Trading flexibility for adoption: Dynamic versus static walking in ridesharing
38 Pages Posted: 15 Dec 2021
Date Written: December 13, 2021
On-demand ridesharing aims to fulfill riders' transportation needs whenever and wherever they want. Although this service level is appealing for riders, overall system efficiency can improve substantially if riders are willing to be flexible. Riders' flexibility in time has previously been studied through time windows and rider queues; here, we explore riders' flexibility in space via walking to more accessible pickup locations. Ridesharing platforms have traditionally implemented dynamic walking, which jointly optimizes rider-driver assignment with rider pickup locations. We propose the new paradigm of static walking, which communicates a predetermined pickup location to the rider, and then optimizes rider-driver assignment. On its surface, dynamic walking appears to be the gold standard; the flexibility of optimization as compared to the restriction of a fixed pickup location makes the viability of static walking far from a foregone conclusion. However, a major drawback of dynamic walking is that riders are deterred by the uncertainty associated with a dynamically-generated pickup location. Static walking aims to mitigate this uncertainty, and is a semi-flexible approach that achieves the value of flexibility without asking riders to shoulder the burden of uncertainty. We study characteristics of networks on which static walking can be viable and propose algorithms to generate fixed pickup locations. Applying these algorithms to simulations on Lyft rides in Manhattan, we find that static walking achieves up to 94-95% of the value of dynamic walking. Static walking can therefore overcome dynamic walking's advantages with just a modest relative increase in rider adoption of as low as 5-6%. Finally, leveraging data on hundreds of thousands of Lyft rides, we empirically demonstrate the value of static walking by comparing riders' travel time outcomes at varying walking distances.
Keywords: ridesharing, ride-hailing, walking, matching, optimization, experiment
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