Ridesharing Problem Research Articles

A set-based differential evolution algorithm for QoS-oriented and cost-effective ridesharing

The rapid proliferation of private cars brings serious problems such as traffic congestion and air pollution. Ridesharing provides a promising way to alleviate these issues by allocating riders to drivers with similar itineraries. In this work, we focus on a QoS-oriented and cost-effective ridesharing problem and propose an efficient algorithm to solve the problem. First, we formulate the ridesharing model and encode the solutions based on sets considering both service quality and cost. Then, we develop a set-based differential evolution algorithm to search the global optimum for the formulated problem. From the algorithm aspect, we specifically design new operators, such as the greedy initialization, the inter-vehicle mutation, and the route-sensitive selection, to enhance the performance of differential evolution for dealing with the ridesharing problem. The experimental results show that our method outperforms the state-of-the-art methods on metropolis transport datasets.

Nonprofit peer-to-peer ridesharing optimization

Both for-profit and nonprofit peer-to-peer (P2P) ridesharing services have gained enormous popularity in recent years due to their advantages over solo driving and public transit. We study the rideshare matching and routing problem in a nonprofit P2P ridesharing system consisting of a matching agency, drivers and riders. The matching agency is a government or a not-for-profit organization and its objective is to maximize the societal benefits of ridesharing. The drivers involved are commuters and hence have their own travel plans, which are executed regardless of whether riders are matched with them. We consider both static and dynamic versions of the nonprofit P2P ridesharing problem. Existing modeling and solution approaches for similar P2P ridesharing problems can only solve relatively small problem instances optimally. We propose an exact solution algorithm for the static version of the problem by taking advantage of its special characteristics. This exact solution approach formulates and solves the problem as a set packing formulation using route-based variables, and uses an efficient graph-based approach to generate all necessary vehicle routes in the formulation quickly. We also develop a column generation (CG) based heuristic approach for the static problem. Finally, we propose two dynamic dispatching policies for the dynamic version of the problem. Our proposed exact algorithm solves very large problem instances (e.g., with 600 drivers and 1800 riders) of the static problem and our CG based heuristic can find near-optimal solutions for even larger instances of the static problem in short computation time. Our dynamic dispatching policies can generate near-optimal solutions for the dynamic problem in real-time fashion. We also generate some important insights based on some taxi trip data in Washington DC. First, P2P ridesharing can bring significant cost-saving, especially when the participants have a relatively flexible schedule. For every 10% increase in schedule flexibility, there is an about 4% to 7% increase in cost-saving. Second, the cost-saving due to ridesharing increases with the vehicle capacity, but this increase slows down quickly when the vehicle capacity reaches 4. Third, ridesharing generates more cost savings during peak hours and in urban areas.

Dynamic Ridesharing in Peak Travel Periods

In this paper, we study a variant of the dynamic ridesharing problem with a specific focus on peak hours: Given a set of drivers and rider requests, we aim to match drivers to each rider request by achieving two objectives: maximizing the served rate and minimizing the total additional distance, subject to a series of spatio-temporal constraints. Our problem can be distinguished from existing work in three aspects: (1) Previous work did not fully explore the impact of peak travel periods where the number of rider requests is much greater than the number of available drivers. (2) Existing solutions usually rely on single objective optimization techniques, such as minimizing the total travel cost. (3) When evaluating the overall system performance, the runtime spent on updating drivers' trip schedules as per incoming rider requests should be incorporated, while it is excluded by most existing solutions. We propose an index structure together with a set of pruning rules and an efficient algorithm to include new riders into drivers' existing trip schedule. To answer new rider requests effectively, we propose two algorithms that match drivers with rider requests. Finally, we perform extensive experiments on a large-scale test collection to validate the proposed methods.

A decomposition algorithm to solve dynamic city-scale ridesharing problem

This paper proposes a computational efficient, rapid, dynamic ridesharing algorithm. Firstly, a designed pre-processing procedure reduces size of the input date of potential nodes for vehicle to pass through. The proposed decomposition algorithm decomposes the large-scale problem with pre-processing date into independent sub-problems, and the method of parallel computing is adopted to solve the sub-problems. We call the dynamic programming algorithm to deal with the vehicle routing problem involved in sub-problems. Greedy random adaptive search algorithm is compared with the decomposition algorithm to verify the algorithm. Finally, a mathematical example shows that the algorithm can effectively solve large-scale dynamic ridesharing problems.

Exploring the Ridesharing Efficiency of Taxi Services

The application of ridesharing strategy to autonomous taxi system holds great promise for improving the efficiency in the future on-demand ride-hailing services. Prior to the implementation of dispatching strategies to the autonomous taxi system, it is necessary to gain insight into the performance of the dispatching strategies. This study aims to solve the dynamic ridesharing problem and conduct a comprehensive quantity analysis of the ridesharing efficiency for various demand levels and carrying capacities in a metropolitan area. We quantify the success rate of serviced requests, the trip travel time, the discount rate of taxi fare, and total energy consumption for different carrying capacities and demand levels in the road network of Shenzhen city. The simulation results show that the ride-matching success rate within 3 minutes enables to increase by more than 13% in the ridesharing mode, and over 80% of the passengers can be served within 6 minutes if the carrying capacity is set to four. The trip travel time and energy consumption also show a significant downward trend as the capacity of the taxi increases in the ridesharing mode.

Modeling and Optimization of a Multi-Objective Ridesharing Problem in the Case of Medical Waste

Medical wastes is now a major concern of the world community and more particularly that of Moroccans. Indeed, these wastes, classified as hazardous products, are the source of serious infections, contamination of groundwater and air pollution. Through this paper, we encouraged the use of ridesharing to cope with the risks and costs arising from the logistics of these medical wastes. Thus, we have proposed a mathematical model that governs the multi-objective nature of this logistics and the various constraints associated with it. Since the exact approach had difficulties in large instances, we proposed the Genetic Algorithm and Evolution Strategy as metaheuristic to solve the model. The Evolution Strategy showed its efficiency and stability and therefore we have demonstrated through this metaheuristic the possibility of a compromise between the main objectives of our model.

Fast optimised ridesharing: Objectives, reformulations and driver flexibility

Ridesharing schemes, which match passengers to private car journeys with spare seats, are becoming popular for improving access, and reducing costs, emissions and congestion. Current deployed ridesharing systems offer matches to participants based on trip details and on preferences. They do not optimise for system-wide objectives that generally incur a high computational cost. Academic studies tend to focus on specific single objectives, while stakeholders and policy-makers for practical deployments may have many different, possibly vague, objectives. In this paper, we try to reduce the gap between research and deployed applications, and tackle the problem of computing global plans for a flexible ridesharing scheme, for different objectives. In a flexible scheme, some drivers are willing to leave their cars and become passengers of other drivers. We consider objectives to improve access (maximising served passengers), reduce emissions and congestion (minimising total distance), and to sustain the long-term viability of the system (maximising served users). In the studied scheme, riders reach a meeting point and are picked-up along the intended routes of the drivers. We show that when drivers can change roles and become passengers, a state-of-the-art solver is not able to solve quickly ridesharing schemes optimizing for global sustainable objectives. To handle this issue, we introduce new spatio-temporal constraints, based on the notion of time-consistent sets of riders, from which we derive new logical rules and routing properties. We encode the new properties and the derived logical rules within new ridesharing problem formulations. We show that solving the introduced formulations encoding the routing properties and the logical rules are up to one order of magnitude faster than the basic model, and that even for the larger instances with 1000s of users, achieves results within 2% and 5% of the optimal value within a time limit of 300 seconds. Our study also shows the interaction between the different objectives and to which extent satisfying one objective also satisfies other objectives. This study demonstrates that the introduced method that integrates spatio-temporal constraints encoding the drivers’ shared paths using logical rules within ridesharing problem formulations, help to solve faster, larger ridesharing schemes satisfying various global sustainable objectives. The methods developed in this study are of particular interest to developers and researchers interesting in reformulation techniques using logical rules to speed-up the solving of real world applications. The outcomes of this study will also benefit a variety of stakeholders (users, companies, public institutes) that aim at offering or maintaining ridesharing schemes as a sustainable solutions.

Efficient algorithms for ridesharing of personal vehicles

Given a set of trips in a road network, where each trip has an individual, a vehicle and some requirements, the ridesharing problem is to select a subset of vehicles to deliver the individuals of all trips to their destinations satisfying the requirements. Common requirements of a trip include the source, destination, vehicle capacity, detour distance limit, preferred paths, and time constraints of the trip. Minimizing the total travel distance of the vehicles and minimizing the number of vehicles are major optimization goals. These minimization problems are complex and NP-hard because each trip may have many requirements. We study simplified minimization problems in which each trip's requirements are specified by the source, destination, vehicle capacity, detour distance and preferred path parameters. We show that both minimization problems can be solved in polynomial time if all of the following conditions are satisfied: (1) all trips have the same destination or same source; (2) no detour is allowed and (3) each trip has one unique preferred path. It is known that both minimization problems are NP-hard if any one of the three conditions is not satisfied. Our results and the NP-hard results suggest a boundary between the polynomial time solvable cases and NP-hard cases for the minimization problems.

Solving the first‐mile ridesharing problem using autonomous vehicles

AbstractAutonomous vehicles (AVs) are paving a way to reshape the operation and management of urban transportation systems by improving travel mobility. In this study, we propose to use AVs for solving the first‐mile (FM) problem that aims to transport passengers from their homes to metro stations. Passengers submit travel requests in advance and a fleet operator dispatches AVs and arranges ridesharing in a rolling horizon framework. The ridesharing is implemented by assigning one AV to serve several requests subject to the vehicle capacity, the maximum travel time, and the accessibility constraints. A mixed integer linear programming (MILP) model is formulated to determine the AV dispatch and ridesharing schemes for the minimum operational costs. Then, another MILP model with the objective of the maximum user satisfaction is formulated for the purpose of comparison. A cluster‐based solution method is designed to deal with the large‐scale FM problem. Extensive numerical experiments are conducted to demonstrate the application of the proposed method. Results show that ridesharing can reduce both required AV fleet size and vehicle traveled miles. The proposed solution method is able to solve the problem efficiently and fulfil the requirement of online computation.

Ridesharing

Ridesharing is becoming a popular mode of transportation with profound effects on the industry. Recent algorithms for vehicle-to-customer matching have been developed; yet cross-study evaluations of their performance and applicability to real-world ridesharing are lacking. Evaluation is complicated by the online and real-time nature of the ridesharing problem. In this paper, we develop a simulator for evaluating ridesharing algorithms, and we provide a set of benchmarks to test a wide range of scenarios encountered in the real world. These scenarios include different road networks, different numbers of vehicles, larger scales of customer requests, and others. We apply the benchmarks to several state-of-the-art search and join based ridesharing algorithms to demonstrate the usefulness of the simulator and the benchmarks. We find quickly-computable heuristics outperforming other more complex methods, primarily due to faster computation speed. Our work points the direction for designing and evaluating future ridesharing algorithms.

Application of parallel tabu search in solving Ride-sharing problem with HOV Lanes

Ride-sharing can be defined as a system where travelers can share their vehicles and travel cost with others who have a similar destination and time schedule. Ride-sharing problem with High Occupancy Vehicle (HOV) Lanes is a problem of finding an optimal route to serve costumer demand where each demand and vehicle consists of origin and destination point, and some of the route are restricted-use freeways lanes reserved for vehicles with more than a predetermined number of occupants, which called HOV lanes. With the use of ride-sharing and HOV Lanes, it is expected to reduce the number of the vehicles thus will mitigate the congestion. The optimal solution is the one with minimum number of total distance, passenger’s ride time, and cost. This study discusses the application of insertion heuristic and parallel tabu search to optimize the use of Ride-sharing with HOV Lanes. Insertion heuristic is applied to obtain an initial solution and parallel tabu search algorithm is applied to improve the initial solution and obtain a better solution. For the case of eight vehicles and eight requests, from insertion heuristic we obtained an initial solution with an objective function value of 425 and with the used of parallel tabu search method we obtained a better solution with an objective function value of 420.

Bus Pooling: A Large-Scale Bus Ridesharing Service

Ridesharing, a shared service that uses the information and knowledge matching, can efficiently utilize scattered social resources to reduce the demand for vehicles in urban road networks. However, car ridesharing has the problems of low capacity and high cost, and it cannot satisfy demands for recurring, long-distance, and low-cost trips. In this paper, we formally define the bus ridesharing problem and propose a large-scale bus ridesharing service to resolve this problem. In our proposed model, the rider can use an online bus-hailing service to upload his or her trip demand and wait to be picked up when it gathers enough people. The provider assigns drivers to riders after integrating the matched ride requests. To maximize ridesharing’s success rate, we developed both exact algorithms and approximate algorithms to optimize the ride-matching service. A real-life dataset that contains 65,065-trip instances extracted from 10,585 Shanghai taxis from one day (Apr 1, 2018) is used to demonstrate that our proposed service can provide higher cost performance and on-demand bus services for every ride request. Meanwhile, it reduces the number of vehicles used by 92% and 96% and the amount of oil used by 87% and 92% compared with car ridesharing and no ridesharing, respectively.

Bi-objective green ride-sharing problem: Model and exact method

We investigate the bi-objective green ride-sharing problem (BGRSP) with consideration of the drivers' interests. The first objective is to minimize carbon emissions. The second objective is to maximize average ride profit so that every driver's interest can be satisfied. The average ride profit is the average profit of all used rides and it is non-linear due to the variable number of the used rides. The BGRSP is a nonlinear multi-objective problem. We develop an exact method with three steps to solve the BGRSP. The highlight of the exact method is to cut most of the non-Pareto-optimal solutions and use a decomposition method. First, we define the Pareto-optimal ride and prove that every Pareto-optimal solution of the BGRSP is composed of the Pareto-optimal rides; thus, the solution space is reduced by cutting the non-Pareto-optimal rides. Second, we define the partition (equivalent to the solution of BGRSP) based on the relationship matrix between customers and Pareto-optimal rides which is diagonalized into several submatrices, and prove that all partitions of the relationship matrix can be obtained by the partitions of the submatrices. Therefore, the larger-scale NP-hard problem is decomposed into several small-scale NP-hard problems, each of which produces partitions of each submatrix. Third, we define the Pareto-optimal partition and prove every Pareto-optimal solution of BGRSP is composed of the Pareto-optimal partitions of each submatrix. Thus, the solution space can be significantly reduced by cutting the non-Pareto-optimal partitions, even by (1-5.5E-42)*100%. The exact method is validated by solving a benchmark instance of pdp_100-lr101 from Li & Lim benchmark with 106 customers and 200 vehicle capacity. The proposed model and method can reduce carbon emissions and make every driver satisfied simultaneously.

Ride-matching and routing optimisation: Models and a large neighbourhood search heuristic

This paper considers a ridesharing problem on how to match riders to drivers and how to choose the best routes for vehicles. Unlike the others in the literature, we are concerned with the maximization of the average loading ratio of the entire system. Moreover, we develop a flow-dependent version of the model to characterize the impact of pick-up and drop-off congestion. In another extended model we take into account the riders’ individual evaluation on different transportation modes. Due to the large size of the resulting models, we develop a large neighbourhood search algorithm and demonstrate its efficiency.

Ridesharing Problem with Flexible Pickup and Delivery Locations for App-Based Transportation Service: Mathematical Modeling and Decomposition Methods

App-based transportation service system, such as Uber and Didi, has brought a new transportation mode to users, who are able to make reservations using mobile apps conveniently. However, one of the fundamental challenges in app-based transportation system is the inefficiency and unreliability of the vehicle routing plans caused by complex topology of urban road network and unpredictable traffic conditions. A common way to tackle this problem is repositioning pickup or delivery locations via the coordination between drivers and passengers. This paper studies an on-demand ridesharing problem that determines the optimal ride-share matching strategy and vehicle routing plan with respect to flexible pickup and delivery locations. By introducing the concept of space-time windows, the problem is formulated as the pickup and delivery problem with space-time windows (PDPSW) in space-time network. To solve the model efficiently and accurately, we particularly develop a customized solution approach based on Lagrangian relaxation. Numerical examples are conducted to demonstrate the performance of the proposed framework and draw some managerial insights into the optimal system operation. The results indicate that adopting the serving strategy of flexible pickup and delivery locations will evidently reduce the system cost and improve the service quality in app-based transportation service systems.

Modeling an enhanced ridesharing system with meet points and time windows.

With the rising of e-hailing services in urban areas, ride sharing is becoming a common mode of transportation. This paper presents a mathematical model to design an enhanced ridesharing system with meet points and users’ preferable time windows. The introduction of meet points allows ridesharing operators to trade off the benefits of saving en-route delays and the cost of additional walking for some passengers to be collectively picked up or dropped off. This extension to the traditional door-to-door ridesharing problem brings more operation flexibility in urban areas (where potential requests may be densely distributed in neighborhood), and thus could achieve better system performance in terms of reducing the total travel time and increasing the served passengers. We design and implement a Tabu-based meta-heuristic algorithm to solve the proposed mixed integer linear program (MILP). To evaluate the validation and effectiveness of the proposed model and solution algorithm, several scenarios are designed and also resolved to optimality by CPLEX. Results demonstrate that (i) detailed route plan associated with passenger assignment to meet points can be obtained with en-route delay savings; (ii) as compared to CPLEX, the meta-heuristic algorithm bears the advantage of higher computation efficiency and produces good quality solutions with 8%~15% difference from the global optima; and (iii) introducing meet points to ridesharing system saves the total travel time by 2.7%-3.8% for small-scale ridesharing systems. More benefits are expected for ridesharing systems with large size of fleet. This study provides a new tool to efficiently operate the ridesharing system, particularly when the ride sharing vehicles are in short supply during peak hours. Traffic congestion mitigation will also be expected.

Large-scale nationwide ridesharing system: A case study of Chunyun

Abstract In this paper, we propose a large-scale nationwide ridesharing system named CountryRoads, which is designed to resolve the surge of homeward-bound persons during holiday seasons. CountryRoads matches drivers who are willing to share their spare seats with passengers who are unable to obtain public transportation for long-distance trips. The system utilizes a variety of user interfaces, including text messages, mobile websites, and smart-phone applications to enable the participation of a variety of people. Further, it implements an online greedy-matching algorithm to solve the ridesharing problem in order to propose matches between drivers and passengers. We deploy and evaluate CountryRoads during the Chinese Spring Festival travel season (Chunyun period), when all forms of transportation are stressed or near saturation, and when many lower-income individuals fail to reserve seats for rail or flight trips. We present our design choices, analytics, and lessons learned from our multi-year deployment in 2012, 2014, and 2015. The improvements over the years resulted in up to 17,272 users, 4777 ridesharing tuples, and an eventual success rate of 23.2% in 2015. This is a significant result considering that most of our participants use CountryRoads as a last resort in response to public transportation shortages.

STaRS: Simulating Taxi Ride Sharing at Scale

As urban populations grow, cities face many challenges related to transportation, resource consumption, and the environment. Ride sharing has been proposed as an effective approach to reduce traffic congestion, gasoline consumption, and pollution. However, despite great promise, researchers and policy makers lack adequate tools to assess the tradeoffs and benefits of various ride-sharing strategies. In this paper, we propose a real-time, data-driven simulation framework that supports the efficient analysis of taxi ride sharing. By modeling taxis and trips as distinct entities, our framework is able to simulate a rich set of realistic scenarios. At the same time, by providing a comprehensive set of parameters, we are able to study the taxi ride-sharing problem from different angles, considering different stakeholders' interests and constraints. To address the computational complexity of the model, we describe a new optimization algorithm that is linear in the number of trips and makes use of an efficient indexing scheme, which combined with parallelization, makes our approach scalable. We evaluate our framework through a study that uses data about 360 million trips taken by 13,000 taxis in New York City during 2011 and 2012. We describe the findings of the study which demonstrate that our framework can provide insights into strategies for implementing city-wide ride-sharing solutions. We also carry out a detailed performance analysis which shows the efficiency of our approach.

Algorithmic analysis for ridesharing of personal vehicles

The ridesharing problem is to share personal vehicles by individuals with similar itineraries. A trip in the ridesharing problem is an individual and his/her itinerary. To realize a trip is to deliver the individual to his/her destination by a vehicle satisfying the itinerary requirement. Major optimization goals in the ridesharing problem include minimizing the number of vehicles and minimizing the total travel distance of vehicles to realize all trips. There are many parameters in the minimization problems, making them complex and NP-hard. The problems can be simplified by considering only some of the parameters. We give an algorithmic analysis for the simplified minimization problems and explore a boundary between the NP-hard and polynomial time solvable cases. We prove that the simplified minimization problems, where only the source, destination, vehicle capacity, detour distance and preferred path parameters are considered, are still NP-hard. We show that the simplified problem of minimizing the number of vehicles becomes polynomial time solvable if the considered parameters satisfy certain conditions. These suggest a boundary between the NP-hard and polynomial time solvable cases.

A Ride-Sharing Problem with Meeting Points and Return Restrictions

Ride sharing has been widely acknowledged as an effective solution for reducing travel costs, congestion, and pollution. This paper considers the ride-sharing problem of the scheduled commuter and business traffic within a closed community of companies that agree to share the calendars of their employees. We propose a formulation in the form of a general integer linear program (ILP) for the aforementioned ride-sharing problem, which incorporates return restrictions to satisfy the business needs, as well as meeting points and the option for riders to transfer between drivers. All the instances with 40 and 60 participants and most of the instances with 80 participants can be solved to optimality within a time limit of two hours. Using instances of up to 100 participants, the ILP can be solved with a gap of no more than 1.8% within the time limit. Because of the high computational complexity, we develop a constructive heuristic that is based on the savings concept. This heuristic is also able to combine ride sharing with the use of an external mobility service provider. Our numerical study shows that ride sharing can be an effective way of reducing the number of trips and vehicle miles. Particularly, ride sharing creates more benefits when the participation is high and when the origins and the destinations of the trips are more spatially concentrated. The results show that ride sharing can create up to 31.3% mileage savings and up to 28.7% reduction in the number of cars needed to fulfill employees’ travel schedules. We also illustrate our model using a real-life ride-sharing problem of a Dutch consultancy and research firm. The online appendix is available at https://doi.org/10.1287/trsc.2018.0832 .