Carsharing Companies Research Articles

Carsharing equitable relocation problem: A two-stage stochastic programming approach with learning-embedded endogenous uncertainty in demand

Uncertain demand may exacerbate the imbalance of the supply–demand for a one-way carsharing system and complicate vehicle relocation decisions. To consider the effect of the uncertainty, this study proposes a two-stage stochastic nonlinear programming model, integrating long-term and short-term decisions and maximizing the profit of the carsharing companies. Specifically, in the first stage, tactical decisions of fleet sizing and initial vehicle distribution are determined before the realization of the uncertain demand. Operational decisions of both operator-based and user-based relocation are optimized in the second stage. Moreover, this paper first studies the user-based relocation incentives, which affect the distribution of uncertain demand with an endogenous relationship. A learning-embedded optimization method is introduced to learn such a distribution, enabling the decision-making optimization model to achieve higher performance under the guidance of the demand uncertainty. Second, we envision an equitable relocation issue that considers an uneven distribution of the unsatisfied demand with two different equity criteria measured from the aspects of stations and OD pairs, respectively. Third, the large problem scale, the nonlinear objective function and constraints, and the endogenous demand uncertainty constitute the nontrivial challenges to the feasible solution. For solving the problem efficiently, we linearize the nonlinear terms and develop a dedicated two-phase solution algorithm with a learning-embedded trust-region method in phase I to solve the continuous relaxation problem and a mixed-integer linear programming guided iterative rounding in phase II to obtain the integer solutions of carsharing operations. The solution algorithm adaptively bridges the learning and optimization process via the trust-region method with flexible sample generation. Finally, we conduct numerical experiments based on a real-world one-way carsharing system in Beijing to demonstrate the effectiveness and applicability of the proposed method and reveal some insights for the carsharing service.

Profit and utility optimization through joint dynamic pricing and vehicle relocation in carsharing operations

Pricing is one of the main determinants of a successful carsharing business plan. Companies develop different pricing strategies to increase attractiveness, profit, and service usage. Using dynamic pricing strategies can lead to service improvement in terms of profit and better customer satisfaction. This paper presents a novel research contribution to the field of transportation policy by introducing a new framework for designing dynamic pricing strategies in carsharing operations. We develop two hybrid-pricing strategies to increase profit and user utility in car sharing and analyze the service key performance indicators. These two different hybrid-pricing strategies are based upon two different approaches: one relying on demand related information (i.e., fixed price and time-based dynamic price) and one relying on supply related characteristics (i.e., maximum profit price and availability-based dynamic price). By considering both user utility and company indicators, this model features a bi-level structure that allows for rapid implementation. The framework relies on real-world data, typically available to carsharing companies, including membership data, geographic distribution of users, fleet composition, and the location of vehicles and stations. Additionally, we propose a relocation procedure that relocates vehicles on a day-to-day adjustment process. We study the impact of these strategies in an agent-based environment capable to accurately replicate a real carsharing service that operated in the city of Munich, Germany. Once these policies are in place, results show how it is possible to increase profit and customers’ utility. Moreover, we show how an increment in profit corresponds to a reduction of the utility and vice versa. Overall, the effectiveness of the proposed hybrid-pricing strategies in improving key performance indicators such as profit and score in carsharing services is demonstrated through the positive impact of demand-based pricing combined with relocation operations, while supply-based pricing strategies were found to be ineffective in enhancing profit and booking time.

Оценка перспектив развития совместного потребления транспортных услуг в городах Российской Федерации

Purpose: To assess the prospects for the development of new forms of joint consumption of transport services in Russian cities, as well as the development of scientifically sound recommendations for companies implementing such projects. Methods: To achieve the goal of the research at the first stage the methods of systemic and comparative analysis, synthesis and other methods of general scientific cognition are used. At the second stage, the authors use the method of interviewing by means of questionnaires of potential clients, summary and grouping of the obtained results, statistical analysis of the structure of the responses received. The information base of the study has been the materials of open sources, periodicals, as well as Internet resources, including open data of the official sites of carsharing companies. Results: The main scientific result is the evaluation of the prospects of "people's" carsharing in Russia, taking into account the place and role of this business model in the strategic documents, as well as the results of processing the questionnaires of potential customers of such a transportation service. The main factors hindering the development of the business model of "people's" carsharing in the cities of the Russian Federation, as well as the directions of development of such transportation services have been determined. Practical significance: As a result of the study, the authors have formed practical recommendations for companies implementing such projects, as well as for the executive authorities of the subjects of the Russian Federation responsible for transport services. The possibilities of stimulating the initiative of "people's" carsharing are determined.

The carsharing market in Russia: Factors of formation and development trends

The paper identifies the factors that have influenced the development of the carsharing market in Russia, analyses current trends and identifies the problems hindering the development of the service. As the research shows, the carsharing market developed at a fast pace in 2015-2021, not counting the pause in the first half of 2020 caused by the coronavirus epidemic. This was facilitated by the benefits that carsharing offers to users, as well as government support for carsharing services. It is revealed that users’ interest in this service grew from 2015 to 2019, then the trend changed to a negative one. Analysis of internet users’ search queries reveals that their awareness about carsharing has increased by 2022 as compared to 2019. In 2022, the market for carsharing services saw dramatic changes due to the departure of a number of foreign car manufacturers and difficulties with the supply of spare parts. It is concluded that the carsharing market has a high degree of monopolisation. The most significant factors limiting the development of carsharing include the rapid growth of tariffs, difficulties in renting a car within walking distance, user concerns about possible damage to the car, technical failures, leakage of personal data, and a lower sense of privacy, security and comfort compared to a private car. The elimination of the identified negative factors (curbing fare increases, state support of carsharing companies in the regions, improved accessibility of carsharing, etc.) will contribute to a more rapid development of this service.

Application of Simulation Method and Regression Analysis to Optimize Car Operations in Carsharing Services: A Case Study in South Korea

A carsharing service is a form of public transportation that enables a group of people to share vehicles based at certain stations by making reservations in advance. One of the common problems of carsharing is that companies can have difficulty optimizing the number of vehicles in operation. This paper reports on investigations of the relationship between the number of cars and the number of reservations per day with either the acceptance ratio or utilization ratio based on the commercially-operational dataset of a carsharing company in Korea. A discrete event simulation is run to analyze a round-trip service for every possible number of cars and number of reservations with the output acceptance ratio and utilization ratio. The simulation data revealed that increasing the number of reservations with respect to a certain number of cars will decrease the acceptance ratio, thus increasing the percentage of the utilization ratio. Based on the simulation data results, a rational regression model can achieve high precision when predicting the acceptance ratio or the utilization ratio compared to other prediction algorithms such as the Multi-Layer Perceptron (MLP) and the Radial Basis Function (RBF) models. K-means clustering was used to understand the pattern and provide additional policies for carsharing companies. Consequently, opening a carsharing business is very promising in terms of profit, escalating the level of customer satisfaction. In addition, a small reduction in the utilization ratio by operators will create a large increase in the acceptance ratio.

A cooperative strategy for optimizing vehicle relocations and staff movements in cities where several carsharing companies operate simultaneously

Carsharing has become a popular travel mode owing to its convenience of use, easy parking, and low cost of using a car by those who only need it occasionally. However, because of the inadequate location of carsharing stations (station-based systems) or vehicles (free-floating systems), effectively requiring expensive and complex relocation strategies, a number of customers are lost, and some carsharing companies are facing bankruptcy. This study proposes a data-driven, dynamic, multi-company relocation method, which aims to reduce relocation costs and increase profit in one-way carsharing station-based systems through cooperative strategies. The method starts from the prediction of carsharing inflows and outflows at each station throughout the day using a new deep learning algorithm designated as “the attention-enhanced temporal graph convolutional network”. It adopts an encoder-decoder structure to simultaneously capture the temporal and spatial carsharing usage patterns. A two-phase integer programming model is proposed to optimize the process of vehicle relocation and staff rebalancing with cooperative relocation strategies: the sharing of relocation staff, the sharing of vehicles and stations among the different companies. An adaptive large neighborhood search based heuristic approach is implemented to solve the two-phase model. Based on the 6-month travel records from four carsharing companies operating simultaneously in Fuzhou, China, the proposed model and cooperative strategies are assessed. The results show that the total profit of the four carsharing companies can be increased by 25.49% with the cooperation of staff and vehicles. In addition, we prospect the future relocation with automated vehicles, whereby the profit can be increased by 46.69% without the need to employ the relocation staff.

Optimization of personalized price discounting scheme for one-way station-based carsharing systems

As a promising pattern of shared mobility, one-way carsharing systems provide users with flexible car-rental services and have enjoyed rapid development in the past few years. Almost all previous relevant optimization studies assume that the objective of the pricing or discounting scheme is to balance the fleet from the standpoint of the carsharing companies. However, the users’ preferences for various levels of service have not been explored sufficiently. This paper introduces a carsharing personalized price discounting scheme (CSPPDS) problem for one-way reservation-based11It is assumed that users make reservations for their trips one day ahead of set-off in this work. services, considering user acceptance of the discounted price and various levels of service that the system provides to the users. To maximize the system’s profit, the presented CSPPDS problem decides the discounting scheme and whether to accept the requests. Meanwhile, the actual requests travel plans that can be different from the users’ original travel requests in pick-up locations and start times are optimized. A real-world case study based on a one-way carsharing system in Beijing is conducted to demonstrate the applicability of the presented model.

Визначення оптимальної кількості транспортних засобів для функціонування каршерінгу в місті Києв в рамках вдосконалення транспртної системи міста

The problem of ensuring the quality of transport services provided to the population by a car-sharing firm is studied. For a formalized description of functioning process car-sharing company, information, scientific and experimental bases were used, with the help of which it became possible to solve transport problems in the conditions of constant improvement of the city's transport system. The main purpose of this study using a mathematical apparatus is to obtain the resulting indicators, which will provide recommendations for improving the level of customer service by car-sharing companies, taking into account the possible effects of certain system parameters. A mathematical model was used to conduct the study, which takes into account the main indicators that affect the determination of the optimal number of vehicles of the company to ensure a high level of customer service of the car-sharing company. In the course of the work, the most important indicators influencing the quality of transport services were identified and a method for determining the optimal number of vehicles to qualitatively meet the needs of customers of a car-sharing company was proposed. Statistical data on car-sharing needs in Kyiv were obtained on the basis of the Getmancar application. The function of exponential distribution of travel time from their number during the study period of 13 days was obtained. Necessary calculations were performed and recommendations were received to increase the fleet of car-sharing vehicles from Getmancar to 293 units. This will increase the density of vehicles in the area, thus ensuring a 100% probability of finding a free vehicle at a distance of 0.6 to 1.2 km. It is proposed to apply the obtained results in the process of optimization of the transport network of Kyiv. The proposed method is recommended to be used during the reorganization of the city transport system, urban redevelopment, forecasting the results of the transport system during the intensive development of the transport network.

What travel scenarios are the opportunities of car sharing?

In face of high-investment and low-revenue crisis, it is critical for new-born car-sharing companies to focus on niche market. Existing literatures have already discussed the niche market of car sharing based on people’s irregular travel demands and scenarios; however, current research findings are still lack of adequate travel data support. Aiming to solve this problem, we develop a travel scenarios mining method that first define land usage types of travel Origin-Destination (OD) locations using spatial clustering analysis of city’s Points of interests (POI) data, and then discover the most representative travel scenarios using association rules mining method with car-sharing records data. Applying this approach to the car-sharing service case in Beijing, China, we find that: day-time business trips and evening entertainment trips around the city centre, commuting trips in off-peak time, and short-distance city travel for tourism are three travel scenarios suitable for car sharing’s niche in the initial stage of market entering. Furthermore, spatio-temporal consumption profiles and competitive advantages of car sharing in the three travel scenarios are analyzed. Finally, theoretical and managerial implications are discussed. In this study, we address the question of finding niche market and suggest that it is critical for car sharing industry treading through the crisis in the early stage of development.

Carsharing Business Models’ Strategizing Mindsets Regarding Environmental Sustainability

Carsharing is seen as one of the most promising business model innovations for transforming the mobility system towards sustainability. Yet, carsharing businesses are faced with similar sustainability paradoxes as business in general; whether a company’s business model is pushed towards sustainability depends on its strategizing mindset. However, few studies have addressed the strategizing mindsets of carsharing providers. The present study addresses this gap with an empirical assessment of (1) the relevance of environmental sustainability in the strategies of carsharing corporations compared to their proclaimed vision and (2) the underlying type of strategizing mindset, i.e., corporate purpose and vision. In using a Sustainability Balanced Scorecard approach in a reversed manner, accompanied by an objective hermeneutic methodology, we reconstructed strategy maps out of two distinct carsharing business models, unveiling for each company its strategic reasoning, its understanding of corporate purpose and its underlying strategizing mindset. The results reveal that none of the carsharing businesses follow a mindset that orients their strategy to a systems-based view and accordingly do not provide solutions to environmental problems. The study not only adds to our understanding of carsharing companies, it also introduces a conceptual framework that allows the investigation of purpose in comparison to vision, revealing a company’s strategizing mindset.

Enhancing the performance of one-way electric carsharing systems through the optimum deployment of fast chargers

One-way electric carsharing systems (OWECS) provide environmentally friendly mobility that enables users to commence and terminate their trips at a preselected station within a region. However, the operations of OWECS are complicated mainly due to: (i) unbalanced spatial and temporal distribution of demand which causes shortage or surplus of vehicles at stations, and (ii) excessive battery charging requirements that can reach up to 8 h. To ensure vehicle availability to their customers, carsharing companies hire personnel to relocate vehicles to restore the demand–supply balance, and increase the number of trips served. On the other hand, fast charging technologies can reduce the charging time drastically and can help carsharing companies to cope with the inefficiencies arising from excessive battery charging times. Therefore, fast charging technologies have the potential to enhance the operational performance of OWECS.In this study, we propose an integer programming model to determine the number and location of fast chargers to be implemented in OWECS while considering vehicle relocation and battery availability. We propose a time–space–battery level network model which allows to track battery levels of each vehicle. As the number of stations increases, the number of relocation variables created increases polynomially which makes the model intractable for problem instances found in real world OWECS. Therefore, we are introducing three heuristics, two of which are based on the concept of reduction of vehicle relocation variables, while the third heuristic is based on station grouping. The heuristics that are categorized as relocation reduction type heuristics generate only a fraction of all possible relocation arcs significantly reducing the number of relocation variables. The third heuristic reduces the number of variables by grouping the stations and solves the problem initially at an aggregate level using groups of stations instead of individual stations, while subsequently optimizes the location of chargers within each group of stations.We tested different approaches combining heuristics and tested them on smaller instances with exact solutions to identify the approach that is both accurate and efficient. We applied the selected heuristic approach on real-life instances taken from an OWECS based in Nice, France. The results suggest that the use of the proposed modeling and solution framework leads to a configuration of the network of fast and conventional chargers that improves the profitability and the number of trips served by the OWECS system.

Who gives up a private car for a car-sharing service? An empirical case study of Incheon City, South Korea

Car-sharing services are an example of sharing economy business models in urban transportation. Car-sharing is attracting global attention as a practical test subject within sharing economies. A few studies have been conducted on the effects of car-sharing, but little research has been performed on the effects of car-sharing on the disposal of owned cars and the purchase of new cars. This study analyzes the characteristics of people who change car ownership plans or intentions to purchase a private car after using car-sharing services. We conducted a survey on car-sharing users, focusing on the question, “Has there been a change in private car ownership and purchase plans after using car-sharing?” We used a multinomial logistic regression model for statistical analysis. Based on our main findings, we suggest four strategies to promote car-sharing. First, there should be a focus on car-sharing coupons or promotional brochures for women who are considering buying a new car. Second, there should be promotions for elderly users of car-sharing. Third, car-sharing stations should be established near residences with low household membership, low household income, and limited space for parking. Fourth, the service management status of car-sharing companies contracted with local governments should be assessed to maintain high satisfaction levels in users. This study is meaningful in that it presents specific policies to both increase the use of car-sharing and reduce private car ownership.

Providers and Practices: How Suppliers Shape Car-Sharing Practices

Social practice theories can be useful for studying changes in mobility systems as regards automobility practices. However, many studies address the demand side and the user practices of consumers, without examining the supplier side. This Norwegian study focuses on the role of providers in car-sharing practices, using data from household interviews with car-sharing users, stakeholder workshops, and interviews with providers of car-sharing services. How are car-sharing providers shaping car-sharing practices, and with what implications? How do business models and platform technologies affect car-sharing practices? The results show how new car-sharing service companies, in addition to established firms such as car dealers and car rental companies, affect car-sharing practices by offering several alternatives for accessing cars. The implications of this are discussed, noting how car-sharing practices are shaped by car-sharing providers in the recursive relationship between practice-as-entity and practice-as-performance. The conclusions offer a critical view of how the providers contribute to various kinds of car-sharing understandings, as well as the implications for policy and practitioners.

Assessing Two-way and One-way Carsharing: an Agent-Based Simulation Approach

Carsharing companies can customize their service by adopting different pricing schemes and offers with the aim of increasing fleet use and profits. Different business models have been developed such as round-trip and one-way. It is clear that, even though many aspects of the business model and operations are the same, the different way in which these services are supplied leads to a diverse response from the users. In this work, we analyze how a fixed pricing scheme affects the behavior of the members of two different carsharing systems: two-way and one-way, explicitly considering their different income distributions to analyze social equity aspects. The dynamic pricing policy is simulated in MATSim, an agent-based simulator able to generate realistic mode choices based on individual activity-travel behavior. Scenarios with a synthetic population of carsharing members for the city of Berlin are analyzed. We aim to provide an experimental analysis that addresses the different behavior of different demand sectors, categorized by income, in function of the supply distributed on the territory. Simulation results show that the two services are not in competition between each other: the two-way service is used as a substitute for private cars while the one-way system is preferred from agents who choose to use multiple types of modes during the day. The response from the different income classes tends to be similar for both services since all the users within the same purchase power have the same degree of acceptance for both systems.

나눔카 서비스의 교통형평성 평가 연구

With the recent economic recession and worsening of environmental pollution, shared economy has emerged as an alternative solution. Corresponding to global trends, Seoul City, in 2012, declared “the shared city of Seoul” project to create social value for sharing goods. In February 2013, Seoul City launched a public car-sharing service, called “Nanum Car,” in cooperation with private car sharing companies. The private sector dominated the car-sharing business; accordingly, rental places were arranged in high demand areas. However, after Seoul City partook in the car-sharing service, it became important to improve traffic equity for the public. In this study, traffic equity variables that can define the characteristics of a car-sharing service were determined; additionally, data related to the exact location of the “Nanum Cars” and the number of vehicles deployed were collected. These data were used to analyze whether the “Nanum Car” service considers traffic equity. This study evaluated the car-sharing arrangements in terms of traffic equity in cooperation with Seoul city and private car-sharing companies. This study is more diverse that previous studies and provides recent research data. Therefore, this study can be used to establish the future direction of the “Nanum Car” business.

Socio-Economic Risks of Carsharing Customers in the Moscow Megalopolis

The article presents the results of sociological research of socio-economic risks of carsharing customers in the Moscow megalopolis. Carsharing is considered as one of the new types of sharing economy stimulated our days both by information technologies development and postmodern values of people. Currently carsharing is progressing rapidly in the Russian capital that makes relevant its various aspects of the investigation. There is a real contradiction when, on the one hand, various paid services, including carsharing, are extending and on the other hand corresponding risks for people are growing too. The main goal of the research is discovering of socio-economic risks of Moscow carsharing customers both real and potential. The primary method of empirical sociological of carried out in April-May, 2019 is in-depth interviews with actual and potential customers of Moscow carsharing companies. The study reveals that both actual and potential carsharing customers have some fears related to technical and sanitary conditions of a rented car, traffic accidents, insurance, billing etc. At the same time, we found out that the shared usage of a car itself actual the customers more responsible. It studies that development of carsharing in Moscow is constrained mainly by subjective factors related to the mentality of Russians (fears, phobias) and by such factors as sufficiently low level of trust both to people and social institutions. Obtained results are correlated to the statements made by other researchers and let to map out directions of further study of this field.

Bi-level optimisation model for one-way electric carsharing stations based on survival model

Electric carsharing is considered as one of the leading solutions to alleviate traffic problems and promote the sustainable development of cities. However, extensive planning method is used to determine the carsharing station and capacity in the case of mismatch between supply and demand, which is not conducive to bring social benefits into play and leads to the imbalance between investment and profit. The reason is that the strategic planning of the stations has not been well studied, especially when considering the maximum profit and profit stability simultaneously. In response, an electric carsharing station optimisation method that considers various factors is proposed. A bi-level optimisation model for electric carsharing station, which considers profit of company and convenience of user, is constructed based on the survival analysis. Then, a case study is conducted in Lanzhou, China to validate the model. Results show that the method proposed is conducive to promoting the sustainable profit of electric carsharing companies. Budget, survival distance and cover rate have significant impacts on the profit of companies. In addition, improving profit value and stability requires trade-off under budget constraints in electric carsharing system.

An optimization model and genetic-based matheuristic for parking slot rent optimization to carsharing

Carsharing represents a major example of smart mobility service that allows a customer to rent a vehicle for a limited amount of time paying a per-minute fee. It may relieve people of the costly and non-sustainable burden of owning a car, especially when residing in a city. Though the spread of carsharing may bring significative benefits to (smart) cities, its penetration can be obstructed by non-up-to-date regulations, which can be still tied to a non-smart vision of mobility. In this study, we provide an overview of remarkable city regulations for carsharing, particularly highlighting the importance that parking policies can have in favouring the diffusion and use of carsharing services. Given such importance, we characterize the optimization problem of a local government that wants to analytically choose the best subset of parking slots to rent to carsharing companies, in order to improve urban mobility. To model and solve the problem we propose a new Binary Linear Programming problem and genetic-based matheuristic. Finally, we present results from computational tests referring to realistic data of the Italian city of Rome, showing that our optimization approach can return a fair territorial distribution of the parking slots, satisfying various families of constraints limiting the distribution.

Dynamic Pricing on Round-Trip Carsharing Services: Travel Behavior and Equity Impact Analysis through an Agent-Based Simulation

Carsharing companies can customize their service by adopting different pricing schemes and offers with the goal of increasing service attractivity, fleet usage and profits. Dynamic pricing strategies can be designed to adjust and balance cars’ availability temporally and spatially; they may pose some questions regarding customer equity, since such measures could impact their activities and mode choice. In this paper, we develop an experimental design for conducting an explorative analysis of how availability-based and time-based dynamic pricing schemes impact demand and supply performances. The strategy is simulated in the open-source agent-based software MATSim and compared to a fixed pricing policy scheme. Two spatial distributions of the value of time for the population of Berlin are applied (radially and coaxially) to systematically analyze agents’ behavior response to these pricing policies. Results confirm that when dynamic pricing is applied people with low value of time tend to abandon the carsharing mode in favor of other modes of transportation. Overall, the strategy applied in this study appears to be unfair, since it hinders low income groups from using the carsharing service.

Optimal Model for Carsharing Station Location Based on Multi-Factor Constraints

The development of the sharing economy has made carsharing the main future development model of car rental. Carsharing network investment is enormous, but the resource allocation is limited. Therefore, the reasonable location of the carsharing station is important to the development of carsharing companies. On the basis of the current status of carsharing development, this research considers multiple influencing factors of carsharing to meet the maximum user demand. Meanwhile, the constraint of the limited cost of the company is considered to establish a nonlinear integer programming model for station location of carsharing. A genetic algorithm is designed to solve the problem by analyzing the location model of the carsharing network. Finally, the results of a case study of Lanzhou, China show the effectiveness of the establishment and solution of the station location model.