Ride-sourcing Platforms Research Articles

Competition equilibrium of ride-sourcing platforms and optimal government subsidies considering customers’ green preference under peak carbon dioxide emissions

With the proposal of the “peak carbon dioxide emissions” (carbon peak), committing to carbon emission reduction in the ride-sourcing market has become the focus of the sustainable development of the platform. Under the background of “carbon peak”, this paper constructs a duopoly competition model of ride-sourcing market considering government subsidies and customers' green preference. One platform adopts the self-operated mode of purchasing new energy vehicles (NEVs), and the other platform adopts the franchised mode of driver owned vehicle. We find that: First, while one platform maintains the competitive equilibrium decisions, no matter how the other platform adjusts pricing and fleet size decisions, the equilibrium will be disrupted and profits will be lost. Second, the customers' green preference and subsidy preference are more favorable to the self-operated platform than to the franchised platform. Especially when customers' subsidy preference is weak, the effect of green preference is more obvious. Third, with the increase in the total investment in government subsidies, the optimal ride fare rate of the two platforms will decrease, and the franchised platform will curtail the fleet size. Ultimately, government subsidies will be more conducive to the benefit of the self-operated platform and improve social welfare. Customers’ green preference and government subsidies will both contribute to the goal of carbon peak. The extension part considers the realities of green credit policy and a general case of the ride-sourcing market. This study provides management suggestions for promoting the sustainable development of the ride-sourcing market.

Coordinating supply and demand in ride-sourcing markets with pre-assigned pooling service and traffic congestion externality

This paper proposes an equilibrium model to describe a ride-sourcing market with both ride-pooling (RP) and non-pooling (NP) services considering traffic congestion externality. In particular, we develop a deductive model to approximate the pool-matching probability and passengers’ expected waiting time under a pre-assigned pool-matching mechanism with meeting points, which is being used in products like Uber Express Pool. Using the model, we derive the equilibrium properties and examine the optimal operating strategies for achieving two market scenarios, including the monopoly optimum and social optimum. In contrast to the traditional wisdom, we find that the market could exist in one or multiple equilibria, depending on the trip fare difference between RP and NP services. Additionally, we show that average sojourn time first decreases in the vehicle fleet size (when traffic congestion is mild) and then increases in the vehicle fleet size (when traffic congestion is heavy), exhibiting a “smiling curve”. It is found that the monopoly optimum solution is always located in the downward side of the “smiling curve”, which implies the ride-sourcing platform will self-regulate its fleet size to avoid heavy traffic congestion. We also discuss the impacts of potential labor supply and background traffic on the platform’s optimal operating strategy and its optimal profit. This study gains interesting managerial insights that help understand how ride-sourcing platforms determine optimal operating strategies for both RP and NP services by considering traffic congestion externality.

Vehicle Redistribution in Ride-Sourcing Markets Using Convex Minimum Cost Flows

Ride-sourcing platforms often face imbalances in the demand and supply of rides across areas in their operating road-networks. As such, dynamic pricing methods have been used to mediate these demand asymmetries through surge price multipliers, thus incentivising higher driver participation in the market. However, the anticipated commercialisation of autonomous vehicles could transform the current ride-sourcing platforms to fleet operators. The absence of human drivers fosters the need for empty vehicle management to address any vehicle supply deficiencies. Proactive redistribution using integer programming and demand predictive models have been proposed in research to address this problem. A shortcoming of existing models, however, is that they ignore the market structure and underlying customer choice behaviour. As such, current models do not capture the real value of redistribution. To resolve this, we formulate the vehicle redistribution problem as a non-linear minimum cost flow problem which accounts for the relationship of supply and demand of rides, by assuming a customer discrete choice model and a market structure. We demonstrate that this model can have a convex domain, and we introduce an edge splitting algorithm to solve a transformed convex minimum cost flow problem for vehicle redistribution. By testing our model using simulation, we show that our redistribution algorithm can decrease wait times by more than 50%, increase profit up to 10% with less than 20% increase in vehicle mileage. Our findings outline that the value of redistribution is contingent on localised market structure and customer behaviour.

Real-Time Driver-Request Assignment in Ridesourcing

Online on-demand ridesourcing service has played a huge role in transforming urban transportation. A central function in most on-demand ridesourcing platforms is to dynamically assign drivers to rider requests that could balance the request waiting times and the driver pick-up distances. To deal with the online nature of this problem, existing literature either divides the time horizon into short windows and applies a static offline assignment algorithm within each window or assumes a fully online setting that makes decisions for each request immediately upon its arrival. In this paper, we propose a more realistic model for the driver-request assignment that bridges the above two settings together. Our model allows the requests to wait after their arrival but assumes that they may leave at any time following a quitting function. Under this model, we design an efficient algorithm for assigning available drivers to requests in real-time. Our algorithm is able to incorporate future estimated driver arrivals into consideration and make strategic waiting and matching decisions that could balance the waiting time and pick-up distance of the assignment. We prove that our algorithm is optimal ex-ante in the single-request setting, and demonstrate its effectiveness in the general multi-request setting through experiments on both synthetic and real-world datasets.

Optimal prices for ridesourcing in the presence of taxi, public transport and car competition

Ridesourcing platforms have acquired an important role as an alternative travel mode in almost all major cities in the world. These new mobility applications offer benefits for users over traditional taxi and public transport alternatives. However, there is mounting evidence that ridesourcing worsens traffic congestion. Given this potential negative impact and the complicated relationship between travel modes, it is unclear how policymakers should respond to the appearance of this new transport option. In this paper, we present a social welfare maximization model with four modes (automobile, taxi, buses and ridesourcing services) to derive the optimal first-best and second-best fares, considering the congestion externality generated by each mode. We apply this model to Santiago, Chile, using available parameter estimates to derive optimal fares with a novel inverse demand system (Inverse Product Differentiation Logit model). The results indicate that ridesourcing fares should be 29% higher per ride in the first-best scenario and 59% higher in the second-best scenario compared to current levels. However, our second-best scenario (in which buses and taxis have fares given by current levels and there is no congestion pricing for cars) reaches only 18% of the welfare gains from a first-best scenario. Sensitivity analysis shows that these results are not sensitive to several key assumptions, however they are sensitive to the parametrization of the flow-delay function in the second-best scenario. Our simulations also show that the optimal surcharge should be slightly higher if the average occupancy rate for ridesourcing services increases. This result is due to the higher overall use of this service, as the average fare per passenger decreases when more passengers ride together.

Shared parking for ride-sourcing platforms to reduce cruising traffic

Ride-sourcing platforms create cruising traffic that exacerbates road congestion in large cities. This paper considers shared parking services for ride-sourcing vehicles to reduce the cruising traffic. We propose a novel business model that integrates shared parking services into the ride-sourcing platform. In the proposed business model, the platform (a) provides app-based interfaces to connect passengers, drivers and garage operators (private or commercial garages); (b) matches passengers to ride-sourcing vehicles for ride services; (c) matches idle vehicles to vacant parking spaces to reduce cruising traffic; and (d) determines the ride fare, driver payment, and parking rates to maximize its profit. We model the integrated platform as a three-sided market (i.e., passenger, driver and garage) that involves two matching processes: passenger-driver matching and driver-garage matching. An economic equilibrium model is proposed to characterize the incentives of passengers, drivers, garage operators, and the ride-sourcing platform. Optimal pricing strategies and the corresponding market outcomes are derived by solving the platform’s profit maximization problem. Based on the model, we show that integrated parking and ride-sourcing service can offer a Pareto improvement: in certain regime, it leads to higher passenger surplus, higher driver surplus, higher garage surplus, higher platform profit, and reduced cruising traffic. Further, we investigate the impacts of matching efficiency between drivers and garages on the proposed business model. We show that a higher matching efficiency always leads to increased platform profit and driver surplus, but it may not necessarily benefit passengers or garage operators in all cases. The above insights are validated through realistic numerical studies for San Francisco.

Competition Equilibrium of Ride-Sourcing Platforms and Optimal Government Subsidies Considering Customers’ Green Preference Under Peak Carbon Dioxide Emissions

Competition Equilibrium of Ride-Sourcing Platforms and Optimal Government Subsidies Considering Customers’ Green Preference Under Peak Carbon Dioxide Emissions

Competition and third-party platform-integration in ride-sourcing markets

Recently, some third-party integrators attempt to integrate the ride services offered by multiple independent ride-sourcing platforms. Accordingly, passengers can request ride through the integrators and receive ride service from any one of the ride-sourcing platforms. This novel business model, termed as third-party platform-integration in this work, has potentials to alleviate market fragmentation cost resulting from demand splitting among multiple platforms. Although most existing studies focus on operation strategies for one single monopolist platform, much less is known about the competition and platform-integration and their implications on operation strategy and system efficiency. In this work, we propose mathematical models to describe the ride-sourcing market with multiple competing platforms and compare system performance measures between two market scenarios (i.e., with and without platform-integration) at Nash equilibrium and social optimum. We find that platform-integration can increase the total realized demand and social welfare at Nash equilibrium and social optimum, but may not necessarily generate a greater profit when the vehicle supply is sufficiently large, and/or the market is considerably fragmented. We show that the market with platform-integration generally achieves greater social welfare due to two reasons. First, platform-integration can generate a thicker market and reduce matching frictions; second, multiple ride-sourcing platforms are competing for passengers by setting lower trip fares independently.

Modeling network equilibrium of competitive ride-sourcing market with heterogeneous transportation network companies

The ride-sourcing market (RSM) consists of three parts: ride-sourcing platforms or transportation network companies (TNCs) that set the price for passengers and wage of drivers, passengers requesting on-demand ride services, and drivers offering the services. This paper proposes an origin–destination (OD) competitive network equilibrium model for RSM with heterogeneous TNCs. A finite-dimensional variational inequality is adopted to formulate the RSM competitive network equilibrium model based on the non-cooperative game theory. It assumes that each TNC and individual drivers aim to maximize profits or earnings, while passengers of demand regions seek utility maximization. The monotonicity of equilibrium optima is analytically derived, and the sensitivity analysis of parameters is theoretically conducted and numerically illustrated. The managerial insights are gained both for the government and TNCs. The analytical findings include: (a) market equilibrium price increases and market equilibrium travel quantity decreases in either the transaction cost, driver cost, or TNC cost; (b) market equilibrium price decreases in the number of light-asset TNCs, while the market equilibrium travel quantity increases in it; and (c) raising RSM entry requirements improves the market equilibrium price and reduces travel demand on RSM. The numerical results illustrate the plausibility and flexibility of the analytical model. The proposed model is general and capable of analyzing the network equilibrium with multi-type drivers, demand regions, and heterogeneous ride-sourcing platforms.

Book-ahead & supply management for ridesourcing platforms

Ridesourcing platforms recently introduced the “schedule a ride” service where passengers may reserve (book-ahead) a ride in advance of their trip. Reservations give platforms precise information that describes the start time and location of anticipated future trips; in turn, platforms can use this information to adjust the availability and spatial distribution of the driver supply. In this article, we propose a framework for modeling/analyzing reservations in time-varying stochastic ridesourcing systems. We consider that the driver supply is distributed over a network of geographic regions and that book-ahead rides have reach time priority over non-reserved rides. First, we propose a state-dependent admission control policy that assigns drivers to passengers; this policy ensures that the reach time service requirement would be attained for book-ahead rides. Second, given the admission control policy and reservations information in each region, we predict the “target” number of drivers that is required (in the future) to probabilistically guarantee the reach time service requirement for stochastic non-reserved rides. Third, we propose a reactive dispatching/rebalancing mechanism that determines the adjustments to the driver supply that are needed to maintain the targets across regions. For a specific reach time quality of service, simulation results using data from Lyft rides in Manhattan exhibit how the number of idle drivers decreases with the fraction of book-ahead rides. We also observe that the non-stationary demand (ride request) rate varies significantly across time; this rapid variation further illustrates that time-dependent models are needed for operational analysis of ridesourcing systems.

Exploring multi-homing behavior of ride-sourcing drivers via real-world multiple platforms data

Multi-homing behavior refers to the behavior that ride-sourcing drivers simultaneously register and sequentially provide services on multiple ride-sourcing platforms. The multi-homing behavior of ride-sourcing drivers significantly impacts the competition among multiple ride-sourcing platforms in a competitive market. To better understand the multi-homing behavior, we present exploratory evidence on the factors that influence drivers' platform switching behavior. The RF-MNL (random forest multinomial logistic regression) framework is applied to analyze multi-homing driver behavior in a competitive ride-sourcing market. Multinomial logistic regression (MLR) is adopted to model the platform switching behavior of multi-homing drivers. The random forest is employed to seek the best combination of variables for the MLR model, which is calibrated by using the one-month multi-platform ride-sourcing data in Hangzhou, China. A variety of explanatory variables that influence ride-sourcing drivers' multi-homing behavior are estimated. The results show that the driver's socio-demographic characteristics, income level, bonus income (e.g., long-distance price rise), and work time related factors (e.g., the time gap of order dispatching, and wait time) play an essential role in determining the platform switching decision. This study corroborates the evidence of significant factors that impact drivers' switching from one ride-sourcing platform to another, which can support decision-making for ride-sourcing platforms to attract drivers serving the platform exclusively. We also examine how heterogeneity in drivers' multi-homing tendencies affects the platform's policy. To our best knowledge, this paper is one of the first quantitative studies that empirically reveal the commonly observed multi-homing behavior of ride-sourcing drivers by exploring real-world city-wide data collected on multiple platforms.

Graph-Based Equilibrium Metrics for Dynamic Supply–Demand Systems With Applications to Ride-sourcing Platforms

How to dynamically measure the local-to-global spatio-temporal coherence between demand and supply networks is a fundamental task for ride-sourcing platforms, such as DiDi. Such coherence measurement is critically important for the quantification of the market efficiency and the comparison of different platform policies, such as dispatching. The aim of this paper is to introduce a graph-based equilibrium metric (GEM) to quantify the distance between demand and supply networks based on a weighted graph structure. We formulate GEM as the optimal objective value of an unbalanced optimal transport problem, which can be formulated as an equivalent linear programming and efficiently solved. We examine how the GEM can help solve three operational tasks of ride-sourcing platforms. The first one is that GEM achieves up to 70.6% reduction in root-mean-square error over the second-best distance measurement for the prediction accuracy of order answer rate. The second one is that the use of GEM for designing order dispatching policy increases drivers’ revenue for more than 1%, representing a huge improvement in number. The third one is that GEM can serve as an endpoint for comparing different platform policies in AB test. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.

Optimal contract design for ride-sourcing services under dual sourcing

To cope with the uncertainty of labor supply from freelance/self-scheduling drivers, some ride-sourcing platforms recruit contractual drivers, who are paid a fixed salary for pre-specified work schedules. This paper develops an aggregate modeling framework to examine the practicability of such a dual-sourcing strategy. We investigate the optimal contract design of dual sourcing under demand uncertainty, varying price sensitivity of freelancers, and heterogeneity in drivers’ risk attitude. Our results uncover the conditions under which dual sourcing benefits both the platform and drivers. We show that the platform’s staffing and pricing decisions are most responsive to freelancers’ price sensitivity. When the price sensitivity stays adequately low, both the platform and drivers can be better off under dual sourcing compared to the self-scheduling counterpart. On the contrary, with moderate price sensitivity, freelancers will be made worse off by dual sourcing. The dual-sourcing contracts are most effective in markets where drivers are risk-averse.

Predicting origin-destination ride-sourcing demand with a spatio-temporal encoder-decoder residual multi-graph convolutional network

With the rapid development of mobile-internet technologies, on-demand ride-sourcing services have become increasingly popular and largely reshaped the way people travel. Demand prediction is one of the most fundamental components in supply-demand management systems of ride-sourcing platforms. With an accurate short-term prediction for origin-destination (OD) demand, the platforms make precise and timely decisions on real-time matching, idle vehicle reallocations, and ride-sharing vehicle routing, etc. Compared to the zone-based demand prediction that has been examined in many previous studies, OD-based demand prediction is more challenging. This is mainly due to the complicated spatial and temporal dependencies among the demand of different OD pairs. To overcome this challenge, we propose the Spatio-Temporal Encoder-Decoder Residual Multi-Graph Convolutional network (ST-ED-RMGC), a novel deep learning model for predicting ride-sourcing demand of various OD pairs. Firstly, the model constructs OD graphs, which utilize adjacent matrices to characterize the non-Euclidean pair-wise geographical and semantic correlations among different OD pairs. Secondly, based on the constructed graphs, a residual multi-graph convolutional (RMGC) network is designed to encode the contextual-aware spatial dependencies, and a long-short term memory (LSTM) network is used to encode the temporal dependencies, into a dense vector space. Finally, we reuse the RMGC networks to decode the compressed vector back to OD graphs and predict the future OD demand. Through extensive experiments on the for-hire-vehicles datasets in Manhattan, New York City, we show that our proposed deep learning framework outperforms the state-of-arts by a significant margin.

On ride-pooling and traffic congestion

Ridesourcing platforms, such as Uber, Lyft and Didi, are now launching commercial on-demand ride-pooling programs that enable their affiliated drivers to serve two or more passengers in one ride. It is generally expected that successful designs of ride-pooling programs can reduce the required vehicle fleet size, and achieve various societally beneficial objectives, such as alleviating traffic congestion. The reduction in traffic congestion can in turn save travel time for both ridesourcing passengers and normal private car users. However, it is still unclear to what extent the implementation of ride-pooling affects traffic congestion and riders’ travel time. To this end, this paper establishes a model to describe the ridesourcing markets with congestion effects, which are explicitly characterized by a macroscopic fundamental diagram. We compare the time cost (sum of travel time and waiting time) of ridesourcing passengers and normal private car users (background traffic) in the ridesourcing markets without ride-pooling (each vehicle serves one passenger) and with ride-pooling (each vehicle serves one or more passengers). It is found that, a win-win situation can be achieved under some scenarios such that the implementation of on-demand ride-pooling reduces the time cost for both ridesourcing passengers and private car users. Furthermore, we find that the matching window is a key decision variable the platform leverages to affect the stationary equilibrium state. As the matching window increases, passengers are expected to wait for longer time, but the pool-matching probability (the proportion of passengers who are pool-matched) increases, which further alleviates traffic congestion and in turn reduces passengers’ travel time. It is interesting to find that, there is a globally optimal matching window for achieving the minimum time cost for ridesourcing passengers in the normal flow regime.

Economic modeling and analysis of the ride-sourcing market considering labor supply

Mobile technology development has led to the upsurge of app-based ride-sourcing platforms on the market. This paper analyzes the pricing strategies of ride-sourcing platforms. Economic modeling is used to propose an aggregate model, which includes the demand, labor supply, and the matching between demand and supply. The interactive relationship between labor supply and drivers' revenue is explicitly considered. The market is ultimately modeled as a system of nonlinear equations. We prove the existence and stability of market equilibrium and propose an approximate iterative algorithm to solve for the equilibrium. The algorithm is verified through a numerical experiment. A partial-derivative-based sensitivity analysis is conducted to quantitatively evaluate the impacts of platform pricing strategies on ride-sourcing market performance, based on which we examine desirable price perturbations from the perspectives of customers, drivers, and the platform, respectively. The triple-win Pareto improvement condition is also numerically analyzed.

Modeling and managing heterogeneous ride-sourcing platforms with government subsidies on electric vehicles

This paper formulates duopoly competition between two non-cooperative heterogeneous ride-sourcing platforms considering the adoption of electric vehicles (EV) and government subsidies on EVs. One ride-sourcing platform adopts an EV asset-heavy strategy by undertaking EV depreciation costs, while the other ride-sourcing platform adopts an asset-light strategy by hiring drivers who own vehicles. The first-order conditions of each platform's pricingunder competitive equilibrium are derived based on a game-theoretical model that involves the stakeholders (i.e., riders, drivers, and ride-sourcing platforms). Based on the modeling framework ofduopoly competition between two heterogeneous ride-sourcing platforms, this paper proposes an optimization model with social welfare maximization for the determination of governmentsubsidy strategies. We conduct numerical illustrations to demonstrate how governmental subsidies on EV purchase and charging stations impact the endogenous variables in equilibrium (e.g., price for riders, wage for drivers, and market share for each platform) under different formulations of riders' waiting time cost function, which are increasing returns to scale. Also, a specialmodel with a fixed commission ratio is discussed. The results provide suggestions for decision-makers on how to allocate subsidy within the budget constraint.

Pricing and equilibrium in on-demand ride-pooling markets

With the recent rapid growth of technology-enabled mobility services, ride-sourcing platforms, such as Uber and DiDi, have launched commercial on-demand ride-pooling programs that allow drivers to serve more than one passenger request in each ride. Without requiring the prearrangement of trip schedules, these programs match on-demand passenger requests with vehicles that have vacant seats. Ride-pooling programs are expected to offer benefits for both individual passengers in the form of cost savings and for society in the form of traffic alleviation and emission reduction. In addition to some exogenous variables and environments for ride-sourcing market, such as city size and population density, three key decisions govern a platform's efficiency for ride-pooling services: trip fare, vehicle fleet size, and allowable detour time. An appropriate discounted fare attracts an adequate number of passengers for ride-pooling, and thus increases the successful pairing rate, while an appropriate allowable detour time prevents passengers from giving up ride-pooling service. This paper develops a mathematical model to elucidate the complex relationships between the variables and decisions involved in a ride-pooling market. We find that the monopoly optimum, social optimum and second-best solutions in both ride-pooling and non-pooling markets are always in a normal regime rather than the wild goose chase (WGC) regime—an inefficient equilibrium in which drivers spend substantial time on picking up passengers. Besides, in general, a unit decrease in trip fare in a ride-pooling market attracts more passengers than would a non-pooling market, because it not only directly increases passenger demand due to the negative price elasticity, but also reduces actual detour time, which in turn indirectly increases ride-pooling passenger demand. As a result, we prove that monopoly optimum, social optimum and second-best solution trip fares in a ride-pooling market are lower than that in a non-pooling market under certain conditions. These theoretical findings are further verified by a set of numerical studies.

Optimal Contract Design for Ride-Sourcing Services Under Dual Sourcing

To cope with the uncertainty of labor supply from freelance/self-scheduling drivers, some ride-sourcing platforms recruit contractual drivers, who are paid a fixed salary for prespecified work schedules. This paper develops an aggregate modeling framework to examine the practicability of such a dual-sourcing strategy. We investigate the optimal contract design of dual sourcing under demand uncertainty, varying price sensitivity of freelancers, and heterogeneity in drivers’ risk attitude. Our results uncover the conditions under which dual sourcing benefits both the platform and drivers. We show that the platform’s staffing and pricing decisions are most responsive to freelancers’ price sensitivity. When the price sensitivity stays adequately low, both the platform and drivers can be better off under dual sourcing compared to the self-scheduling counterpart. On the contrary, with moderate price sensitivity, freelancers will be made worse off by dual sourcing. The dual-sourcing contracts are most effective in markets where drivers are risk-averse.

Understanding ride-sourcing drivers' behaviour and preferences: Insights from focus groups analysis

Ride-sourcing has recently been at the centre of attention as the most disruptive mode of transport associated with the so-called shared mobility era. Drivers, riders, the platform, policymakers, and the general public are considered as the main stakeholders of the system. While ride-sourcing platforms have been growing, so did the heightened tension between them and their drivers. That is why understanding drivers' behaviour and preferences is of key importance to ride-sourcing companies in managing their relationship with drivers (also known as driver-partners) and in retaining them in the presence of competence. Ride-sourcing drivers are not only chauffeurs but fleet owners. They can make various operational and tactical decisions that directly influence other stakeholders and the transport system performance as a whole. Conducting a series of focus groups with ride-sourcing drivers in the Netherlands, we have studied their opinions about the system functionalities as well as their possible interactions with the platform and wishes for changes. The focus group results suggest that the main decisions of drivers, which are ride acceptance, relocation strategies, working shift and area in which to work, could be affected by many elements depending on platform strategies, drivers' characteristics, riders' attributes, and exogenous factors. We find that part-time and full-time drivers, as well as experienced and beginning drivers, are characterized by distinctive behaviour. Flexibility and freedom were mentioned as the key reasons for joining the platform while an unfair reputation system, unreliable navigation algorithm, high competition between drivers, passenger-oriented platform, high-commission fee, and misleading guidance were acknowledged as being the main system drawbacks. Based on our findings, we propose a conceptual model that frames the relationship between the tactical and operational decisions of drivers and related factors.