Ride-matching Problem Research Articles

A stochastic ridesharing user equilibrium model with origin-destination-based ride-matching strategy

Ridesharing, as an emerging mode of modern urban transportation, has gained widespread popularity due to its ability to improve convenience and reduce the expenses in daily travel. To capture its impact on the network-wide traffic flow pattern, this paper develops a new variational inequality (VI) formulation for modeling the stochastic ridesharing user equilibrium (SRUE) problem considering an origin–destination (O-D) -based ride-matching strategy. The proposed SRUE problem is formulated based on a hierarchical logit choice model to depict the multi-stage decision-making (i.e., role choice, ride-matching, and route choice) of the ridesharing travelers. The SRUE model also reflects a two-sided market equilibrium considering the interaction effect between the ridesharing market and the road traffic equilibrium market. The logit choice model captures the ride-matching problem between the drivers and the passengers traveling on the same/different O-D pairs. Moreover, the lower- and upper-bound constraints for a ridesharing driver picking up passengers are established at the O-D level. For solving the SRUE model with O-D-based ride-matching constraints, the Lagrangian dual method (LDM) is adopted by incorporating the Barzilai-Borwein (BB) step size to update the dual variables, which accelerates the convergence of the LDM. Three numerical examples are conducted to illustrate the features of the proposed SRUE model and demonstrate the performance of the solution algorithm.

Ride-matching for the ride-hailing platform with heterogeneous drivers

We study a ride-matching problem in a ride-hailing platform with heterogeneous drivers. Drivers are split into two categories, part-time drivers and full-time drivers , based on their level of reliance on the platform. Part-time drivers are less patient than full-time drivers and more sensitive to waiting time in the matching process, which causes uncertainty in the supply for ride-hailing platforms. Motivated by this uncertainty, we propose a priority-based matching policy (PMP), where the platform gives part-time drivers matching priority over full-time drivers when making matches. We prove that the ride-hailing platform can always benefit from PMP on measures, such as the number of matches (passenger–driver pairs) or total revenue. We identify the principal factors in determining the optimal matching priority ratio to be applied in PMP; these include the passenger arrival rate, the arrival rate of part-time drivers, and the average patience level of part-time drivers. We also show that under certain market conditions, PMP is a negative policy for full-time drivers, e.g., reducing their average income. Using a subsidy, the platform can compensate for the lost income of full-time drivers. This creates a triple-win situation for the platform, drivers and passengers.

Developing an Optimal Peer-to-Peer Ride-Matching Problem Algorithm with Ride Transfers

Thanks to recent developments in ride-hailing transit services throughout the world, the peer-to-peer (P2P) ride-matching problem has been actively considered in academia in recent years. P2P ride-matching not only reduces travel costs for riders but also benefits drivers by saving them money in exchange for their additional travel time. However, assigning riders to drivers in an efficient way is a complex problem that requires focusing on maximizing the benefits for both riders and drivers. This study aims to formulate a multi-driver multi-rider (MDMR) P2P ride-matching problem, based on rational preferences and cost allocation for both driver and rider, which also allows riders to transfer between multiple drivers for their travels if needed. Tabu Search (TS) for system-optimal ride-matching and a Greedy Matching (GM) algorithm for stable ride-matching were developed to solve ride-matching cases. The results show that the developed algorithm can successfully solve the proposed P2P MDMR ride-matching problem. MDMR P2P ride-matching can be used in areas where not much demand for ride-sharing exists, or for long-distance travel. Also it can be applied to designs for a more efficient on-demand transit network which can allow for transfers between routes. Moreover, the comparison of results between two implemented approaches shows that system-optimal centralized ride-matching can bring more cost savings for all participants in the system, although it may not always be stable when riders and drivers can choose their ride-matching for their own maximum benefit.

A Traveler Incentive Program for Promoting Community-Based Ridesharing

Traffic congestion has become a serious issue around the globe, partly owing to single-occupancy commuter trips. Ridesharing can present a suitable alternative for serving commuter trips. However, there are several important obstacles that impede ridesharing systems from becoming a viable mode of transportation, including the lack of a guarantee for a ride back home as well as the difficulty of obtaining a critical mass of participants. This paper addresses these obstacles by introducing a traveler incentive program (TIP) to promote community-based ridesharing with a ride back home guarantee among commuters. The TIP program allocates incentives to (1) directly subsidize a select set of ridesharing rides and (2) encourage a small, carefully selected set of travelers to change their travel behavior (i.e., departure or arrival times). We formulate the underlying ride-matching problem as a budget-constrained min-cost flow problem and present a Lagrangian relaxation-based algorithm with a worst-case optimality bound to solve large-scale instances of this problem in polynomial time. We further propose a polynomial-time, budget-balanced version of the problem. Numerical experiments suggest that allocating subsidies to change travel behavior is significantly more beneficial than directly subsidizing rides. Furthermore, using a flat tax rate as low as 1% can double the system’s social welfare in the budget-balanced variant of the incentive program.

Multi-party ride-matching problem in the ride-hailing market with bundled option services

As demands for convenient and comfortable mobility grow, transportation network companies (TNCs) begin to diversify the ride-hailing services they offer. Modes that are offered now include ride-pooling (RP), non-ride-pooling (NP), and a third “bundled” option, which combines RP and NP. This emerging bundled option allows riders to be served via either RP or NP mode, whichever becomes available first. This paper examines the added complexity that a ride-hailing service platform faces when it introduces a third bundled option. Incorporating the predicted pooling information in the near future, a ride-matching problem is dedicated to matching vehicles with riders under various scenarios over a number of matching iterations. We formulate the multi-period ride-matching problem using an integer linear programming model with multiple objectives and to make dispatching decisions based on certain matching criteria. The complexity of the problem requires resolution via a two-stage Kuhn-Munkres (2-KM) algorithm, whose robustness is verified by computational tests. Some interesting insights are obtained: (1) how the bundled option impacts system performance metrics depends on whether the supply is sufficient or not; (2) there is an optimal value of the criterion of the maximum pickup time that maximizes the ride-pooling time savings.

Who is more likely to get a ride and where is easier to be picked up in ride-sharing mode?

The ubiquity of information and communication technology (ICT) and application of global positioning system (GPS) enabled cell phones provide new opportunities to implement ride-sharing in many ride-hailing platforms, where matching proposals with multiple riders are established on very short notice. In this paper, the travelers joining in the ridesharing are assumed to be homogeneous in terms of having their own vehicles. When they have announced their travel requests, the ride-sharing platform will check whether they can be picked up by any other travelers. If failed, they will drive by themselves and become a driver who would like to pick up other passengers in the system. To solve this problem, the ride-matching problem is formulated as a set-partitioning problem and a so-called ordered greedy (OG) method is presented to get the approximately optimum under the large-scale circumstance. The results of simulation examples prove that the proposed method can achieve a reasonable matching result through Cplex within a few seconds but at most 3.8% worse than the exact optimum. Furthermore, several interesting results are also found via simulating generated data and the real-world data of Chengdu in China. In simulation experiments, with a higher level of demand density, the easiest place to find a ride is not in the center but a ring close by it, which is determined by traffic flows, OD distance and vehicles’ utilization. As a contrast, the optimal strategy for participants to be a rider is going to other specific regions rather than staying in the city center in real-world experiments.

Frontiers in Service Science: Ride Matching for Peer-to-Peer Ride Sharing: A Review and Future Directions

As a consequence of the sharing economy attaining more popularity, there has been a shift toward shared-use mobility services in recent years, especially those that encourage users to share their personal vehicles with others. To date, different variants of these services have been proposed that call for different settings and give rise to different research questions. Peer-to-peer (P2P) ride sharing is one such service that provides a platform for drivers to share their personal trips with riders who have similar itineraries. Unlike ride-sourcing services, drivers in P2P ride sharing have their own individual trips to make and are not driving for the sole purpose of serving rider requests. Unlike traditional carpooling, P2P ride sharing can serve on-demand and one-time trip requests. P2P ride sharing has been identified as a sustainable mode of transportation that results in several individual and societal benefits. The core of a P2P ride-sharing system is a ride-matching problem that determines ride-sharing plans for users. This paper reviews the major studies on the operations of P2P ride-sharing systems, with a focus on modeling and solution methodologies for matching, routing, and scheduling. In this paper, we classify ride-sharing systems based on their operational features and review the existing methodologies for each class. We further discuss a number of important directions for future research.

Trip-based graph partitioning in dynamic ridesharing

A dynamic ridesharing system is a platform that connects drivers who use their personal vehicles to travel with riders who are in need of transportation, on a short notice. Since each driver/rider may have several potential matches, to achieve a high performance level, the ridesharing operator needs to make the matching decisions based on a global view of the system that includes all active riders and drivers. Consequently, the ride-matching problem that needs to be solved can become computationally expensive, especially when the system is operating over a large region, or when it faces high demand levels during certain hours of the day. This paper develops a graph partitioning methodology based on the bipartite graph that arises in the one-to-one ride-matching problem. The proposed method decomposes the original graph into multiple sub-graphs with the goal of reducing the overall computational complexity of the problem as well as providing high quality solutions. We further show that this methodology can be extended to more complex ride-matching problems in a dynamic ride-sharing system. Using numerical experiments, we showcase the advantages of the new partitioning method for different forms of ride-matching problems. Moreover, a sensitivity analysis is conducted to show the impact of different parameters on the quality of our solution.

Multi-Objective Online Ride-Matching

We study the following multi-period multi-objective online ride-matching problem. A ride-sourcing platform needs to match passengers and drivers in real time without observing future information, considering multiple objectives such as platform revenue, pick-up distance, and service quality. We develop an efficient online matching policy that adaptively balances the trade-offs among multiple objectives in a dynamic setting, and provide theoretical performance guarantee for the policy. We prove that the proposed adaptive matching policy can achieve a “target-based optimal solution”, i.e., a solution that minimizes the Euclidean distance to any pre-determined multi-objective target. Specifically, the outcome under our policy converges to the “compromise solution” if we set the utopia point as the target. Through numerical experiments and industrial testing using real data from a ride-sourcing platform, we demonstrate that our approach indeed obtains solutions that are closest to the pre-determined targets under various settings, in comparison to existing approaches. The policy presents solutions with delicate balance among multiple objectives and brings value to all the stakeholders in the ride-sourcing ecosystem comparing to benchmark policies: (1) drivers with higher service scores are dispatched with more orders and receive higher incomes; (2) passengers are more likely to be served by drivers with higher service scores, and passengers with higher order revenues are served with higher answer rates, at the expense of a small increase in pick-up distance; (3) the platform obtains a higher total revenue.

A real-time algorithm to solve the peer-to-peer ride-matching problem in a flexible ridesharing system

Real-time peer-to-peer ridesharing is a promising mode of transportation that has gained popularity during the recent years thanks to the wide-spread use of smart phones, mobile application development platforms, and online payment systems. An assignment of drivers to riders, known as the ride-matching problem, is a central component of a peer-to-peer ridesharing system. In this paper we discuss the features of a flexible ridesharing system and propose an algorithm to optimally solve the ride-matching problem in a flexible ridesharing system in real-time. We generate random instances of the problem, and perform sensitivity analysis over some of the important parameters in a ridesharing system. Furthermore, we discuss two novel approaches to increase the performance of a ridesharing system.

A decomposition algorithm to solve the multi-hop Peer-to-Peer ride-matching problem

In this paper, we mathematically model the multi-hop Peer-to-Peer (P2P) ride-matching problem as a binary program. We formulate this problem as a many-to-many problem in which a rider can travel my transferring between multiple drivers, and a driver can carry multiple riders. We propose a pre-processing procedure to reduce the size of the problem, and devise a decomposition algorithm to solve the original ride-matching problem to optimality by means of solving multiple smaller problems. We conduct extensive numerical experiments to demonstrate the computational efficiency of the proposed algorithm and show its practical applicability to reasonably-sized dynamic ride-matching contexts. Finally, in the interest of even lower solution times, we propose heuristic solution methods, and investigate the trade-offs between solution time and accuracy.

An evolutionary approach to solve the dynamic multihop ridematching problem

The multihop ridesharing system generates a ridematching solution with an arbitrary number of transfers that respects personal preferences of the users and their time constraints with detour willingness. As it is considered to be NP-complete, an efficient metaheuristic is required in the application to solve the dynamic multihop ridematching problem. In this context, a novel approach, called Metaheuristics Approach Based on Controlled Genetic Operators ( MACGeO), which is supported by an original dynamic coding, is developed to address the multihop ridematching problem. The performance of the proposed approach is measured via simulation scenarios, which feature various numbers of carpool drivers (vehicles) and riders (passengers). Experimental results show that the multihop ridematching could greatly increase the number of matched requests while minimizing the number of vehicles required.

Multi-Hop Ridematching optimization problem: Intelligent chromosome agent-driven approach

In order to solve the now ubiquitous transport problems we face, may they be financial or environmental, we are primarily interested in the development of a dynamic optimized ridesharing service. Shared cars was developed in order to meet transport needs (spatio–temporal flexibility) and to promote the co-modal practice. The focus is thus for different modes of transport, whether public or private, to complement each other by integrating the car pooling system as an efficient alternative. Keeping this in mind, we concentrate on setting up an automatic and optimal ridesharing system. Said system is part of an intelligent co-modal platform which provides the required efficiency in such a context. In order to solve the problem, we must create a Ridematching solution with an arbitrary number of transfers that respects the personal preferences of the users as well as their time constraints. However, as considered to be NP-Complete, a more efficient metaheuristics is required in the application in order to solve the dynamic Multi-Hop Ridematching problem (MHRP). Evolutionary Algorithms (EAs) are known as a powerful and robust optimization technique. Nevertheless, EAs are not only expensive but also difficult to configure. This study puts forward an original Evolutionary Approach in which the chromosomes are defined as Autonomous and Intelligent Agents (E2AIA). Through an accurate protocol negotiation, the chromosomes agents can control the genetic operators and provide guidance when searching for optimal solutions within a reasonable time. This is crucial in real-world systems, where time for deliberation is a very important factor. Test results indicate that the classical evolutionary algorithm, developed to solve the dynamic MHRP, results in poor performance when compared with our E2AIA in terms of optimality and computational time. It is worth noting that the proposed agent-driven method is general and could be adapted to expert design and intelligent systems for other complex search issues.

Agent-based Evolutionary Cooperative Approach for Dynamic Multi-Hop Ridematching Problem

A Dynamic Multi-hop Ridesharing is a flexible form in which the request can be matched to one or more drivers at different times such that the rider reaches his destination hop by hop. This kind of ridesharing offers more possibilities for the riders. The Multi-Hop Ridematching Problem (M — HRP) in its dynamic context is considered to be NP-Complete. Therefore, we mainly focus on suitable concepts to reduce real time ridematching's high complexity. In this context, we aim to set up a distributed architecture through which an optimizing decentralized process will be performed. In this study, we developed an original distributed approach based on evolutionary cooperative algorithms driven by agents' communication. The cooperation aspect appears into two levels: In the agent architecture level to ensure communication, negotiation and collaboration between agents, and in the algorithmic level to ensure consistency and persistence of the cooperation between these agents.

Mitigating Traffic Congestion: Solving the Ride-Matching Problem by Bee Colony Optimization

Urban road networks in many countries are severely congested. Expanding traffic network capacities by building more roads is very costly as well as environmentally damaging. Researchers, planners, and transportation professionals have developed various Travel Demand Management (TDM) techniques, i.e. strategies that increase travel choices to travelers. Ride sharing is one of the widely used TDM techniques that assumes the participation of two or more persons that together share a vehicle when traveling from few origins to few destinations. In ride-matching systems, commuters wishing to participate in ride sharing are matched by where they live and work, and by their work schedule. There is no standard method in the open literature to determine the best ride-matching method. In this paper, an attempt has been made to develop the methodology capable to solve the ride-matching problem. The proposed Bee Colony Optimization Metaheuristic is sufficiently general and could be applied to various combinatorial optimization problems.