论文标题
基于加性高斯流程模型来建模乘坐乘法匹配和拾取过程
Modeling Ride-Sourcing Matching and Pickup Processes based on Additive Gaussian Process Models
论文作者
论文摘要
匹配和拾取过程是乘车服务的核心功能。先前的研究采用了丰富的分析模型来描述这两个过程并获得运营见解。虽然模型和数据之间的拟合良好被驳回了。为了同时考虑模型和数据之间的适用性以及可分析的可处理地层，我们提出了一种基于数据驱动的方法，该方法基于添加性高斯流程模型（AGPM），用于乘车市场建模。该框架是根据在中国杭州收集的现实数据测试的。我们拟合分析模型，机器学习模型和AGPM，其中将匹配项或拾音器的数量用作输出，空间，时间，需求和供应协变量作为输入。结果证明了AGPM在估计准确性方面恢复这两个过程的优势。此外，我们通过利用训练有素的模型来设计和估计闲置车辆搬迁策略来说明AGPM的建模能力。
Matching and pickup processes are core features of ride-sourcing services. Previous studies have adopted abundant analytical models to depict the two processes and obtain operational insights; while the goodness of fit between models and data was dismissed. To simultaneously consider the fitness between models and data and analytically tractable formations, we propose a data-driven approach based on the additive Gaussian Process Model (AGPM) for ride-sourcing market modeling. The framework is tested based on real-world data collected in Hangzhou, China. We fit analytical models, machine learning models, and AGPMs, in which the number of matches or pickups are used as outputs and spatial, temporal, demand, and supply covariates are utilized as inputs. The results demonstrate the advantages of AGPMs in recovering the two processes in terms of estimation accuracy. Furthermore, we illustrate the modeling power of AGPM by utilizing the trained model to design and estimate idle vehicle relocation strategies.
