临的拥塞问题。目标是为所有客户（服务保证）提供少量车辆的服务，同时在乘车持续时间的限制下最小化等待时间。本文提出了一种端到端的方法，该方法将紧密整合到最先进的派遣算法，一种机器学习模型，以预测随着时间的推移区域到区域的需求，以及一种模型预测控制优化，以重新安置空闲车辆。在纽约市使用历史悠久的出租车旅行的实验表明，这种整合在所有测试案例中降低了30％的平均等待时间，并且在高需求区域的最大实例上达到了接近55％。
This paper considers the dispatching of large-scale real-time ride-sharing systems to address congestion issues faced by many cities. The goal is to serve all customers (service guarantees) with a small number of vehicles while minimizing waiting times under constraints on ride duration. This paper proposes an end-to-end approach that tightly integrates a state-of-the-art dispatching algorithm, a machine-learning model to predict zone-to-zone demand over time, and a model predictive control optimization to relocate idle vehicles. Experiments using historic taxi trips in New York City indicate that this integration decreases average waiting times by about 30% over all test cases and reaches close to 55% on the largest instances for high-demand zones.
