Bibliographic record
Abstract
Since the debut of UberPool in August 2014, ride-sharing has become a standard service on various platforms, such as Lyft Shared, GrabShare, and DiDi Hitch. Different from solo ride-sharing services like UberX, shared rides allow platforms to pool two riders in one car. On the one hand, ride-sharing is efficient in increasing platforms' effective capacity with the same fleet of vehicles. On the other hand, ride-sharing may incur negative externality due to compromises on privacy, space, and security. Uber reported over 20% of all rides globally are shared. To generate insights into efficient operations for society, we identify and close the gap between riders' self-interested behavior and the societal preference on shared and solo rides. In Chapter 2, we study three objectives in the ride-sharing economy: 1. volume maximization, which represents the ride-sharing platform’s interest in its growth stage, 2. revenue maximization, which represents the platform’s interest in its sustainment stage, and 3. social welfare maximization, which represents policymakers’ interest. In this chapter, we compare the optimal joining rate and sharing fraction, as well as the corresponding optimal prices of solo and shared rides that induce self-interested riders to reach the optimal goals, among three objectives. In Chapter 3, we analyze and compare the choices by decentralized riders and the centralized social planner of joining rates and sharing probabilities, under First-In-First-Out and Priority-For-Sharing disciplines. We no longer assume that riders automatically reach the most preferable equilibrium automatically. Instead, based on the evolution of equilibrium, we propose social, monetary, and priority schemes to induce riders to reach the preferable initial condition of the system, from which riders converge to the Pareto-dominant equilibrium overtime. In Chapter 4, riders and the platform react to the temporal change in system and make decision based on the real-time queue length (observable queue) and the platform may release or hide this information. We analyze the first-best outcome and riders' equilibrium behaviors and prove that the dynamic pricing is not sufficient to induce self-interested riders to reach the first-best. In addition to the dynamic pricing, we also require dispatching flexibility to fully induce self-interested riders to reach the first-best outcome. In Chapter 5, we perform a numerical experiment using the data from the City of Chicago. We assume that the ride-sharing platforms focuses either on revenue maximization or volume maximization in reality. In both cases, the joining rate and sharing fraction under revenue maximization is lower than the ones under social welfare and volume maximization. In addition, in both cases, social welfare maximization incurs a revenue loss to platforms.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".