Surge Pricing Research Articles

Prosociality in Microtransit

We study (public) microtransit, a type of transportation service wherein a municipality offers point-to-point rides to residents, for a fixed, nominal fare. Microtransit exemplifies practical resource allocation problems that are often over-constrained in that not all ride requests (pickup and dropoff locations at specified times) can be satisfied or satisfied only by violating soft goals such as sustainability, and where economic signals (e.g., surge pricing) are not applicable—they would lead to unethical outcomes by effectively coercing poor people. We posit that instead of taking rider preferences as fixed, shaping them prosocially will lead to improved societal outcomes. Prosociality refers to an attitude or behavior that is intended to benefit others. This paper demonstrates a computational approach to prosociality in the context of a (public) microtransit service for disadvantaged riders. Prosociality appears as a willingness to adjust one’s pickup and dropoff times and locations to accommodate the schedules of others and to enable sharing rides (which increases the number of riders served with the same resources). This paper describes an interdisciplinary study of prosociality in microtransit between a transportation researcher, psychologists, a social scientist, and AI researchers. Our contributions are these: (1) empirical support for the viability of prosociality in microtransit (and constraints on it) through interviews with drivers and focus groups of riders; (2) a prototype mobile app demonstrating how our prosocial intervention can be combined with the transportation backend; (3) a reinforcement learning approach to model a rider and determine the best interventions to persuade that rider toward prosociality; and (4) a cognitive model of rider personas to enable evaluation of alternative interventions.

Customer Voice on Two-Sided Platforms: The Effect of Surge Pricing on Customer Complaints

Two-sided platforms (e.g., ride-sharing, freelance labor, and lodging) match customers and service providers. Customers can voice their satisfaction level through review/rating systems, which the platforms use to monitor and screen service providers. However, customers may misdirect complaints about platform-level features toward individual service providers. We examine the extent and consequences of this issue in the context of a large ride-sharing platform. We focus on surge pricing as the platform-level feature. Our findings are as follows. First, for an average trip, surge pricing makes it 1.13 times more likely for the driver to receive a complaint (either a low rating or a formal complaint). Second, complaints negatively impact a driver’s future income. Surge-induced complaints offset about 23% of a driver’s immediate gain from the surged fare. Third, after the platform adopted a policy to cap the magnitude of surge prices, the effect of surge on customer complaints reduced. The policy also led to more usage of the platform. The results suggest a close interaction between the pricing and review systems on ride-sharing platforms. More broadly, they point to the importance for online two-sided platforms to identify and address customer complaints about platform-level features that may have been misdirected to service providers. This paper was accepted by Eric Anderson, marketing. Funding: Y. Zhu acknowledges financial support from 3M [Nontenured Faculty Grant]. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2024.4992 .

Advanced Algorithms for Surge Pricing Optimization in Multi-City Ride-Sharing Networks

In the rapidly evolving landscape of ride-sharing services, surge pricing has emerged as a crucial mechanism for managing demand and supply dynamics, particularly in multi-city networks. This study explores advanced algorithms designed to optimize surge pricing strategies in these complex environments. By leveraging machine learning techniques and real-time data analytics, the proposed algorithms enhance pricing efficiency while ensuring fairness and customer satisfaction. The research incorporates a multi-agent system approach, facilitating coordinated pricing decisions across different cities to minimize price discrepancies and improve service availability. Simulations demonstrate the effectiveness of the algorithms in maximizing revenue while mitigating negative customer experiences associated with high surge rates. This work contributes to the existing literature on ride-sharing economics by providing a comprehensive framework for implementing advanced surge pricing algorithms, ultimately aiming to enhance operational efficiency and user satisfaction in multi-city ride-sharing networks.

A dynamic surge pricing model throughout product lifecycle

The shorter lifecycle and faster upgrading of the product make the potential demand change rapidly, thereby pricing based on market changes is critical to increasing profits. In response to changes in the potential demand, we present a dynamic surge pricing model that characterizes sales trajectories and gradient pricing. We introduce the Lotka–Volterra system to construct the sale-forecast system and prove its effectiveness in predicting the product lifecycle curve that reflects the change of the potential demand. In contrast to the linear demand function, we propose a gradient pricing mechanism based on marginal sales and total sales to describe the relationship between price and potential demand throughout the life cycle. Particularly, the dynamic surge pricing model degrades to Cournot model in the maturity phase of the market. We characterize the dynamic equilibrium and conduct the sensitivity analysis of parameters, showing that the dynamic surge pricing model outperforms Cournot model in terms of profit. A numerical example illustrates that the profit of the dynamic surge pricing model is nearly 21.82% higher than that of Cournot model.

Determinants Factor of Passengers’ Propensity to Utilise E-Hailing Services in Kota Kinabalu, Sabah

In the realm of public transportation, e-hailing services have emerged as a prominent mode of transportation in recent years. This study aims to elucidate the complex relationships between four key constructs, namely safety features, fare, ease of usage, and accessibility, in shaping user inclination towards the adoption of e-hailing services. Using the Stated Preference Survey (SPS) method, a meticulously designed online questionnaire was administered to three hundred respondents in Kota Kinabalu. Employing partial least squares-structural equation modelling for analysis, the results highlight the significance of ease of usage and accessibility as robust predictors influencing Kota Kinabalu residents' preference for using e-hailing services. The safety aspect displays notable disparities based on gender, as male and female consumers possess contrasting perspectives regarding the degree of protection provided by e-hailing services. In contrast, the concept of price value has a restricted ability to forecast user propensity, as surge pricing was universally enforced during peak hours in all e-hailing services. The study's findings provide useful insights for e-hailing businesses, indicating strategic enhancements in service delivery to guarantee customer comfort and safety, thereby increasing their service coverage, and attracting a wider user demographic.

Governing work through personal data: The case of Uber drivers in Geneva

This article presents an ethnographic account of the advocacy initiative, conducted by NGO PersonalData.IO and the company Hestia.ai, that seeks to empower gig workers by helping them regain access to their personal data through data access rights, using the European Union General Data Protection Regulation. It is based on a case study of Uber drivers in Geneva that has a worldwide relevance for the gig economy. Previously self-employed, drivers are now classified as employees and their working time and earnings must be calculated according to local labour laws. We contribute to debates on algorithmic management in ride-hailing platforms by focusing on participatory methods of accountability through personal data, from an infrastructural perspective. First, we focus on the nexus between personal data protection and algorithmic management to understand the domination of ride-hailing platforms over the workers’ means of production, i.e., their personal data. We provide empirical transparency on the data structures of Uber for the sake of algorithmic accountability. These structures are utilised for their surge pricing algorithms and ultimately govern the workforce. Second, within a collective process of governance, we built participatory tools and methods for empowering gig workers and data scientists. These are means for calculating earnings and working that made explicit a new social meaning of work, i.e., “lost time between rides”.

Enhancing the Well-Being of Uber Drivers: Insights and Strategies from DiDi

The rise of the sharing economy, epitomized by Uber, has revolutionized work dynamics. Uber, with its allure of flexibility and accessibility, has empowered countless individuals to explore new avenues of employment. However, within this transformative landscape, unique challenges have taken root, particularly affecting those who rely on Uber as their primary source of income. With Uber's driver community now numbering over 5.4 million strong, the imperative to address these challenges has become increasingly pronounced. This study embarks on an exploration of innovative operational solutions, drawing inspiration from the practices of Didi, the ride-sharing juggernaut in China. It addresses income instability through fairer commissions and surge pricing incentives, combats isolation with community-building features, and tackles discrimination with enhanced driver rating systems through different tags. By embracing and implementing these multifaceted strategies, Uber has the potential to fortify its platform, fostering a mutually beneficial ecosystem that not only bolsters the company's growth but also enhances the livelihoods of its dedicated drivers.

Hiding in plain sight: Surge pricing and strategic providers

Many on‐demand service platforms employ surge pricing policies, charging higher prices and raising provider compensation when customer demand exceeds provider supply. There is increasing evidence that service providers understand these pricing policies and strategically collude to induce artificial supply shortages by reducing the number of providers showing as available online. We study a stylized mathematical model of a setting in which an on‐demand service platform determines its pricing and provider compensation policies, anticipating their impact on customer demand and the participation of strategic providers, who might collectively decide to limit the number of providers showing online as available. We find that collusion can substantially harm the platform and customers, especially when the potential demand is large, and the supply of providers in nearby regions is limited. We explore two pricing policies that a platform could employ in the presence of (potential) provider collusion: a bonus pricing policy that offers additional provider payments on top of the regular compensation and the optimal pricing policy that maximizes the platform's expected profit while taking strategic provider behavior fully into consideration. Both policies feature a compensation structure that ensures that total provider earnings increase in the number of providers available, thereby encouraging all providers to offer their service. We show that both policies can effectively mitigate the impact of potential provider collusion, with the bonus pricing policy often performing near‐optimally. As it might be difficult for a platform to accurately estimate the propensity of providers to collude, we numerically evaluate how platform profits are affected if the pricing policy is designed based on possibly incorrect estimates of the providers' propensity to collude. Our observations suggest that a platform should design its pricing policy under the assumption that all providers are strategic and consider collusion, as the losses associated with implementing such a policy in settings with minor risk of collusion are limited, while the potential losses from failing to consider rampant collusion can be significant.

RIde-hail vehicle routing (RIVER) as a congestion game

The RIde-hail VEhicle Routing (RIVER) problem describes how drivers in a ride-hail market form a dynamic routing strategy according to the expected reward in each zone of the market. We model this decision-making problem as a Markov decision process (MDP), and view the drivers as playing an MDP routing game, with “congestion” induced by competitive matching in a zone. The meeting probability (i.e., the probability of successfully picking up a passenger after cruising in a zone for one period) is derived from a physical model, which is specified for street-hail and e-hail service modes separately, and calibrated with simulation data. We define a Wardrop equilibrium for the MDP routing game, and then prove it exists and can be obtained by solving a fixed-point problem. We further show a system optimum – corresponding to the maximum total expected reward accumulated over time by all drivers – can be achieved if drivers make individual routing decisions according to a cooperative reward rather than the personal reward. In other words, the proposed cooperative strategy “decentralizes” the system optimal solution in the MDP routing game. The results from numerical experiments, including a case study of Chicago, indicate the service mode plays a critical role in shaping system performance. While e-hail enjoys a higher fleet utilization rate than street-hail thanks to its more advanced matching technology, it may lead to a more uneven spatial distribution of vacant vehicle supply. As expected, cooperative routing improves system performance in terms of both total reward and equal distribution of supply. Yet, its benefit is much stronger in e-hail than in street-hail, especially when the supply is overly abundant. We also find, when a local demand surge occurs, the cooperative rewards rise in sync, similar to surge pricing on the supply side. Interestingly, the impact of the price surge spreads broadly in space, propagating far beyond the epicenter of the demand surge.

The dark side of surge pricing and the mitigating role of information disclosure

ABSTRACT This research paper investigates the impact of surge pricing, a dynamic pricing strategy that adjusts prices based on real-time demand and supply, on customers’ perception of price fairness. We aim to provide insights for both academic and managerial audiences. Our study focuses on how businesses using surge pricing can minimize negative effects on customer behavior by properly communicating price changes. We conducted two experiments to examine customers’ fairness perception of surge pricing policies. Our findings revealed that customers generally perceive surge pricing as less fair. However, we also discovered that aligning the amount of information shared with the price increase can effectively improve these fairness perceptions. Our research contributes to the literature on price communication by exploring factors that influence consumers’ understanding and acceptance of price changes. Additionally, our findings offer practical guidance for businesses on how to communicate pricing information in a way that customers perceive as fair, ultimately benefiting both companies and their customers.

Sustainability analysis framework for on-demand public transit systems

There is an increased interest from transit agencies to replace fixed-route transit services with on-demand public transits (ODT). However, it is still unclear when and where such a service is efficient and sustainable. To this end, we provide a comprehensive framework for assessing the sustainability of ODT systems from the perspective of overall efficiency, environmental footprint, and social equity and inclusion. The proposed framework is illustrated by applying it to the Town of Innisfil, Ontario, where an ODT system has been implemented since 2017. It can be concluded that when there is adequate supply and no surge pricing, crowdsourced ODTs are the most cost-effective transit system when the demand is below 3.37 riders/km2/day. With surge pricing applied to crowdsourced ODTs, hybrid systems become the most cost-effective transit solution when demand ranges between 1.18 and 3.37 riders/km2/day. The use of private vehicles is more environmentally sustainable than providing public transit service at all demand levels below 3.37 riders/km2/day. However, the electrification of the public transit fleet along with optimized charging strategies can reduce total yearly GHG emissions by more than 98%. Furthermore, transit systems have similar equity distributions for waiting and in-vehicle travel times.

Optimal pricing strategy of an on-demand platform with cross-regional passengers

Recent years have witnessed the emergence and prosperity of online ride-hailing, which empowers the traditional taxi industry with information technology. However, the ride-hailing platform has no direct control over drivers, one of the biggest challenges for the platform is how to balance the supply and the demand. Considering the cross-regional demand and the surge demand, this paper focuses on the pricing strategy of the ride-hailing platform, and further introduces strategies such as bonus incentive and information sharing. We find that when facing demand surging and supply shortage in one region, the platform can adopt surge pricing or bonus incentive in the adjacent region before the demand surges to balance the supply and the demand. Specially, the surge pricing strategy benefits the platform when the surge demand is high or the cross-regional demand is low, but the bonus incentive strategy has the opposite effect. The existence of cross-regional demand improves the performance of the bonus incentive strategy but reduces that of the surge pricing strategy. We further show that the platform prefers bonus incentive to surge pricing when only a small number of drivers are required to move. But bonus incentive always improves social welfare better than surge pricing. Extending to information asymmetry, information sharing can also encourage more drivers to move. However, the effect is limited such that the proportion of drivers who move will not exceed a threshold. The platform also has the incentive to share false information, which makes drivers question the credibility of the shared information. In this case, the surge pricing strategy can be used as a costly signal to verify the credibility of the information.

Surge pricing and consumer surplus in the ride-hailing market: Evidence from China

Surge pricing leads to efficient operations of ride-hailing services, but its implications on consumer welfare are difficult to estimate owing to endogeneity biases resulting from the simultaneity between passenger demand and surge pricing. We address this issue and present a comprehensive study on consumer surplus estimation in the ride-hailing market using multi-day data of around two hundred thousand daily ride-hailing trips in Nanjing, China. We estimate a relationship between surge level and demand to obtain the elasticity of passenger demand relative to the surge level. Potential endogeneity issues are addressed by applying the dynamic panel generalized method of moments estimation. The price elasticity estimates are then used to compute the consumer surplus. Our results provide three important insights. First, the magnitude of the surge elasticity of demand during peak hours is the lowest compared to other periods. Second, the magnitude of elasticity is higher in urban areas during the off-peak than in suburban areas. Third, the consumer surplus per kilometer is lower in urban areas due to longer trip lengths at higher surge levels than in suburban areas. The consumer surplus generated by a ride-hailing company per yuan (Y) ranges between Y0.47 and Y0.89. Our findings are relevant for both ride-hailing companies and policymakers. While ride-hailing companies can decide areas, time of the day, and the extent of surge pricing based on the spatiotemporal heterogeneity in price elasticity, government agencies can use consumer surplus as a yardstick to devise pricing regulations.

A data fusion approach for ride-sourcing demand estimation: A discrete choice model with sampling and endogeneity corrections

Ride-sourcing services offered by companies like Uber and Didi have grown rapidly in the last decade. Understanding the demand for these services is essential for planning and managing modern transportation systems. Existing studies develop statistical models for ride-sourcing demand estimation at an aggregate level due to limited data availability. These models lack foundations in microeconomic theory, ignore competition of ride-sourcing with other travel modes, and cannot be seamlessly integrated into existing individual-level (disaggregate) activity-based models to evaluate system-level impacts of ride-sourcing services. In this paper, we present and apply an approach for estimating ride-sourcing demand at a disaggregate level using discrete choice models and multiple data sources. We first construct a sample of trip-based mode choices in Chicago, USA by enriching household travel survey with publicly available ride-sourcing and taxi trip records. We then formulate a multivariate extreme value-based discrete choice model with sampling and endogeneity corrections to account for the construction of the estimation sample from multiple data sources and endogeneity biases arising from supply-side constraints and surge pricing mechanisms in ride-sourcing systems. Our analysis of the constructed dataset reveals insights into the influence of various socio-economic, land use and built environment features on ride-sourcing demand. We also derive elasticities of ride-sourcing demand relative to travel cost and time. Finally, we illustrate how the developed model can be employed to quantify the welfare implications of ride-sourcing policies and regulations such as terminating certain types of services and introducing ride-sourcing taxes.

Spatiotemporal Analysis of the Relationships Between Pricing Strategies and Willingness to Share for Ridesourcing Modes

Willingness to share trips and surge pricing schemes remain unexplored areas in ridesourcing research. This study first conducts an explorative spatiotemporal analysis on both features’ interdependencies, that is, willingness to share and surge pricing. The willingness-to-share behavior is analyzed with respect to its econometric and psychological aspects. Mile-price and hour-of-day are used as proxies for the elasticity and psychological perception of security to share rides with strangers, respectively. Surge pricing is discussed within the classic economic theory on supply and demand dynamics, as well as other factors such as traffic conditions and trip length. The willingness-to-share pattern mined in this analysis is further analyzed in a behavioral market segmentation context to identify the type of underlying existing spatiotemporal trends. The proposed methodological protocol builds on mining spatial heterogeneity and temporal trends to provide a comprehensive understanding of the willingness-to-share behavior. The study uses the large ridesourcing dataset collected in the City of Chicago to showcase the implementation and provides a critical and contextual discussion on the behavioral segmentation pattern and the underlying urban socioeconomic fabric. Two types of oscillating and sporadic trends were captured in statistically significant hot and cold willingness-to-share spots that relate to more and less socially disadvantaged community areas, respectively. Two regression models were developed to identify the determinants of the observed trends. The non-white population percentage, percentage of low-income households, and a younger population exhibit significant positive relationships with more willingness-to-share ridesourcing trips.

Blockchain-Based Decentralized Cab Aggregator

Abstract: A Blockchain-based decentralized cab aggregator system that aims to revolutionize the traditional ride-hailing industry. The system leverages the transparency and security features of blockchain technology to create a thrustless environment where riders and drivers can connect without the need for intermediaries. The decentralized platform also eliminates the problem of surge pricing and ensures that drivers are paid fairly. In addition, the use of smart contracts ensures that all parties fulfil their obligations, thereby reducing the risk of fraud and disputes. The project promises to create a more equitable and efficient cab aggregation system that benefits all stakeholders involved.

On the Relocation Behavior of Ride-sourcing Drivers

ABSTRACT Ride-sourcing drivers, as individual service suppliers, can freely adopt their own relocation strategies, including waiting, cruising freely, or following the platform recommendations. These decisions substantially impact the balance between supply and demand, and consequently affect system performance. We conducted a stated choice experiment to study the searching behaviour of ride-sourcing drivers and examine novel policies. A unique dataset of 576 ride-sourcing drivers working in the US was collected and a choice modelling approach was used to estimate the effects of multiple existing and hypothetical attributes. The results suggest that ride-sourcing drivers’ relocation strategies vary considerably between different drivers groups. Surge pricing significantly stimulates drivers to head toward the designated areas. However, the distance between the driver’s location and surge or high-demand areas demotivates them from following the platform repositioning recommendations. We discuss the implications of our findings for various platform policies on real-time information sharing and platform repositioning guidance.

The Effects of Surge Pricing and Service Quality on Consumer Satisfaction and Retention in Pakistan

The Effects of Surge Pricing and Service Quality on Consumer Satisfaction and Retention in Pakistan

Demand management for smart transportation: A review

The current revolutions of automation, electrification, and sharing are reshaping the way we travel, with broad implications for future mobility management. While much uncertainty remains about how these disruptive technologies would exactly impact demand for future mobility and enhancement of transportation supply, it is clear that innovative demand management is equally important as smart supply technology development in solving worsening traffic problems in big cities. In this work, we will discuss the significances, opportunities, and challenges of demand management in the era of smart transportation. Innovative ways of travel demand management for road transportation, public transit, and smart mobility are described, including tradable travel credit schemes for road congestion mitigation, revenue-preserving and Pareto-improving strategies for peak-hour transit demand management, and a novel reward scheme integrated with surge pricing in a ride-sourcing market.

The effects of surge pricing on driver behavior in the ride‐sharing market: Evidence from a quasi‐experiment

AbstractSurge pricing has been used to coordinate supply and demand in the ride‐sharing industry, but its causal effects on driver behavior remain unclear. This motivates us to examine how surge pricing causally affects driver earnings and labor supply by leveraging a unique quasi‐experiment, in which a leading ride‐sharing company in China introduced surge pricing in two cities at different times. Using a difference‐in‐differences design with the causal forest method, we find that surge pricing led to increases in drivers' weekly revenue. Decomposing the weekly revenue into “intensive margin” and “extensive margin” factors, we discover two countervailing effects at play: a cherry‐picking effect and a competition effect, and the daily revenue decreased because the latter dominated. Consequently, the increased weekly revenue can be explained by the extensive margin: drivers worked on more days to compensate for the decreased daily revenue, a result consistent with the income targeting behavior. Finally, we examine heterogeneous treatment effects across drivers, and find that surge pricing enticed more part‐time drivers to flood the market and crowd out full‐time drivers, and that the increase in the drivers' weekly revenue was primarily driven by part‐time drivers. Therefore, the benefit of surge pricing was unevenly distributed across drivers.