Spatial pricing empirical evaluation of ride-sourcing trips using the graph-fussed lasso for total variation denoising

This study explores the spatial pricing discrimination of ride-sourcing trips using empirical data. We use information from more than 1.1 million rides in Austin, Texas, provided by a non-profit transportation network company from a period where the main companies were out of the city. We base the analysis on operational variables such as the waiting or idle time between trips, reaching time, and trip distance. Also, we estimate three different productivity measures to evaluate the impact of the trip destination on the driver continuation payoff. We propose the application of a total variation denoising method that enhances the spatial data interpretation. The selected methodology, known as the graph-fussed lasso (GFL), uses an l₁-norm penalty term that presents a variety of benefits to the denoising process. Specifically, this approach provides local adaptivity; it can adapt to inhomogeneity in the level of smoothness across the graph. Solving the GFL smoothing problem involves convex-optimization methods, we make use of a fast and flexible algorithm that presents scalability and high computational efficiency. The principal contributions of this research effort include a temporal and spatial evaluation of different ride-sourcing productivity measures in the Austin area, an analysis of ride-sourcing trip pricing and its effect on driver equity, and a description of the principal ride-sourcing travel patterns in the city of Austin. The main results suggest that drivers with rides ending in the central area present favorable spatial differences in productivity when including the revenue of two consecutive trips. However, the time effect was more contrasting. Weekend rides tend to provide better driver productivity measures.