Modelling ride-sourcing matching and pickup processes based on additive Gaussian Process Models

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 modelling. 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 modelling power of AGPM by utilizing the trained model to design and estimate idle vehicle relocation strategies.

Automated summary

In this study, a data-driven approach based on the additive Gaussian Process Model (AGPM) is proposed for ride-sourcing market modeling. The results demonstrate the advantages of AGPMs in terms of estimation accuracy and their ability to design and estimate idle vehicle relocation strategies.