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Ride-hailing marketplaces like Uber and Lyft use dynamic pricing, often called surge, to balance the supply of available drivers with the demand for rides. We study driver-side payment mechanisms for such marketplaces, presenting the theoretical foundation that has informed the design of Uber’s new additive driver surge mechanism. We present a dynamic stochastic model to capture the impact of surge pricing on driver earnings and their strategies to maximize such earnings. In this setting, some time periods (surge) are more valuable than others (nonsurge), and therefore trips of different time lengths vary in the induced driver opportunity cost. First, we show that multiplicative surge, historically the standard on ride-hailing platforms, is not incentive compatible in a dynamic setting. We then propose a structured, incentive-compatible pricing mechanism. This closed-form mechanism has a simple form and is well approximated by Uber’s new additive surge mechanism. Finally, through both numerical analysis and real data from a ride-hailing marketplace, we show that additive surge is more incentive compatible in practice than is multiplicative surge. This paper was accepted by David Simchi-Levi, revenue management and market analytics.
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A Traveler Incentive Program for Promoting Community-Based Ridesharing
Traffic congestion has become a serious issue around the globe, partly owing to single-occupancy commuter trips. Ridesharing can present a suitable alternative for serving commuter trips. However, there are several important obstacles that impede ridesharing systems from becoming a viable mode of transportation, including the lack of a guarantee for a ride back home as well as the difficulty of obtaining a critical mass of participants. This paper addresses these obstacles by introducing a traveler incentive program (TIP) to promote community-based ridesharing with a ride back home guarantee among commuters. The TIP program allocates incentives to (1) directly subsidize a select set of ridesharing rides and (2) encourage a small, carefully selected set of travelers to change their travel behavior (i.e., departure or arrival times). We formulate the underlying ride-matching problem as a budget-constrained min-cost flow problem and present a Lagrangian relaxation-based algorithm with a worst-case optimality bound to solve large-scale instances of this problem in polynomial time. We further propose a polynomial-time, budget-balanced version of the problem. Numerical experiments suggest that allocating subsidies to change travel behavior is significantly more beneficial than directly subsidizing rides. Furthermore, using a flat tax rate as low as 1% can double the system’s social welfare in the budget-balanced variant of the incentive program.
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- Award ID(s):
- 2046372
- PAR ID:
- 10315686
- Date Published:
- Journal Name:
- Transportation Science
- ISSN:
- 0041-1655
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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This paper proposes a novel quantity-based demand management system that aims to promote ridesharing. The system sells a time-dependent permit to access a road facility (conceptualized as a bottleneck) by auction but encourages commuters to share permits with each other. The commuters may be assigned one of three roles: solo driver, ridesharing driver, or rider. At the core of this auction-based permit allocation and sharing system (A-PASS) is a trilateral matching problem (TMP) that matches permits, drivers, and riders. Formulated as an integer program, TMP is first shown to be tightly bounded by its linear relaxation. A pricing policy based on the classical Vickrey–Clarke–Groves (VCG) mechanism is then devised to determine the payment of each commuter. We prove that, under the VCG policy, different commuters pay exactly the same price as long as their role and access time are the same. Importantly, by controlling the number of shared rides, any deficit that may arise from the VCG policy can be eliminated. This may be achieved with a relatively small loss to system efficiency, thanks to the revenue generated from selling permits. Results of a numerical experiment suggest A-PASS strongly promotes ridesharing. As sharing increases, all stakeholders are better off: the ridesharing platform receives greater profits, the commuters enjoy higher utility, and society benefits from more efficient utilization of the road infrastructure.more » « less
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Abstract This article formulates and solves a stochastic optimization model to investigate the impact of crowdsourced platforms (e.g., ridesharing, on‐demand delivery, volunteer food rescue, and carpooling) offering small, personalized menus of requests and incentive offers for drivers to choose from. To circumvent nonlinear variable relationships, we exploit model structure to formulate the program as a stochastic linear integer program. The proposed solution approach models stochastic responses as a sample of variable and fixed scenarios, and to counterbalance solution overfitting, uses a participation ratio parameter. The problem is also decomposed and iterated among two separate subproblems, one which optimizes menus, and another, which optimizes incentives. Computational experiments, based on a ride sharing application using occasional drivers demonstrate the importance of using multiple scenarios to capture stochastic driver behavior. Our method provides robust performance even when discrepancies between predicted and observed driver behaviors exist. Computational results show that offering menus and personalized incentives can significantly increase match rates and platform profit compared to recommending a single request to each driver. Further, compared to the menu‐only model, the average driver income is increased, and more customer requests are matched. By strategically using personalized incentives to prioritize promising matches and to increase drivers' willingness to accept requests, our approach benefits both drivers and customers. Higher incentives are offered when drivers are more likely to accept, while fewer incentives and menu slots are reserved for driver‐request pairs less likely to be accepted.more » « less
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null (Ed.)We study an online hypergraph matching problem with delays, motivated by ridesharing applications. In this model, users enter a marketplace sequentially, and are willing to wait up to $$d$$ timesteps to be matched, after which they will leave the system in favor of an outside option. A platform can match groups of up to $$k$$ users together, indicating that they will share a ride. Each group of users yields a match value depending on how compatible they are with one another. As an example, in ridesharing, $$k$$ is the capacity of the service vehicles, and $$d$$ is the amount of time a user is willing to wait for a driver to be matched to them. We present results for both the utility maximization and cost minimization variants of the problem. In the utility maximization setting, the optimal competitive ratio is $$\frac{1}{d}$$ whenever $$k \geq 3$$, and is achievable in polynomial-time for any fixed $$k$$. In the cost minimization variation, when $k = 2$, the optimal competitive ratio for deterministic algorithms is $$\frac{3}{2}$$ and is achieved by a polynomial-time thresholding algorithm. When $k>2$, we show that a polynomial-time randomized batching algorithm is $$(2 - \frac{1}{d}) \log k$$-competitive, and it is NP-hard to achieve a competitive ratio better than $$\log k - O (\log \log k)$$.more » « less
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Urban heat exposure is an increasing health risk among urban dwellers. Many cities are considering accommodating active mobility, especially walking and biking, to reduce greenhouse gas emissions. However, promoting active mobility without proper planning and transportation infrastructure to combat extreme heat exposure may cause more heat-related morbidity and mortality, particularly in future with projected climate change. This study estimated the effectiveness of active trip heat exposure mitigation under built environment and travel behavior change. Simulations of the Phoenix metro region's 624,987 active trips were conducted using the activity-based travel model (ABM), mean radiant temperature (T MRT , net human radiation exposure), transportation network, and local climate zones. Two scenarios were designed to reduce traveler exposure: one that focuses on built environment change (making neighborhoods cooler) and the other on travel behavior (switching from shorter travel time but higher exposure routes to longer travel time but cooler routes) change. Travelers experienced T MRT heat exposure ranging from 29°C to 76°C (84°F to 168°F) without environmental or behavioral change. Active trip T MRT exposures were reduced by an average of 1.2–3.7°C when the built environment was changed from a hotter to cooler design. Behavioral changes cooled up to 10 times more trips than changes in built environment changes. The marginal benefit of cooling decreased as the number of cooled corridors transformed increased. When the most traveled 10 km of corridors were cooled, the marginal benefit affected over 1,000 trips/km. However, cooling all corridors results in marginal benefits as low as 1 trip/km. The results reveal that heavily traveled corridors should be prioritized with limited resources, and the best cooling results come from environment and travel behavior change together. The results show how to surgically invest in travel behavior and built environment change to most effectively protect active travelers.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1287/trsc.2021.1121
