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.
翻译:之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操作上的洞察力;虽然模型和数据之间的合宜性被否定;为了同时考虑模型和数据之间的合宜性以及分析性的可移植结构,我们提议以乘车外包市场模型的加加加高西进程模型(AGPM)为基础的数据驱动方法;根据在中国杭州收集到的真实世界数据测试该框架;我们适合分析模型、机器学习模型和AGPM,其中匹配或接合的数量被用作产出,空间、时间、需求和供应变量用作投入;结果显示了AGPM在回收这两个过程时在估算准确性方面的优势;此外,我们通过使用经过培训的模型来设计和估计闲置车辆迁移战略,来说明AGPM的建模能力。