Trading Flexibility for Adoption: From Dynamic to Static Walking in Ride-Sharing
Bibliographic record
Abstract
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 .
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".