  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.
