Trading Flexibility for Adoption: From Dynamic to Static Walking in Ride-Sharing

On-demand ride-sharing aims to fulfill riders’ transportation needs whenever and wherever they want. Although this service level appeals to riders, overall system efficiency can improve substantially if riders are willing to be flexible. Here, we explore riders’ flexibility in space via walking to more accessible pickup locations. Ride-sharing platforms have traditionally implemented dynamic walking to optimize rider pickup locations and rider-driver assignment jointly. We propose an alternative that we call static walking, which presents a predetermined pickup location to the rider before optimizing rider-driver assignment. Although dynamic walking enables more efficient matching of riders and drivers, we hypothesize that riders prefer static walking because of the certainty of the pickup location before booking the ride. Using simulations on Lyft data, we show that static walking can capture up to 96% of the value of dynamic walking in congested urban networks at a fixed adoption rate. Furthermore, experimentation on Lyft’s user interface suggests that providing riders with information on pickup location before an opt-in decision can increase walking adoption—to the extent that static walking may outperform dynamic walking overall. More broadly, this study highlights the importance of carefully designing flexibility mechanisms on platforms: a little flexibility goes a long way, especially when flexibility presents a barrier to adoption. This paper was accepted by J. George Shanthikumar, data science. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.03201 .