Surge Pricing Research Articles

Surge Pricing on A Service Platform under Spatial Spillovers: Evidence from Uber

Ride-sharing platforms employ zone-specific surge pricing to match capacity with demand. Since the prices are spatially dispersed, labor capacity spills over (i.e., drivers move around) across zone...

Contemporary Issues in the Ethics of Data Analytics in Ride-Hailing Service

Big data technology has brought about the establishment of transportation network companies (TNCs), such as Uber in the USA, and Didi in China, who provide the ride hailing services (RHS's) which allows the individuals to act as independent contractors serving customers via a smartphone app. The RHS system installed gigantic databases which can store enormous data and its equipment can collect various kinds of data at any time. Additionally, data analysis technologies in TNCs such as behavioral analysis, heatmap optimization surge pricing and the automatic order assignment mechanisms, can make the RHS more efficient. However, along with achievements big data technology offers, we are struggling towards the ethical problems, including privacy, inequity and safety.

Driver Surge Pricing

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 (non-surge), and so 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.

Integrated Reward Scheme and Surge Pricing in a Ride-Sourcing Market

Surge pricing is commonly used in on-demand ridesourcing platforms to dynamically balance demand and supply, although it is controversial and has long stimulated debate regarding its pros and cons. In practice, there is usually a reasonable or legitimate range of prices. However, such a constrained surge-pricing strategy may fail to balance demand and supply in certain cases—e.g., even adopting the highest allowed price cannot reduce peak-period demand to a level at which the market clears without some form of non-price rationing. To address this limitation, we propose a novel reward scheme integrated with surge pricing: Passengers pay an additional amount to a reward account on top of the regular surge price during peak hours, then use the balance in their reward account to subsidize trips during off-peak hours. We propose models to describe the number of travel requests and the number of active drivers on the platform, and characterize the market equilibrium under several assumptions. We compare scenarios with and without the reward scheme from three perspectives: passenger utility, driver income, and platform revenue and profit. We find that in some situations, all three stakeholders—i.e., passengers, drivers, and the platform—will be better off under the reward scheme integrated with surge pricing.

Pricing strategy of cab aggregators in India

Cab aggregators are making rapid waves across India and the world. Ola and Uber have seized the opportunity with both the hands through startup and stand up India campaign to tap the potential of the Indian market. The article critically evaluates the pricing strategy employed by and Ola and Uber in India, from an economic perspective. It underlines surge pricing as a strategic business tool vis-a-vis revenue management model and analyses the reasons behind it, including challenges to its application from a legal and a consumer standpoint. It also dwells on the alternative mechanisms such as ‘concealed’ surge pricing, and the effect of such selective pricing on actual demand and supply of cabs on the road and driver incentives. Finally, it prescribes policy solutions, and outlines future questions which can be derived from this study.

An Empirical Analysis of Market Formation, Pricing, and Revenue Sharing in Ride-Hailing Services

We empirically study the two-sided market for ride-hailing services and the effects of pricing policies and regulation on the value generated by these platforms. We first develop a discrete choice model for the formation of mutually dependent demand (consumer side) and supply (driver side) that jointly and endogenously determine pricing. Using this model and a comprehensive data set obtained from the largest mobile ride platform in China, we estimate consumer and driver price elasticities as well as other factors that affect market participation for the two main markets the company hosts, namely basic ride-hailing and taxi services, with a high level of model fit. Based on these estimation results and counterfactual analysis, we demonstrate that surge pricing improves consumer and driver welfare as well as platform revenues, while reducing taxi revenues on the platform. However, our analysis suggests that surge pricing can hurt consumer surplus and platform revenues during non-peak hours, and hence should be used selectively during the day. We show that platform revenues can be maximized by increasing drivers' revenue share from the current levels. We measure the effects of proposed regulations such as imposing ride-hail price hikes, and demonstrate that they would decrease consumer and driver surplus significantly. Finally, we estimate that the platform's basic ride-hailing services generated consumer value equivalent to more than 32 Billion USD in China in 2017.

Your Uber Is Arriving: Managing On-Demand Workers Through Surge Pricing, Forecast Communication, and Worker Incentives

On-demand platforms (e.g., Uber, Lyft) often rely on independent workers, who are not directly under the platform’s control, to be available at the “right” time and locations to serve consumers at ...

Viability assessment of emerging smart urban para-transit solutions: Case of cab aggregators in Kolkata city, India

The dimensions of transport development are closely linked with the socio-economic profile and technological level of a city. The entire trafficscape of the city of Kolkata evolved as the efficiency of the mass transport services declined, disposable income of the majority increased. There was gradual privatisation with increase in the share of para-transit modes. As the literate, technologically conversant urban population wanted a smarter alternative to regular taxis and auto rickshaws the app-based cab aggregator services brought in the silent revolution in the transport communication system. The main objectives of the paper are to bring out the essence of changing scenario, to access qualitatively and quantitatively that how far analysis of big data and reliance on technology through ICT and IoT has been able to meet the gap between expected and actual service delivery of the said para-transit mode through SWOT analysis, regression analysis and perception studies involving primary and secondary database and to have an idea about the future trajectory of this emerging app cab market. The study found that though majority of the commuters were satisfied with availability, reliability, cost effectiveness, safety, cashless option, driver behaviour, comfort and absence of bargaining there were issues regarding transparency and surge pricing. The drivers too were content with their improved earnings but were unhappy with the long hours and were apprehensive with regular incentive cuts. So, the situation is volatile with promising future if the companies manage to balance between the virtual capabilities and actual reality.

A resource sharing (sharing platform) scheme on online taxi services

This paper will review the match between single driver and single rider in online taxi services through a resource sharing (sharing platform) for the operators with the objectives to maximize the profit for drivers (operators) and minimize waiting time for passengers so that the matching rate is higher. A low matching rate between rider and driver can cause the consumer to drop the services. The matching between single driver and single rider in online taxi services through a sharing platform scheme is formulated in maximum weighted bipartite matching problem. To solve the proposed model, we use Kuhn Munkres Algorithm, while to solve the shortest path for the driver to pick up the passenger and the shortest path of passenger's origin destination, modified Dijkstra with adaptive algorithm based on Wei Peng et.al (2012) is used. Based on illustrative example with several cases, we found a resource sharing scenario can optimize the matching between driver and rider and moreover can solve the surge pricing problem which is deemed as less transparant to customer

Pricing policy selection for a platform providing vertically differentiated services with self-scheduling capacity

In this article, we study three pricing policies for a monopoly platform, such as Uber or Gett, who offers vertically differentiated services to customers via multiple types of self-scheduling providers. Ideally, the platform can employ a “dynamic pricing” policy, which pays providers wages and charges customers prices for the transactions of different services that both adjust based on prevailing demand conditions, to maximize its profit. However, since it is challenging for the platform to implement and for providers to understand this policy, the other two pricing policies are commonly adopted in practice, that is, “surge pricing” policy (adopted by Uber) which pays providers a fixed commission of its dynamic prices, and “static pricing” policy (applied by Gett) which pays providers a fixed commission of its fixed prices. By observing these phenomena, we propose to study and discuss the platform’s profit performance of these three pricing strategies. We show that the surge pricing policy does not always perform well, which can explain why some on-demand platforms would implement the static pricing policy in practice. Also, although the dynamic pricing policy will significantly improve the platform’s profit, we find that the profitability of the static (surge) pricing policy would approach that of the dynamic pricing policy if the platform can balance the number of different types of providers and/or reduce the commission rate.

Surge Pricing and Two-Sided Temporal Responses in Ride-Hailing

Surge pricing in ride hailing platforms is a pivotal and controversial subject. In this paper, we investigate surge pricing from a temporal perspective; in particular, we highlight the strategic behavior by riders and drivers and that drivers respond to surge pricing much slower than riders. We first identify an equilibrium pricing strategy of a short-lived sharp price surge followed by a lower price that we refer to as skimming surge pricing (SSP). We rationalize the controversial sharp surge pricing practice beyond the basic economic principle of demand and supply and show that it can benefit riders: The short-lived sharp price surge causes many high-value riders to voluntarily wait out the initial surge period, who attract additional drivers to come to the region to serve riders at a much lower price than the initial surge price. We also identify another equilibrium pattern of a low initial price followed by a higher price that we refer to as penetration surge pricing (PSP). We find that PSP equilibria are superior to SSP equilibria, but requires platforms to share demand-supply information with drivers. We extend the base model by accounting for the spatial perspective that drivers need to travel over different distances to the surge region, and gain additional insights on how the driver spatial distribution affects our results. We also show that a flat-fee commission generally increases matching volumes and platform profits compared with proportional commissions. Our findings have practical implications for platforms to improve their surge pricing practices.

Surge Pricing on a Service Platform Under Spatial Spillovers: Evidence From Uber

Ride-sharing platforms employ surge pricing to match anticipated capacity spillover with demand. We develop an optimization model to characterize the relationship between surge price and spillover. We test predicted relationships using a spatial panel model on a dataset from Uber’s operation. Results reveal that Uber’s pricing accounts for both capacity and price spillover. There is a debate in the management community on the efficacy of labor welfare mechanisms associated with shared capacity. We conduct counterfactual analysis to provide guidance in regards to the debate, for managing congestion, while accounting for consumer and labor welfare through this online platform.

Do Digital Platforms Reduce Moral Hazard? The Case of Uber and Taxis

Digital platforms provide a variety of technology-enabled tools that enhance market transparency, such as real-time monitoring, ratings of buyers and sellers, and low-cost complaint channels. How do these innovations affect moral hazard and service quality? We investigate this problem by comparing driver routing choices and efficiency on a large digital platform, Uber, with traditional taxis. The identification is enabled by matching taxi and Uber trips at the origin-destination-time level so they are subject to the same underlying optimal route, by exploiting characteristics of the pricing schemes that differentially affect the incentives of taxi and Uber drivers in various circumstances, and by examining changes in behavior when drivers switch from taxis to Uber. We find that (1) taxi drivers route longer in distance than matched Uber drivers on metered airport routes by an average of 8%, with non-local passengers on airport routes experiencing even longer routing; (2) no such long routing is found for short trips in dense markets (e.g., within-Manhattan trips) or airport trips with a flat fare; and (3) long routing in general leads to longer travel time, instead of saving passengers time. These findings are consistent with digital platform designs reducing driver moral hazard, but not with competing explanations such as driver selection or differences in driver navigation technologies. We also find evidence of Uber drivers' long routing on airport trips in times of surge pricing, suggesting that the tech-enabled market designs may not be binding in our setting.

The Efficient Queue and the Case Against Dynamic Pricing

Surge pricing—using data and algorithms to raise prices in response to unexpected increases in demand—has spread across the economy in recent years, from Amazon, to Disney World, to commuter highways, not to mention Uber, which is infamous for surge pricing rides. Companies claim that surge pricing equilibrates supply and demand, but that is impossible, at least in the short run when demand unexpectedly outstrips supply. What surge pricing really does is to ration existing supplies based on ability to pay. That is both distributively unjust and potentially inefficient. It is also anticompetitive in the sense that it reduces the power of the competitive pricing that prevails before a surge in demand to carry over into the surge period. As such, surge pricing is similar in effect to price fixing, which also prevents competitive pricing from carrying over into periods during which firms have obtained power to raise prices. Courts should therefore rule surge pricing per se illegal under the antitrust laws, just as they do price fixing today.

Surge Price or Subsidy? Information Sharing or Suggestion? A Behavioral Investigation of Workers’ Relocation in On-Demand Platforms

Problem Definition: We have witnessed a rapid rise of on-demand platforms, such as Uber, in the past few years. While these platforms allow workers to choose their own working hours, they have limited leverage in maintaining the availability of workers within a region. As such, platforms often implement various policies, including offering financial incentives and/or communicating customer demand to workers in order to direct more workers to regions with shortage in supply. This research examines how behavioral biases such as regret aversion may influence workers' relocation decisions and ultimately the system performance. Academic/Practical Relevance: Studies on on-demand platforms often assume that workers are rational agents who make optimal decisions. Our research investigates workers' relocation decisions from a behavioral perspective. A deeper understanding of workers' behavioral biases and their causes will help on-demand platforms design appropriate policies to increase their own profit, worker surplus, and the overall efficiency of matching supply with demand. \newline Methodology: We use a combination of behavioral modeling and controlled lab experiments. We develop analytical models that incorporate regret aversion to produce theoretical predictions, which are then tested and verified via a series of controlled lab experiments. Results: We find that regret aversion plays an important role in workers' relocation decisions. Regret averse workers are more willing to relocate to the supply-shortage zone than rational workers. This increased relocation behavior, however, is not sufficient to translate to a better system performance. Platform interventions, such as demand information sharing and dynamic wage bonus, can help further improve the system. We find that workers' regret-aversion behavior may lead to an increased profit for the platform, a higher surplus for the workers, and an improved demand-supply matching efficiency, thus benefiting the entire on-demand system. Managerial Implications: Our research emphasizes the importance and necessity of incorporating workers' behavioral biases such as regret aversion into the policy design of on-demand platforms. Policies without considering the behavioral aspect of workers' decisions may lead to lost profit for the platform and reduced welfare for workers and customers, which may ultimately hurt the on-demand business.

A Surge-Type Pricing in Ridesharing Systems is Stability Optimal

The availability of drivers at a certain location affects the waiting time of passengers that arrive to be served by the platform. We introduce a queueing model for this waiting time and consider the effect on stability of available drivers’ mobility pattern, their willingness to accept rides in a given location, and the incentives offered by the platform. For any fixed number of drivers, we characterize the largest set of passenger arrival rates which can result to stable queues under some policy dictating the movement of available drivers and their acceptance of rides. It turns out that any such policy can be enforced by offering appropriate region-dependent rewards to drivers for passenger pick up. Next, we show that dynamic rewards which are proportional to the passenger queue lengths, have the property of stabilizing queues for any arrival rates within the stability region. Seen from the perspective of drivers, such rewards – which resemble surge pricing – maximize their utilization.

Surge Pricing and Its Spatial Supply Response

We consider the pricing problem faced by a revenue-maximizing platform matching price-sensitive customers to flexible supply units within a geographic area. This can be interpreted as the problem faced in the short term by a ride-hailing platform. We propose a two-dimensional framework in which a platform selects prices for different locations and drivers respond by choosing where to relocate, in equilibrium, based on prices, travel costs, and driver congestion levels. The platform’s problem is an infinite-dimensional optimization problem with equilibrium constraints. We elucidate structural properties of supply equilibria and the corresponding utilities that emerge and establish a form of spatial decomposition, which allows us to localize the analysis to regions of movement. In turn, uncovering an appropriate knapsack structure to the platform’s problem, we establish a crisp local characterization of the optimal prices and the corresponding supply response. In the optimal solution, the platform applies different treatments to different locations. In some locations, prices are set so that supply and demand are perfectly matched; overcongestion is induced in other locations, and some less profitable locations are indirectly priced out. To obtain insights on the global structure of an optimal solution, we derive in quasi-closed form the optimal solution for a family of models characterized by a demand shock. The optimal solution, although better balancing supply and demand around the shock, quite interestingly also ends up inducing movement away from it. This paper was accepted by David Simchi-Levi, optimization.

Investigating Uber price surges during a special event in Austin, TX

The purpose of this study was to evaluate the characteristics of Transportation Network Company (TNC) Uber's surge pricing during a special event. Using data collected using Uber's developer API over the 2015 Fourth of July weekend, this research investigated the form of price surge multipliers during periods of high demand. Regression models showed surge price was not correlated with ride wait time for July 3, July 4, or July 5, but it was correlated with ride request time in all three nights. July 4 had the strongest correlation and more instances of surge pricing, and those instances were greater in magnitude that the other evenings studied. This research has practical implications for transportation planners in that it reveals the obscurity of the price surge mechanisms. The unpredictability and lack of transparency surrounding surge pricing poses challenges for those working to incorporate TNCs into a city's transportation operations.

Who Benefits from Surge Pricing?

In the last decade, new technologies have led to a boom in real-time pricing. I analyze the most salient example, surge pricing in ride hailing. Using data from Uber, I develop an empirical model of spatial equilibrium to measure the welfare effects of surge pricing. The model is composed of demand, supply, and a matching technology. It allows for temporal and spatial heterogeneity as well as randomness in supply and demand. I find that, relative to a counterfactual with uniform pricing, surge pricing increases total welfare by 1.59% of gross revenue. Welfare effects differ substantially across sides of the market: rider surplus increases by 5.25% of gross revenue, whereas driver surplus and platform profits decrease by 1.81% and 1.77% of gross revenue, respectively. Riders at all income levels benefit, while disparities in driver surplus are magnified.

Surge Pricing Moves Uber's Driver Partners

We study the impact of dynamic pricing (so-called pricing) on relocation decisions by Uber's driver-partners and the corresponding revenue they collected. Using a natural experiment arising from an outage in the system that produces the surge pricing heatmap for a portion of Uber's driver-partners over 10 major cities, and a difference-in-differences approach, we study the short-run effect that visibility of the surge heatmap has on 1) drivers' decisions to relocate to areas with higher or lower prices and 2) drivers' revenue. We demonstrate that the ability to see the surge heatmap has a statistically significant impact on both outcomes. Ability to see the surge heatmap explains 10%-60% of Uber drivers' self-positioning decisions, attracts drivers toward areas with higher surge prices, and increases drivers' revenue on surged trips by up to 70%. This suggests that dynamic pricing helps drivers move to where riders' demand is largest, and that the resulting reduction in spatial search friction and spatial mismatch improves waiting times and welfare for both riders and drivers.