基于学习的开放式驾驶员指导和再平衡,以减少乘车平台上乘客的等待时间

Jie Gao, Xiaoming Li, C. Wang, Xiao Huang
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引用次数: 1

摘要

我们提出了一种基于学习的方法,用于开放式司机指导和网约车平台的再平衡。目标是通过结合基于学习的开放驾驶员引导和再平衡,进一步增强批量匹配减少等待时间的好处。通过利用骑手需求数据,通过将机器学习技术与两阶段随机规划模型相结合来计算引导解决方案。为了验证所提出方法的性能,我们使用纽约出租车旅行数据集进行了数值实验。结果表明,该方法在平均等待时间方面优于单值估计模型和使用泊松分布的参数模型。假设开放的驾驶员在批处理时间窗口前随机定位,与没有引导的批处理匹配相比,该方法减少了70%以上的平均等待时间。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Learning-based open driver guidance and rebalancing for reducing riders’ wait time in ride-hailing platforms
We propose a learning-based approach for open driver guidance and rebalancing in ride-hailing platforms. The objective is to further enhance the wait time reduction benefit of batched matching by incorporating learning-based open driver guidance and rebalancing. By leveraging the rider demand data, the guidance solutions are computed through the integration of machine learning techniques with a two-stage stochastic programming model. To validate the performance of the proposed approach, we conduct numerical experiments using the New York taxi trip data sets. Our results show that the proposed approach outperforms the single value estimation model and the parametric model using Poisson distribution in terms of average wait time. When assuming the open drivers are randomly located before the batching time window, the proposed approach reduces more than 70% of average wait time compared to batched matching without guidance.
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