Evaluation of ride-sourcing search frictions and driver productivity: A spatial denoising approach

Abstract

This paper considers the problem of spatial and temporal mispricing of ride-sourcing trips from a driver perspective. Using empirical data from more than 1.1 million rides in Austin, Texas, we explore the spatial structure of ride-sourcing search frictions and driver performance variables as a function of the trip destination. The spatial information is subject to noise and sparsity, and researchers tend to aggregate the data in large areas, which results in the loss of high-resolution insights. We implemented the graph-fused lasso (GFL), a spatial smoothing or denoising methodology that allows for high-definition spatial evaluation. GFL removes noise in discrete areas by emphasizing edges, which is practical for evaluating zones with heterogeneous types of trips, such as airports, without blurring the information to surrounding areas. Principal findings suggest that there are differences in driver productivity depending on trip type and pickup and drop-off location. Therefore, providing spatio-temporal pricing strategies could be one way to balance driver equity across the network.

Does Job Satisfaction Influence the Productivity of Ride-Sourcing Drivers? A Hierarchical Structural Equation Modelling Approach for the Case of Bandung City Ride-Sourcing Drivers

“…With the greater demand, higher speed, and flexibility of MBRS compared to CBRS [70], it appears that MBRS drivers can accomplish more trips than CBRS, who need more time. This finding is quite different from the finding of Zuniga-Garcia et al [32], using data from Austin, Texas, that short trips yielded lower productivity, even when ending in areas with high demand.…”

“…However, ride-sourcing drivers have found their takehome income from driving to be lower than their expectations, which is the least satisfying attribute of their job. The variation of spatial and temporal working conditions, including the dispatching and pricing scheme, determines operational and driver performance, which creates systematic disparities in drivers' earnings [32] and influences drivers' satisfaction. One reason for this income disparity is how ride-sourcing platforms display drivers' travelled kilometres, which will further determine the drivers' income [9,18].…”

“…One reason for this income disparity is how ride-sourcing platforms display drivers' travelled kilometres, which will further determine the drivers' income [9,18]. Drivers in the same location experienced disparities in productivity after being dispatched on trips with different destinations, where trip distance is the dominant factor in driver productivity [32]. The availability and reliability of information on future demand may enable the driver to wait in place for the next rider's request without cruising around and contributing to congestion [33].…”

Impact of Transportation Network Companies on Ground Access to Airports: A Case Study in Austin, Texas

“…In recent years, there has been substantial work on TNCs in transportation research studies (8)(9)(10)(11)(12). However, there is a lack of research focusing on the TNC impact on airport operations.…”

MaaSSim -- agent-based two-sided mobility platform simulator

“…While modelling the relations between the different actors involved in two-sided platforms proved to be challenging [1,2], their manifestation in the context of a dense and congested urban mobility networks induces additional complexity. Some of the prevalent topics addressed by researchers include: supply-demand interactions [3,4]; optimal matching of drivers to requests [5,6,7]; travellers' mode and platform choices [8,9,10,11]; drivers' participation, working shifts and platform choices [12,13]; labour economics impact [14,15,16]; platform pricing strategies [17]; pooled rides [18,19,20,21]; impact of shared autonomous vehicles [22,23,24]; fleet size determination [25] and driver re-positioning strategies [26,27].…”