This paper aims to combine both economic and network user equilibrium for ride-sourcing and ride-pooling services, while endogenously optimizing the pooling sequence of two origin-destination (OD) pairs. With the growing popularity of ride-sourcing and ride-pooling services provided by transportation network companies (TNC), there lacks a theoretical network equilibrium model that accounts for the emerging ride-pooling service, due to the challenge in enumerating all possible combinations of OD pairs pooling and sequencing. This paper proposes a unified network equilibrium framework that integrates three modules, including travelers' modal choice between e-pooling and e-solo services, e-platforms' decision on vehicle dispatching and driver-passenger matching, and network congestion. To facilitate the representation of vehicle and passenger OD flows and pooling options, a layered OD graph is created encompassing ride-sourcing and ride-pooling services over origins and destination nodes. Numerical examples are performed on both small and large road networks to demonstrate the efficiency of our model. The proposed equilibrium framework can efficiently assist policymakers and urban planners to evaluate the impact of TNCs on traffic congestion, and also help TNCs with pricing and fleet sizing optimization.
Today's ride-hailing systems experienced significant growth and ride-pooling promises to allow for efficient and sustainable on-demand transportation. However, efficient ride-pooling requires a large pool of participating customers. To increase the customers' willingness for participation, we study a novel customer-centered pooling (CCP) mechanism, accounting for individual customers' pooling benefits. We study the benefit of this mechanism from a customer, fleet operator, and system perspective, and compare it to existing provider-centered pooling (PCP) mechanisms. We prove that it is individ…
Ride-pooling has become an important service option offered by ride-hailing platforms as it serves multiple trip requests in a single ride. By leveraging customer data, connected vehicles, and efficient assignment algorithms, ride-pooling can be a critical instrument to address driver shortages and mitigate the negative externalities of ride-hailing operations. Recent literature has focused on computationally intensive optimization-based methods that maximize system throughput or minimize vehicle miles. However, individual customers may experience substantial service quality degradation due to…
Online ride-hailing services have become a prevalent transportation system across the world. In this paper, we study a challenging problem of how to direct vacant taxis around a city such that supplies and demands can be balanced in online ride-hailing services. We design a new reward scheme that considers multiple performance metrics of online ride-hailing services. We also propose a novel deep reinforcement learning method named Deep-Q-Network with Action Mask (AM-DQN) masking off unnecessary actions in various locations such that agents can learn much faster and more efficiently. We conduct…
Mobility-on-Demand (MoD) systems have become a fixture in urban transportation networks, with the rapid growth of ride-hailing services such as Uber and Lyft. Ride-hailing is typically complemented with ridepooling options, which can reduce the negative externalities associated with ride-hailing services and increase the utilization of vehicles. Determining optimal policies for vehicle dispatching and pricing, two key components that enable MoD services, are challenging due to their massive scale and online nature. The challenge is amplified when the MoD platform offers exclusive (conventional…
A fundamental question in any peer-to-peer ride-sharing system is how to, both effectively and efficiently, meet the request of passengers to balance the supply and demand in real time. On the passenger side, traditional approaches focus on pricing strategies by increasing the probability of users' call to adjust the distribution of demand. However, previous methods do not take into account the impact of changes in strategy on future supply and demand changes, which means drivers are repositioned to different destinations due to passengers' calls, which will affect the driver's income for a pe…
The taxi business has been overly regulated for many decades. Regulations are supposed to ensure safety and fairness within a controlled competitive environment. By influencing both drivers and riders choices and behaviors, emerging e-hailing services (e.g., Uber and Lyft) have been reshaping the existing competition in the last few years. This study investigates the existing competition between the mainstream hailing services (i.e., Yellow and Green Cabs) and e-hailing services (i.e., Uber) in New York City. Their competition is investigated in terms of market segmentation, emerging demands, …
The presence of uncertainties in the ride-hailing market complicates the pricing strategies of on-demand platforms that compete each other to offer a mobility service while striving to maximize their profit. Looking at this problem as a stochastic generalized Nash equilibrium problem (SGNEP), we design a distributed, stochastic equilibrium seeking algorithm with Tikhonov regularization to find an optimal pricing strategy. Remarkably, the proposed iterative scheme does not require an increasing (possibly infinite) number of samples of the random variable to perform the stochastic approximation,…
The growth of autonomous vehicles, ridesharing systems, and self driving technology will bring a shift in the way ride hailing platforms plan out their services. However, these advances in technology coupled with road congestion, environmental concerns, fuel usage, vehicles emissions, and the high cost of the vehicle usage have brought more attention to better utilize the use of vehicles and their capacities. In this paper, we propose a novel multi-hop ride-sharing (MHRS) algorithm that uses deep reinforcement learning to learn optimal vehicle dispatch and matching decisions by interacting wit…
Shared-ride mobility services that incorporate traveler walking legs aim to reduce vehicle-kilometers-travelled (VKT), vehicle-hours-travelled (VHT), request rejections, fleet size, or some combination of these factors, compared to door-to-door (D2D) shared-ride services. This paper provides a review of shared-ride services with walking legs (SRSWL), particularly the studies in the literature that model the operational problem(s) associated with SRSWL. The paper describes the operational and societal benefits of SRSWL as well as compares the SRSWL to circuitous D2D shared-ride services, ride-h…
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 intege…
This paper presents the behavioral study's results on willingness-to-pay the extra money by the customers for e-groceries deliveries based on crowd-shipping. The proposed methodology was tested for Ukraine, i.e., a developing country where the crowd-shipping services are under development conditions. To account for the behavior complexity of the consumers who have not faced the crowd-shipping services in the past, the choice model was enhanced with a latent variable. The findings indicate the revealed readiness of the e-shoppers to pay extra money for crowd-shipping delivery if it provides mor…
Autonomous vehicles (AVs) are attractive for ride service providers (RSPs) in part because they eliminate the need to compete for human drivers. We investigate a scenario where two RSPs with AVs compete for customers. We model the problem as a game where the RSPs select prices for each origin-destination pair over multiple time periods in an underlying graph representing the customers' desired trips. Each RSP also decides the number of AVs to be stationed at each node at each time period to serve the customers' demands. The number of customers who avail of the service of an RSP depends on the …
