大规模共乘系统的实时调度：集成Optimization、机器学习和模型预测控制
Real-Time Dispatching of Large-Scale Ride-Sharing Systems: Integrating Optimization, Machine Learning, and Model Predictive Control
摘要：
本文考虑了大规模实时共乘系统的调度，以解决许多城市面临的拥堵问题。目标是用少量车辆服务所有客户（服务保证），同时在乘车持续时间的限制下最大限度地减少等待时间。本文提出了一种端到端的方法，该方法紧密集成了最先进的调度算法，机器学习模型来预测随着时间的推移的区域到区域的需求，以及模型预测控制优化来重新定位闲置车辆。在纽约市使用历史出租车行程的实验表明，这种集成在所有测试案例中将平均等待时间降低了约30%，在高需求区域的最大实例中降低了近55%。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
