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The rise of autonomous driving may sharply reshape ride-sourcing markets by influencing drivers' income and participation, customers' choices, ride-sourcing platforms' operations, and governments' regulations. We introduce a model that characterizes a ride-sourcing market with a mixed fleet of non-atomic autonomous vehicles (AVs) and atomic human-driven vehicles (HVs). Human drivers decide whether and where to offer ride-sourcing services independently, while AVs are fully controlled and dispatched by the platform to serve on-demand ride requests that are distributed heterogeneously. We solve for the optimal platform operating strategy and examine the implications of different government regulations on the platform's optimal decisions and the resulting market equilibrium. Although the emergence of AV might undermine human drivers' welfare, it can simultaneously increase platform profit, customer surplus, and social welfare. Moreover, by looking into the regulatory outcomes of a few prevailing regulations (i.e., price cap, minimum wage, minimum fleet size, and maximum fleet size), we find that the policymakers should determine the regulations according to the conditions of labor supply and the development stage of AV technology. • Develop an analytical model to characterize a mixed fleet of autonomous vehicles (AVs) and human-driven vehicles (HVs). • Identify the platform's operating strategies under distinct market structures. • Compare the monopoly optimum with the social optimum. • Examine multiple regulations that can potentially improve social welfare. [ABSTRACT FROM AUTHOR]

• Derive an analytical framework to determine the optimal matching time interval. • Analyze demand and supply curves under immediate matching strategies. • Optimize the platform's fleet size and price strategies. • Archieve joint profit maximization, social welfare, and second-best optimum. • Illustrate properties of pricing strategies under two immediate matching strategies. Supply/demand characteristics and price strategies in the ride-sourcing market are affected by matching strategies. In particular, immediate matching strategies are widely adopted to tackle supply–demand imbalance issues. This paper studies a two-sided ride-sourcing market under two immediate matching strategies: driver-first-come-first-serve and passenger-first-come-first-serve. In the former, the system is considered over-saturated with passengers, such that any driver is matched immediately upon posting their availability. Therefore, the market would be devoid of waiting drivers. In comparison, the latter strategy is oversaturated with drivers, such that any newly arriving passenger is immediately matched, and there are no waiting passengers in the system. We first derive generalized supply and demand functions and prove that the immediate matching strategies are optimal to maximize the number of completed trips under certain conditions based on equilibrium analysis. Then, we characterize the market equilibrium and discuss the properties of the wild-goose-chase phenomenon under two immediate matching strategies. We optimize the ride-sourcing platform's strategies to achieve a joint profit of platform and driver maximization (monopoly optimum), social welfare maximization (social optimum), and second-best optimum (entailing a reservation profit for the platform). Numerical experiments are conducted to demonstrate the effects of user heterogeneity, and the properties of pricing strategies under both matching strategies on the monopolistic and social optimal solution. The gained managerial insights provide suggestions for decision-makers to coordinate supply and demand in the ride-sourcing market. [ABSTRACT FROM AUTHOR]

• Competition between public parking and shared parking platforms is investigated in the presence of indirect network effects. • Indirect network effects positively affect platforms' profits when alternative activity gains of parking owners are large. • Indirect network effects negatively affect platforms' profits when alternative activity gains are small in certain cases. • Managerial implications are discussed to boost the parking platforms' profits. A parking market including conventional public parking and novel shared parking is considered for matching parking demands and supplies. To investigate the competition between the public parking platform and the shared parking platform, a market equilibrium approach is proposed to investigate the interactions among the parking platforms, parking demanders, and parking owners, where indirect network effects referring mutual attractions between demands and supplies are incorporated. Targeted for profit maximization, we first formulate a basic model and derive the equilibrium results of parking platforms' profits. Results show that indirect network effects positively affect the two parking platforms' profits when the equivalent gains from alternative activities for parking owners are large, while negatively affect the profits otherwise in certain cases. Extensions regarding some considerations are then discussed, where Model D is proposed for dual decision of the shared parking platform and Model S is proposed for the social welfare maximization objective of the public parking platform. Comparisons of the three models in terms of equilibrium results are simulated consequently. Managerial implications to enhance the profits of the two parking platforms are further discussed. [ABSTRACT FROM AUTHOR]

• A multimodal multi-provider market equilibrium model is developed. • A congestion alleviation strategy through tolls and emission pricing is modelled. • Emission tax is modelled to model the EV technology penetration. • Private vehicle, walking, public transport, ride-sourcing, and ridesharing modes are modelled. The prominent planning components of recent transport systems are multimodality, pricing, shared mobility, and bundling. These systems consist of several service providers competing for a higher benefit or a lower cost. This paper proposes a generalised multi-modal multi-provider market equilibrium model to evaluate the operation of transport systems. The model includes the modes of a private vehicle, walking, public transport, ride-sourcing, ridesharing-as-driver, and ridesharing-as-rider. The economic behaviours of the service providers and a network operator are modelled using optimisation problems, i.e. their Karush–Kuhn–Tucker (KKT) optimality conditions in combination with user equilibrium conditions of travellers, forming a complementarity formulation for the market at equilibrium. Extensive computational experiments on an abstract network in Sydney demonstrate the use of the model for handling responses of various market players to EVs technological improvements, travel demand changes, restrictions on GHG emissions, and diminishing ridesharing use cultural barriers. [ABSTRACT FROM AUTHOR]

This paper analytically examines the ride-pooling market equilibrium under a single or multiple ride-pooling service(s) considering different pooling sizes (number of riders sharing the same vehicle) and endogenous congestion effect of the ride-pooling fleet, and investigates the optimal operation decisions of the service operator(s) regarding the pricing, fleet size, and pooling size in different administrative and market regimes. We firstly examine the scenario with a single ride-pooling service platform given a specific vehicle type for either profit-maximization or social welfare-maximization (different administrative regimes), and then extend the model to the scenario with two differentiated ride-pooling services (given different vehicle types), where the platforms may either compete or cooperate (different market regimes). We have the following major findings. First, the pooling size should be optimized to accommodate the congestion effect and thus simultaneously increase operator's profit and social welfare. The optimal pooling size balances the marginal pickup time, crowding cost and waiting time cost, and the optimal value is different in different market regimes. Second, the ride-pooling service operator will internalize the congestion effect of providing more vehicles to serve customers, which yields a larger ratio of waiting customers to vacant vehicles (larger demand–supply ratio). Under two differentiated ride-pooling services, in addition to the direct congestion effect of providing more vehicles, its effect on the other operator's decision will also be internalized by the service operator in concern. Our results also suggest that the competition between platforms tends to reduce the waiting customers-vacant vehicles ratio (demand–supply ratio), while the cooperation between or integration of two operators might yield a large ratio. Third, when the endogenous congestion effect of fleet size is zero, the operator's profit at social optimum will always be negative when the matching function exhibits increasing return to scale and the marginal operation cost is less than the average operation cost, which means that the social welfare seeking operator has to be subsidized. When the endogenous congestion effect of the fleet size is positive, the ride-pooling service operator may earn a positive profit at social optimum under certain conditions. This study enhances the understanding in relation to users' reactions to differentiated ride-pooling services, how such services interact with congestion, and how such services can be optimized in different market regimes. • Ride-pooling market equilibrium with flexible pooling size in the presence of congestion effect. • Optimization of pooling size, fleet size, and fare of ride-pooling service. • Differentiated ride-pooling services under competition/cooperation/integration market regimes. • Complex interactions among customer traveling behaviors, service operation decisions and road traffic congestion. [ABSTRACT FROM AUTHOR]

• Describe equilibrium of ride-sourcing market with a number of competitive platforms. • Quantify the price (efficiency gain or loss) of competition and fragmentation. • Examine trends of platform profits and social welfare towards fragmentation level. • Discuss market scenarios with increasing, constant, and decreasing returns to scale. • Our model covers monopoly, duopoly, oligopoly, and perfect competition. This paper proposes a general model for describing the equilibrium state of a ride-sourcing market with an arbitrary number of platforms competing with each other. As the number of platforms increases, the market changes from monopoly to duopoly, oligopoly, and finally perfect competition, bringing about two different effects on system efficiency. On the one hand, as in other service markets, competition in the ride-sourcing markets prevents a monopolist platform from extracting excessive profit by distorting its operating strategies from socially efficient levels. On the other hand, competition between platforms leads to market fragmentation, thereby increasing matching frictions and passengers' waiting time. To well characterize these two opposite driven forces, we develop a game-theoretical model to find out the Nash equilibrium solutions of a competitive ride-sourcing market, at which no platform can increase its profits by unilaterally changing its own strategy. Then we try to quantify the price (efficiency gain or loss) of competition and fragmentation by establishing an upper bound of the inefficiency ratio, i.e., the ratio of social welfare under a social optimum to social welfare under a competitive Nash equilibrium. We show that the results of market equilibrium, including the inefficiency ratio, are jointly governed by the degree of market fragmentation and competition among platforms. In particular, we find that some key market measures, such as consumer surplus, platform profits, social welfare, display diverse trends of changes with respect to the number of platforms, as the on-demand matching between passengers and drivers exhibits increasing, constant, and decreasing returns to scale. [ABSTRACT FROM AUTHOR]

• Nash and Stackelberg models to capture the interactions of DNO and SEV operator. • A best response-based algorithm is proposed to solve the bilevel problem. • Supply-demand equilibrium existence is proved and uniqueness is numerically tested. • Cruising traffic is increased with medium travel demand while reduced in other cases. This paper considers a group of shared electric vehicles (SEV) that provide transport and energy services through a ride-sourcing platform. Under the novel business model, the transport and power networks are coupled and the resulting market equilibrium depends on the pricing strategies of the distribution network operator (DNO) and the SEV platform operator. To capture the interaction between the DNO and the SEV platform operator, we develop two market competition models, including (a) a Stackelberg competition model where the DNO makes the decision first and the SEV operator reacts accordingly, and (b) a Nash competition model where the DNO and the SEV operator make decisions simultaneously. The strategic interactions of the DNO and the SEV platform operator are analyzed over a coupled transport and power network, which captures the routing and discharging behaviors of EV drivers, the matching process between passengers and drivers, and the corresponding traveler and power flows. We analytically prove that the supply and demand equilibrium between travelers and EV drivers over the transport network always exists. We also numerically validate that the equilibrium solution between the SEV operator and the DNO is stable and unique. By varying the transport demands, we show that the SEV platform operator, the DNO and EV drivers always benefit from the discharge business even though passengers may be slightly worse off. Surprisingly, we find that integrating the discharging service will have diverse impacts on the cruising traffic depending on the demand levels of the transport service: under low demand level, it will reduce the cruising traffic; under medium demand level, it will increase the cruising traffic; under high demand level, it has no impact on the cruising traffic. [ABSTRACT FROM AUTHOR]

Before having a massive deployment of fully connected and autonomous vehicles (CAVs), CAVs with different automation levels and human-driven vehicles (HVs) will coexist on roads for a long time. To quantify the impacts of CAV technology on vehicle market penetration and travelers' route choices in the future from the perspective of transportation planning, this paper investigates a two-sided market equilibrium problem, which considers the vehicle market on one side and the road traffic equilibrium market on the other side. The two markets interactively affect each other through market penetration, information quality, and spatial distribution of congestion, which are all endogenously determined in our model. In the vehicle market, two-stage decision-making is considered to describe various vehicle choices users may face in the future. In the first stage, users between each origin–destination (OD) pair choose a vehicle type between HV and CAV. In the second stage, CAV users further choose a vehicle automaton level. To account for the similarity of CAVs with different automation levels, we use a nested logit (NL) model to capture the two-stage decision-making. A vehicle choice with a higher market penetration provides higher-quality information, which affects the traffic equilibrium market. For the traffic equilibrium, route choices of users with different information quality are described by a multinomial logit (MNL) model. The spatial distribution of congestion determined by the traffic equilibrium also affects vehicle choices in the vehicle market. Specifically, users with higher quality information are more likely to choose routes with the lowest travel time. Consequently, a vehicle choice with a lower expected travel time attracts more users. The two-sided market equilibrium is formulated as a combined NL-MNL model so as to solve the two interactive market equilibria simultaneously. Then, we explore the properties of the equilibrium state. Sufficient and necessary conditions for the path flow pattern and demand pattern at equilibrium are derived, respectively. Based on the properties of the equilibrium state, we derive an equivalent variational inequality (VI) for the combined NL-MNL model. A new approach is provided to deriving two sufficient conditions, either of which guarantees the uniqueness of the VI solution. To solve the proposed problem efficiently, we develop a path-based modified self-regulated averaging (PMSRA) algorithm embedded with a modified K-shortest path method. Finally, numerical experiments are conducted to analyze the effects of CAV technology and demonstrate algorithm efficiency. Sensitivity analysis of parameters in the algorithm is also performed. Our results show that the market penetration of CAVs at the early stage of introduction is low due to the high purchase cost. With the development of CAV technology and mass production, fully automated CAVs may gradually dominate the market, while partially automated CAVs tend to be squeezed out of the market. In addition, our results reveal that the travel time saving from CAV technology and high-quality information is more pronounced for long trips and congested networks. • Consider the mixed traffic flow of CAVs at various automation levels and HVs. • A combined NL-MNL model for travelers' vehicle choices and route choices. • Interdependence of market penetration, endogenous information quality, and congestion. • Derive an equivalent VI whose solution's uniqueness is proved by a new approach. • Pronounced travel time saving of CAVs for long trips and congested networks. [ABSTRACT FROM AUTHOR]

• Propose an OD competitive network equilibrium model for ride-sourcing market. • Adopt finite-dimensional variational inequality based on non-cooperative game theory. • Incorporate heavy-asset and light-asset transportation network companies (TNCs). • Derive analytical monotonicity of equilibrium optima and sensitivity analysis. • Gain managerial insights for both the government and TNCs. 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. [ABSTRACT FROM AUTHOR]