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

This paper considers the problem of measuring spatial and temporal variation in driver productivity on ride-sourcing trips. This variation is especially important from a driver’s perspective: if a platform’s drivers experience systematic disparities in earnings because of variation in their riders’ destinations, they may perceive the pricing model as inequitable. This perception can exacerbate search frictions if it leads drivers to avoid locations where they believe they may be assigned “unlucky” fares. To characterize any such systematic disparities in productivity, the authors develop an analytic framework with three key components. First, the authors propose a productivity metric that looks two consecutive trips ahead, thus capturing the effect on expected earnings of market conditions at drivers’ drop-off locations. Second, the authors isolating purely spatial effects on productivity. Third, the authors apply a spatial denoising method that allows the authors to work with raw spatial information exhibiting high levels of noise and sparsity, without having to aggregate data into large, low-resolution spatial zones. By applying the authors' framework to data on more than 1.4 million rides in Austin, Texas, the authors find significant spatial variation in ride-sourcing driver productivity and search frictions. Drivers at the same location experienced disparities in productivity after being dispatched on trips with different destinations, with origin-based surge pricing increasing these earnings disparities. The authors' results show that trip distance is the dominant factor in driver productivity: short trips yielded lower productivity, even when ending in areas with high demand. These findings suggest that new pricing strategies are required to minimize random disparities in driver earnings.


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  • Accession Number: 01726414
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Dec 23 2019 7:45AM