We study revenue-optimal pricing and driver compensation in ridesharing platforms when drivers have heterogeneous preferences over locations. If a platform ignores drivers' location preferences, it may make inefficient trip dispatches; moreover, drivers may strategize so as to route towards their preferred locations. In a model with stationary and continuous demand and supply, we present a mechanism that incentivizes drivers to both (i) report their location preferences truthfully and (ii) always provide service. In settings with unconstrained driver supply or symmetric demand patterns, our mechanism achieves (full-information) first-best revenue. Under supply constraints and unbalanced demand, we show via simulation that our mechanism improves over existing mechanisms and has performance close to the first-best.
There are various empirical studies, analyzing the impact of dynamic pricing @cite_2 @cite_9 , the labor market for Uber drivers @cite_0 @cite_1 , consumer surplus @cite_4 , the value of flexible work @cite_11 , the gender earnings gap @cite_3 , and the commission vs. medallion lease based compensation models @cite_5 .
@cite_4 @cite_9 @cite_1 @cite_3 @cite_0 @cite_2 @cite_5 @cite_11
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Ride-hailing drivers pay a proportion of their fares to the ride-hailing platform operator, a commission-based compensation model used by many internet-mediated service providers. To Uber drivers, this commission is known as the Uber fee. By contrast, traditional taxi drivers in most US cities make a fixed payment independent of their earnings, usually a weekly or daily medallion lease, but keep every fare dollar net of expenses. We assess these compensation models from a driver’s point of view using an experiment that offered random samples of Boston Uber drivers opportunities to lease a virtual taxi medallion that eliminates the Uber fee. Some drivers were offered a negative fee. Drivers’ labor supply response to our offers reveals a large intertemporal substitution elasticity, on the order of 1.2. At the same time, our virtual lease program was under-subscribed: many drivers who would have benefitted from buying an inexpensive lease chose to opt out. We use these results to compute the average compensation required to make drivers indifferent between ride-hailing and a traditional taxi compensation contract. The results suggest that ride-hailing drivers gain considerably from the opportunity to drive without leasing. The growth of the "gig" economy generates worker flexibility that, some have speculated, will favor women. We explore one facet of the gig economy by examining labor supply choices and earnings among more than a million rideshare drivers on Uber in the U.S. Perhaps most surprisingly, we find that there is a roughly 7 gender earnings gap among drivers. The uniqueness of our data - knowing exactly the production and compensation functions - permits us to completely unpack the underlying determinants of the gender earnings gap. We find that the entire gender gap is caused by three factors: experience on the platform (learning-by-doing), preferences over where when to work, and preferences for driving speed. This suggests that, as the gig economy grows and brings more flexibility in employment, women's relatively high opportunity cost of non-paid-work time and gender-based preference differences can perpetuate a gender earnings gap even in the absence of discrimination. The ubiquitous deployment of mobile and sensor technologies has led to both the capacity to observe human behavior in physical (offline) settings as well as to record it. This provides researchers with a new lens to study and better understand the individual decision processes that were previously unobserved. In this paper, we study decision making behavior of 11,196 taxi drivers in a large Asian city using a rich data set consisting of 10.6 million fine-grained GPS trip records. These records include detailed taxi GPS trajectories, taxi occupancy data (i.e., whether a taxi was occupied with a passenger or was vacant) and taxi drivers’ daily incomes. This capacity to use data where occupancy of the taxi is known is a distinctive feature of our data set and sets this work apart from prior work which has attempted to study driver behavior. The specific decision we focus on pertains to actions drivers take to find new passengers after they have dropped off their current passengers. In particular, we study the role of information derivable from the GPS trace data (e.g., where passengers are dropped off, where passengers are picked up, longitudinal taxicab travel history with fine-grained time stamps) observable by or made available to drivers in enabling them to learn the distribution of demand for their services over space and time. We conduct our study using a heterogeneous Bayesian learning model. We find strong heterogeneity in individual learning behavior and driving decisions, which is significantly associated with individual economic outcomes. Drivers with higher incomes benefit significantly from their ability to learn from not only demand information directly observable in the local market, but also aggregate information on demand flows across markets. Interestingly, our policy simulations indicate information that is noisy at the individual level becomes valuable after being aggregated across various spatial and temporal dimensions. Moreover, the value of information does not increase monotonically with the scale and frequency of information sharing. Finally, our study has important welfare implications in that efficient information sharing leads to an income increase among all drivers, instead of a redistribution of income between different types of drivers. Our work allows us not only to explain driver decision making behavior using these detailed behavioral traces, but also to prescribe information sharing strategy for the firm in order to improve the overall market efficiency. The current study investigates temporal and weather-related variation in taxi ridership patterns in New York City (NYC) from the perspective of demand-supply equilibrium. For this purpose, the study conducted a computationally heavy analysis of a taxi global positioning system (GPS) data set with more than 147 million records covering 10 months of activity. It found that there were significant variations in ridership and trip distances for different day of week-time of day (DOW-TOD) weather condition periods. Drivers, as the actors who determine taxi supply, respond to this variation in a way that maintains their incomes above approximately $20 per hour. The impact of weather is also investigated. Snow conditions do not affect the hourly revenues but when there is rainfall, drivers make more frequent and slightly shorter trips that increase their income. After reaching their income target, drivers may end their shift early, a finding that may explain the perceived taxi shortage during prolonged rain conditions. It was also shown that within existing trip frequency and trip distance patterns, the impact of a proposed taxi fare increase in NYC on hourly revenues would vary among different TOD periods. This suggests that a fare increase has the potential to alter the temporal taxi supply as well as the taxi lease rents (which vary by shift) for certain periods. Based on these findings, the paper provides insights into the temporal and weather-related variation in taxi demand-supply equilibrium. Its findings can assist policymakers who regulate the taxi industry.