Ridesharing Model Research Articles

Designing stable ride-sharing for commuting trip chain with role flexibility

AbstractIn ride-sharing, fixing the roles of commuters with vehicles could come at an opportunity of missed balance between riders and drivers. The decisions made by commuters with vehicles about roles are related to both to-work travel and return-home travel. Commuters may choose not to drive when they have a confirmed ride back home. This study designs a stable ride-sharing model with role flexibility for the commuting trip chain. The preferences of commuters over matching partners in trip chains are formulated. A stable matching algorithm based on commuting trip chain is designed. To assess the performance of the proposed model and algorithm, real-life data from a resident trip survey in Dalian are used to design experimental cases. The results indicate that stable matching based on the trip chain is not a consequence of the repetition of successive one-period stable matching. Compared with matching in two phases, the matching rate of commuters increases by an average of 10%. When the number of commuters with role flexibility increases by four times, the matching rate of commuters increases by an average of 5.6%.

Integration of ridesharing and activity travel pattern generation

This paper proposes a mobility system serving as an innovative platform that synchronises a ridesharing model and an Activity Travel Pattern (ATP) generator, enabling a synergistic enhancement of participants’ mobility experiences. Through system-level integration, it can significantly improve the performance of ridesharing systems while also exploring the intricate relationship between ridesharing and participants’ ATPs. Numerical examples demonstrate the advantages of ridesharing systems when armed with flexible/variable announcements from the ATPs-based participants. The results show that incorporating ATP-based announcements in the system increases the chances of finding a match, and the attractiveness of the ridesharing mode for participants is improved. Despite their lower number, ATP-based participants contribute significantly to finding matches and obtaining distance savings. The study also highlights the key role of tour gyration in the performance of the ridesharing system. It incentivises participants with distant activity locations to join the ridesharing system as ATP-based participants.

On the Sustainability of Shared Mobility Since COVID-19: From Socially Structured to Social Bubble Vanpooling

Public and shared transportation are among the sectors that have been severely affected by the COVID-19 pandemic, as they were perceived to be risky environments for disease transmission. Given that the end of the pandemic is not certain and in order to anticipate future pandemics, attempts have been made to design public and shared mobility systems that are pandemic resilient, avoiding the social and economic burdens of disrupting transportation services. In this paper, we introduce a new ridesharing form based on a novel concept called social bubble vanpooling (SBV) which tries to provide a trade-off between minimizing the risk of exposure of riders to communicable diseases, minimizing the operational costs of ridesharing operators, and providing public health authorities with full contact-tracing capability in ridesharing-related cases, if needed. We propose a new clustering approach where riders are pooled into social bubbles composed of people who are spatio-temporally connected and have similar vulnerability levels with respect to a communicable diseases. We used individual agent-based simulation experiments based on a data sample collected from a real population of riders, and we compared the performance of the proposed SBV with trip-based and long committed ridesharing models. We found that (1) enforcing contact tracing and quarantine is more effective in controlling the spread of the disease when the bubble-based ridesharing scheme is adopted as a commuting mode and (2) it is possible to sustain transportation services without compromising the efforts to mitigate the spread of the pandemic. The proposed ridesharing model appears to be a viable solution when the mobility of individuals is subject to tight restrictions to stop the spread of a communicable airborne disease (such as COVID-19). The flexibility of the model allows maintaining transportation services with profitable operational costs while upholding the precautionary measures to fight the pandemic.

Development and Comparison of Ten Differential-Evolution and Particle Swarm-Optimization Based Algorithms for Discount-Guaranteed Ridesharing Systems

Savings on transportation costs provide an important incentive for shared mobility models in smart cities. Therefore, the problem of maximizing cost savings has been extensively studied in the ridesharing literature. Most studies on ridesharing focus on the maximization of the overall savings on transportation costs. However, the maximization of the overall savings on transportation costs may satisfy users’ expectations for cost savings. For people to adopt ridesharing as a means to reduce costs, a minimal expected cost savings discount must be offered. There is obviously a gap between the existing studies and the real problems faced by service providers. This calls for the development of a study to formulate a ridesharing model that guarantees the satisfaction of a minimal expected cost savings discount. In this paper, we considered a discount-guaranteed ridesharing model that ensures the provision of a minimal expected cost savings discount to ridesharing participants to improve users’ satisfaction with the ridesharing service in terms of cost savings. The goal was to maximize the overall cost savings under certain capacity, spatial, and time constraints and the constraint that the discount offered to ridesharing participants could be no lower than the minimal expected cost savings discount. Due to the complexity of the optimization problem, we adopted two evolutionary computation approaches, differential evolution and particle swarm optimization, to develop ten algorithms for solving the problem. We illustrated the proposed method by an example. The results indicated that the proposed method could guarantee that the discount offered to ridesharing participants was greater than or equal to the minimal expected cost savings discount. We also conducted two series of experiments to assess the performance and efficiency of the different solution algorithms. We analyzed the results to provide suggestions for selecting the appropriate solution algorithm based on its performance and efficiency.

Stable Matching of Users in a Ridesharing Model

A ridesharing system is a transport mode where two or more users share the same vehicle and divide the trip’s expenses based on similar routes and itineraries. Popular ridesharing systems, such as Uber, Flinc, and Lyft, define a matching among users based only on the coincidence of routes. However, these systems do not guarantee a stable matching (i.e., a matching in which no user prefers another different from the assigned one). In this work, a new ridesharing system model is proposed, including three types of trips: identical, inclusive, and partial. This model is used to introduce a new algorithm to address the stable matching problem for ridesharing systems. Finally, a set of experimental simulations of the proposed algorithm is conducted. Experimental results show that the proposed algorithm always produces a stable matching.

Bi Objective Peer-to-Peer Ridesharing Model for Balancing Passengers Time and Costs

Ride-sharing services are one of the top growing sustainable transportation trends led by mobility-as-a-service companies. Ridesharing is a system that provides the ability to share vehicles on similar routes for passengers with similar or nearby destinations on short notice, leading to decreased costs for travelers. At the same time, though, it takes longer to get from place to place, increasing travel time. Therefore, a fundamental challenge for mobility service providers should be finding a balance between cost and travel time. This paper develops an integer bi-objective optimization model that integrates vehicle assignment, vehicle routing, and passenger assignment to find a non-dominated solution based on cost and time. The model allows a vehicle to be used multiple times by different passengers. The first objective seeks to minimize the total cost, including the fixed cost, defined as the supply cost per vehicle, and the operating cost, which is a function of the distance traveled. The second objective is to minimize the time it takes passengers to reach their destination. This is measured by how long it takes each vehicle to reach the passenger’s point of origin and how long it takes to get to the destination. The proposed model is solved using the AUGMECON method and the NSGA II algorithm. A real case study from Sioux Falls is presented to validate the applicability of the proposed model. This study shows that ridesharing helps passengers save money using mobility services without significant change in travel time.

Matching Algorithm for Compact Ride-sharing in Rural Area using Genetic Algorithm

To solve the last mile problem in rural areas, we deal with compact ride-sharing model. For reducing the computational cost, we use a simple GA method and compared the performance of the parameters. To simplify the problem, our algorithm use a simple linear distance and minimize the total traveling distance. A data set used for GA was based on the Inami area in Nanto City, Toyama Prefecture, that is a real case study region. The performance comparison experiment of the algorithm by changing parameters was carried out. Experimental results show that the algorithm is likely to give correct answers for up to four vehicles. It was also shown that the combination of population size and elite proportion for GA could reduce computational costs while ensuring accuracy. In addition, by ride-sharing was actually carried out using the obtained experimental result, it was shown that it could be applied to the actual route even if the calculation was carried out in the linear distance.

Regulation of the Ride-sharing Business Model: International Experience and Lessons for Vietnam

Nowadays, sharing economy business models (SEBMs) have taken a very important role in the economy in Vietnam. These models have ameliorated the quality of life by reducing travel costs, increasing the quality of transportation services, and lessening unemployment, especially after the COVID-19 crisis. Among these SEBMs, the ride-sharing business model has many development opportunities. It has appeared in many countries and has efficiently supported economic growth. However, many problems also arise during the development of this model, including worker’s rights, driver licensing, and unfair competition with traditional companies. These issues bring about conflicts in society, especially between ride-sharing companies and traditional taxi companies. Meantime, the old regulation mechanisms have not been suitable with challenges related to this new business model. Thailand, Singapore and the UK are countries that have long-standing legal systems. With experience of management from these countries, this study explains some ride-sharing model issues in Vietnam and suggests some policies, especially on regulations for ride-sharing companies, ride-sharing drivers, and tax liability of these companies.

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.

Mixed Autonomy in Ride-Sharing Networks

We consider ride-sharing networks served by human-driven vehicles (HVs) and autonomous vehicles (AVs). We propose a model for ride-sharing in this mixed autonomy setting for a multilocation equidistant network, in which a ride-sharing platform sets prices for riders, compensations for drivers of HVs, and operates AVs for a fixed price with the goal of maximizing profits. When there are more vehicles than riders at a location, we consider three vehicle-to-rider assignment possibilities: 1) rides are assigned to HVs first; 2) rides are assigned to AVs first; and 3) rides are assigned in proportion to the number of available HVs and AVs. Next, for each of these priority possibilities, we establish a nonconvex optimization problem characterizing the optimal profits for a network operating at a steady-state equilibrium. We then provide a convex problem which we show to have the same optimal profits, allowing for efficient computation of equilibria, and we show that all three priority possibilities result in the same maximum profits for the platform. Next, we show that, in some cases, there is a regime, for which the platform will choose to mix HVs and AVs in order to maximize its profit, while, in other cases, the platform will use only HVs or only AVs, depending on the relative cost of AVs. For a specific class of networks, we fully characterize these thresholds analytically and demonstrate our results on an example.

Joint pricing and matching in ride-sharing systems

Ride-sharing firms use pricing and matching decisions to control the ride-sharing platforms. Those decisions can be made jointly or interchangeably, which raises the following questions: Is matching optimization necessary? Specifically, is fixing the matching decisions to a simple rule and optimizing only the pricing decisions enough to achieve the optimal performance? In order to answer these questions, we study the interplay between pricing and matching decisions of a ride-sharing firm. There are many studies in the ride-sharing literature that optimize the pricing decisions under an assumed matching policy. However, we show that ignoring matching optimization can result in subpar overall performance. We formulate a stylized ride-sharing model that captures customer and driver behaviors and geospatial nature of the system. Customers are both price and delay sensitive, and drivers are strategic and self-scheduling. We prove that optimizing the matching decisions have first-order effect on the system performance. We show that fixing the matching decisions and optimizing only the pricing decisions does not maximize the number of matchings in general. Similarly, we show that fixing the pricing decisions and optimizing only the matching decisions is not optimal in general. Finally, we show that optimizing in only one dimension (either pricing or matching) has no benefit to the firm under some conditions, whereas joint pricing and matching optimization can lead to a significant performance increase.

Hybridization of game theory and ridesharing to optimize reverse logistics of healthcare textiles

The reverse logistic of Healthcare Textile has become nowadays a major concern in Morocco. Consequently, it imposes to authorities and professionals the obligation to perform a sustainable reverse logistic model. Through this study, we are going to hybridize game theory and ridesharing to optimize a multi-objective reverse logistics of healthcare textiles. We opted for the Genetic algorithm to face the complexity of the ridesharing model. For cost allocation, the allocation based on volume give best results compared to Alternative Cost Avoided Method. Besides, it allows a higher gain for players with the most important activity.

Optimization Approach to Improve the Ridesharing Success Rate in the Bus Ridesharing Service

In high-capacity ridesharing, increasing the number of passengers per shared vehicle can achieve long-term profitability for the public transport providers; hence, the ridesharing success rate (i.e., the percentage of successful applicants) is an important indicator of monetary cost. Unlike peer-to-peer ridesharing vehicles, to open a route, high-capacity ridesharing vehicles need to satisfy the minimum loadable capacity. However, an insufficient amount of similar travel demands gathered on less popular routes can result in the forced cancellation of many bus lines, thereby reducing the ridesharing success rate. In this paper, for a bus ridesharing service, we present an optimization approach for maximizing the ridesharing success rate. Utilizing this approach, we solve the problem of low vehicle utilization by using a dynamic grid-based heuristic algorithm with performance guarantees and a novel bus ridesharing model of a hybrid form of ridesharing that combines slugging with hitchhiking. To further assess the effectiveness of the proposed approach, we evaluate the solution by using benchmarks from a real-life dataset that contains 65,065-trip instances extracted from 10,585 Shanghai taxis from one day (April 1, 2018), and we compare our results with solutions provided by the existing forms of ridesharing. The experimental results demonstrate that the proposed approach can improve the ridesharing success rate by 79.6%.

Is Uber Helping or Hurting Mass Transit? An Empirical Investigation

Sharing economy reshapes the distribution of unused or underutilized asset through digital platforms to individuals who are willing to pay for the services. The new economy model has introduced dramatic impact on the traditional industries by matching the demand and supply in real time. In this paper, we examine how the entry of Uber, a ride-sharing services digital platform, influences the demand of public transportation. Significant debate has surrounded whether the new ride-sharing model siphoned riders from public transit, or made transit feasible for more riders. However, limited empirical research has been done to uncover whether Uber is helping or hurting public transportation systems. Leveraging a natural experiment setting, the entry of Uber, with a difference-in-differences approach, we find a significant drop in the number of passenger trips with bus. Further, our results suggest that the effect of Uber is not uniform – the reduction in bus passenger trips is mitigated when the proportion of old age people in a local urban area is large; the decrease is amplified when the populations of disable people, including with language difficulty, and self-employed people are large. However, the moderating effect of poverty is mixed. We discuss the implications for research and practice.

Ride-sharing with travel time uncertainty

Travel time uncertainty has significant effects on travel reliability and travelers’ generalized trip cost. However, travel time uncertainty has not been considered in existing ride-sharing models, leading to an inaccurate estimation of the benefit from ride-sharing and irrational ride-sharing matches. To fill in the gap, this paper proposes a stochastic ride-sharing model, in which travel time is assumed to be stochastic and follow a time-independent general distribution that has a positive lower bound. Due to travel time uncertainty, travelers may not arrive at their destinations on time. Different from the traditional models taking time windows as hard constraints, the proposed ride-sharing system only requires each participant announcing a role and the desired arrival time window. In the model, the generalized trip cost consists of the cost of driving a vehicle, the cost of travel time, and the cost of schedule delay early and late. This study investigates the effect of the unit variable cost of driving, travelers’ values of time (VOTs), and travel time uncertainty on the cost saving of ride-sharing trips compared to driving-alone trips. A bi-objective ride-sharing matching model is proposed to maximize both the total generalized trip cost saving and the number of matches. The proposed ride-sharing model is further extended to consider time-dependent travel time uncertainty, and the Monte Carlo simulation (MCS) method is developed to evaluate the mean generalized trip cost. Finally, numerical examples are provided to illustrate the properties of the two proposed models. The results show that the unit variable cost of driving, travelers’ VOTs, travel time uncertainty, and the selection of the weight in the objective function have significant impacts on the performance of the proposed ride-sharing system with travel time uncertainty. The results also show that a feasible ride-sharing match based on deterministic travel time can become infeasible in a stochastic ride-sharing system. It is therefore important to consider travel time uncertainty when determining the matches.

A novel algorithm for peer-to-peer ridesharing match problem

Peer-to-peer (P2P) ridesharing is a promising mode of transportation that has gained popularity during the recent years. The widespread use of smart phones, mobile application development platforms and online payment systems provide new opportunities to enable P2P ridesharing. In this paper, we introduce a notion of two-sided matching to build a single-driver multiple-rider stable matching ridesharing model and propose a heuristic algorithm to establish stable or nearly stable matches. We conduct extensive numerical experiments to demonstrate the computational efficiency of the proposed algorithm and show its practical applicability to reasonably sized P2P ridesharing contexts. The results show that we can significantly increase the stability of ridesharing matching solutions at the cost of only a small degradation in system-wide performance.

Collaborative activity-based ridesharing

A new ridesharing model called collaborative activity-based ridesharing is proposed to enhance not only overall matching rates but also the matches between preferred ride partners. By coalescing the merits of two recently suggested innovative ridesharing models – the social network-based ridesharing and the activity-based ridesharing – the new model leverages people's preference to their social networks and the space-time flexibility of daily activities to improve the matching outcome. The capabilities and advantages of the proposed model are justified by a group of agent-based simulations in a realistic study area. The influence of geography on the match outcome is discussed in particular.

An Efficient Ride-Sharing Framework for Maximizing Shared Route

Ride-sharing (RS) has great values in saving energy and alleviating traffic pressure. Existing studies can be improved for better efficiency. Therefore, we propose a new ride-sharing model, where each driver has a requirement that if the driver shares a ride with a rider, the shared route percentage (i.e., the ratio of the shared route's distance to the driver's total travel distance) exceeds an expectation rate of the driver, e.g., 0.8. We consider two variants of this problem. The first considers multiple drivers and multiple riders and aims to compute driver-rider pairs to maximize the overall shared route percentage (SRP). We model this problem as the maximum weighted bigraph matching problem, where the vertices are drivers and riders, edges are driver-rider pairs, and edge weights are driver-rider's SRP. However, it is rather expensive to compute the SRP values for large numbers of driver-rider pairs on road networks. To address this problem, we propose an efficient method to prune many unnecessary driver-rider pairs and avoid computing the SRP values for every pair. To improve the efficiency, we propose an approximate method with error bound guarantee. The basic idea is that we compute an upper bound and a lower bound for each driver-rider pair in constant time. Then, we estimate an upper bound and a lower bound of the graph matching. Next, we select some driver-rider pairs, compute their real shortest-route distance, and update the lower and upper bounds of the maximum graph matching. We repeat above steps until the ratio of the upper bound to the lower bound is not larger than a given approximate rate. The second considers multiple drivers and a single rider and aims to find the top- $k$ drivers for the rider with the largest SRP. We first prune a large number of drivers that cannot meet the SRP requirements. Then, we propose a best-first algorithm that progressively selects the drivers with high probability to be in the top- $k$ results and prunes the drivers that cannot be in the top- $k$ results. Extensive experiments on real-world datasets demonstrate the superiority of our method.

Joint Pricing and Matching in Ridesharing Systems

Ride-sharing firms use pricing and matching decisions to control the ride-sharing platforms. Those decisions can be made jointly or interchangeably, which raises the following questions: Is matching optimization necessary? Specifically, is fixing the matching decisions to a simple rule and optimizing only the pricing decisions enough to achieve the optimal performance? In order to answer these questions, we study the interplay between pricing and matching decisions of a ride-sharing firm. There are many studies in the ride-sharing literature that optimize the pricing decisions under an assumed matching policy. However, we show that ignoring matching optimization can result in subpar overall performance. We formulate a stylized ride-sharing model that captures customer and driver behaviors and geospatial nature of the system. Customers are both price and delay sensitive, and drivers are strategic and self-scheduling. We prove that optimizing the matching decisions have first-order effect on the system performance. We show that fixing the matching decisions and optimizing only the pricing decisions does not maximize the number of matchings in general. Similarly, we show that fixing the pricing decisions and optimizing only the matching decisions is not optimal in general. Finally, we show that optimizing in only one dimension (either pricing or matching) has no benefit to the firm under some conditions, whereas joint pricing and matching optimization can lead to a significant performance increase.

A Glimpse into the Sharing Economy: An Analysis of Uber Driver-Partners in Egypt

The paper offers a glimpse of the driver side of the ride-sharing model in Egypt, provided through the eyes of the driver-partners themselves. Based on a field survey of a sample of Uber driver-partners in Cairo, I study this new form of work against the backdrop of expanding unemployment of the youth and the educated, and continuing employment in the informal sector. I emphasize the relevance of context, particularly the demographic, urban and economic challenges facing Egypt after the uprising of 2011.