Car-sharing Company Research Articles

National-scale bi-directional EV fleet control for ancillary service provision

Deploying real-time control on large-scale fleets of electric vehicles (EVs) is becoming pivotal as the share of EVs over internal combustion engine vehicles increases. In this paper, we present a Vehicle-to-Grid (V2G) algorithm to simultaneously schedule thousands of EVs charging and discharging operations, that can be used to provide ancillary services. To achieve scalability, the monolithic problem is decomposed using the alternating direction method of multipliers (ADMM). Furthermore, we propose a method to handle bilinear constraints of the original problem inside the ADMM iterations, which changes the problem class from Mixed-Integer Quadratic Program (MIQP) to Quadratic Program (QP), allowing for a substantial computational speed up. We test the algorithm using real data from the largest carsharing company in Switzerland and show how our formulation can be used to retrieve flexibility boundaries for the EV fleet. Our work thus enables fleet operators to make informed bids on ancillary services provision, thereby facilitating the integration of electric vehicles.

Polynomial-Time Carsharing Optimization: Linear Formulation and Large-Scale Simulations

Shared mobility services are useful for low-income people that are not satisfied with public transport and cannot comfortably afford their own vehicle. Among the low-cost shared mobility services, carsharing stands out because it avoids the cost of a driver. This work applies a polynomial-time linear programming formulation to optimize different carsharing business models for the Metropolitan Region of São Paulo. Real mobility data are used, focusing on inhabitants who are able to drive. Scenarios are evaluated varying distances that clients would be flexible to walk to get to an available vehicle or parking slot, comparing carsharing business models, and considering different rental prices. Results show that it is possible to offer a profitable low-cost carsharing service without performing vehicle relocations if clients are flexible enough to walk and if only a subset of trips is selected to be served. Results also demonstrate that trips selected to be served are similar among the different business models; are concentrated on São Paulo’s downtown region; are shorter than the average trip, but otherwise behave in a similar way as compared to the complete set of trips; and the lack of parking slots may be a risk to the carsharing company.

Carsharing users’ behaviour and attitudes. The role of car availability in households

This paper provides insights into differences in carsharing users' attitudes, motives for joining carsharing, and transport behaviour between users with and without another car at their disposal. It builds on revealed and stated data about members of the oldest carsharing company in the Czech Republic. Carsharing adopters without a car utilise shared cars more intensively than carsharing users with a car available in their household. On the other hand, unlike the second group, they drive fewer kilometres by car in total. The car availability in households also influences the shift in car use after joining carsharing. The sale of a car thanks to adopting carsharing is a factor leading to a decrease in overall car use. Those who have a car at their disposal within their household have a lower probability of decreasing kilometres driven after joining carsharing. Households without an additional car available seem to be less car-dependent on average than those utilising carsharing as a second or third car. They tend to be more environmentally conscious and more inclined towards policies supporting alternative modes and restricting private car use, although both groups share these beliefs. The findings open a debate over whether carsharing increases the legitimacy of restrictive transport measures against private car ownership and use.

An Optimization Model for Structuring a Car-Sharing Fleet Considering Traffic Congestion Intensity

Ever-growing mobility and traffic congestion within urban areas make the need for a sustainable form of transport inevitable. Traffic congestion has a significant effect on the amount of energy consumption of a vehicle and, as a result, on its associated environmental impacts. Any decision-making regarding structuring a fleet without taking into account the traffic congestion level (TCL) will lead to a less sustainable fleet with higher environmental and economic costs. To address this issue, this study examines the effects of the traffic congestion intensity level on the fleet structure of an urban car-sharing company over a certain planning period. We present a new optimization framework for finding an optimal vehicle composition of the fleet of an urban car-sharing company considering the energy consumption of vehicles at different traffic congestion levels. The results show that electric vehicles (EVs) are more competitive than diesel vehicles (DVs) in high-peak traffic congestion from the outset of the planning period. In addition, we perform a sensitivity analysis to take into account the effects of specific uncertain parameters such as the energy and purchasing costs of EVs on the total cost of ownership. As expected, the purchasing price of EVs, energy prices of DVs, and increase in diesel prices have the highest impact on the total cost.

E-Carsharing siting and sizing DLMP-based under demand uncertainty

Electric vehicle (EV) sales and shared mobility are increasing worldwide. Despite its challenges, e-carsharing has an opportunity to still profit in periods of low rental demand compared to traditional carsharing. The purpose of this paper is to assess the profitability of an e–carsharing company based on distribution local marginal price (DLMP) and vehicle-to-grid (V2G) that cooperates with the distribution system operator (DSO) through a two-stage stochastic model. The AC optimal power flow (ACOPF) is modeled using second-order cone programming (SOCP) linearized by the global polyhedral approximation. The IEEE 33 bus test system and a real Kernel distribution for the EV rental demands are used in four planning cases in the GAMS environment. The results indicate that the proposed methodology does not affect EV user satisfaction. Moreover, the planning disregarding the power grid perspective is the most profitable, but the operation may not be possible in real applications due to the high-power flows via V2G. Finally, the e–carsharing planning considering the DSO perspective increased the charging cost by 1.66 % but also reduced the DLMP peak, losses, and peak demand by 2.5 %, 1.5 %, and 5.1 %, respectively. One important conclusion is that the technical benefits brought to the DSO by the e–carsharing company could be turned into services and advantages for both agents, increasing profit and mitigating negative impacts, such as higher operational costs.

An overnight relocation problem for one-way carsharing systems considering employment planning, return restrictions, and ride sharing of temporary workers

This paper studies a vehicle relocation problem for one-way carsharing systems in which vehicles are relocated by temporary workers during the night. The goal is to develop a method for constructing employment plans and work schedules simultaneously to minimize the total relocation cost. An integer programming model is proposed, which incorporates employment planning, return restrictions and ride sharing of temporary workers. An iterative optimization approach is developed to address large-scale rebalancing problems. The unbalanced stations are clustered based on their relocation demands. Considering a new employment limit in each rebalancing model, the optimization procedure iteratively solves a set of cluster relocation problems and an inter-cluster relocation problem until a convergent solution is obtained. Numerical experiments based on data from a carsharing company in Chengdu reveal two interesting findings: (1) Employing temporary workers from understocked stations costs less than that from overstocked stations; (2) Using a time-based salary contributes to 49.1% reduction in the working hours and 33.8% in the relocation costs.

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 Decision Model for Free-Floating Car-Sharing Providers for Sustainable and Resilient Supply Chains

For green and sustainable supply chains, transportation resilience is a critical issue. Car Sharing is an effective way to improve transportation resilience. The emerging car-sharing industry continues to attract a lot of investment, but few companies in the industry are profitable. Indeed, numerical experiments based on dynamic models in this paper showed that it was challenging for a car-sharing company to be profitable. As the numerical experiments followed the fractional factorial designs, from the factor analysis, it is suggested that a new car-sharing business first study the external business environment. Even if the external environment is sound, the company still needs to pay attention to internal operations management. Moreover, when the company decides the number of cars it owns and the fleet size, it should consider factors including variable daily expenses, maintenance costs, salvage value, and commission.

A Spatio-Temporal autocorrelation model for designing a carshare system using historical heterogeneous Data: Policy suggestion

As an emerging urban mobility service, carsharing has become increasingly popular worldwide. To understand customers’ needs and optimize the design of service networks, the usage of carsharing vehicles whose trips are recorded by the operators has been applied in research estimating carsharing demand. However, as a form of spatio-temporal correlated data, the underlying spatio-temporal information included in such carsharing records has not been investigated in existing models of carsharing demand. Meanwhile, due to the supply limitation of carsharing stations, some demand cannot be fulfilled and thus remains unrecorded in the operational data. Unrealized demand may lead to underestimation of carsharing demand and therefore an incorrect vehicle deployment strategy by the service providers. In view of these issues, this paper develops an innovative approach to estimating the actual demand at a carsharing station with operational data from GoGet, the largest carsharing company in Australia. The accuracy of the estimation is improved by adding spatio-temporal correlated variables as well as variables from emerging data sources such as social media. To explore the latent space-and-time correlated information, spatio-temporal autoregressive and moving-average models have been applied. Based on the results of the analysis, the paper also provides recommendations related to the operation policies of the service providers.

An optimization Model Combining Operator-Based Relocation With User-Based Relocation for Electric Carsharing Systems

This paper proposes an optimization model combining operator-based relocation with user-based relocation for electric carsharing systems, aiming to improve the low usage of vehicles and the imbalance between supply and demand. First, the back-propagation neural network prediction model of vehicle quantity demand and the distribution fitting model of energy demand are constructed. In the operator-based scheme, optimization models of inter-regional and intra-regional relocation are constructed. In the user-based scheme, a multi-objective optimization model considering the operator profit and user experience is proposed. Taking the actual operation data of an electric carsharing company in Shanghai as an example, the mixed scheduling strategy of operator-based relocation and user-based relocation is validated. Results show that: (i) users are very sensitive to price changes, and station distance and price incentive will change users’ pickup and return behavior; (ii) compared with systems without vehicle relocation, the mixed scheduling strategy can reduce the using failure rate by 6.68% and increase net profit by 42.6% in the best situation; and (iii) the using-failure rate alert line, as a switching mechanism between user-based and operator-based relocation, can play a regulatory role, and with the loosening of the alert line, the frequency of operator-based relocation intervention increases, and the using-failure rate decreases further.

Age and Gender Impact on Password Hygiene

Password hygiene plays an essential part in securing systems protected with single-factor authentication. A significant fraction of security incidents happen due to weak or reused passwords. The reasons behind differences in security vulnerable behaviour between various user groups remains an active research topic. The paper aims to identify the impact of age and gender on password strength using a large password dataset. We recovered previously hashed passwords of 102,120 users from a leaked customer database of a car-sharing company. Although the measured effect size was small, males significantly had stronger passwords than females for all age groups. Males aged 26–45 were also significantly different from all other groups, and password complexity decreased with age for both genders equally. Overall, very weak password hygiene was observed, 72% of users based their password on a word or used a simple sequence of digits, and passwords of over 39% of users were found in word lists of previous leaks.

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.

Electric carsharing and the sustainable mobility transition: Conflict and contestation in a Czech actor-network

The energy transition has seen greater progress in some areas than others. Although it accounts for a significant portion of global greenhouse gas emissions, the transportation sector has hardly broken its high dependence on oil. Within the transportation sector, passenger road transport plays a considerable role in terms of both energy consumption and emissions. This paper, therefore, aims to contribute to the knowledge and theory of sustainability transitions in passenger road transportation by addressing the question, “How do actors and technologies interact to shape the process of sustainability transitions in road transport?” To help answer this question, Actor-Network Theory is applied to a local-level case study of an electric carsharing company in Brno, Czech Republic. A review of the energy transitions literature revealed ANT as a fruitful methodological toolbox due to its unique ontology and concepts. The study reveals that both the human and non-human actors within this network indeed play crucial roles that influence its successful expansion. These roles are often contested and negotiated by the various actors, thereby dictating the development of these associations as well as the actor-network as a whole. Moreover, the roles and associations within this network in many instances emerge not through human intention or agency but in response to those negotiated issues particularly surrounding the material elements of the network. While the study focuses on a specific case, it also offers insights and lessons for the broader energy transition, particularly the process of such transition in passenger road transportation.

Challenges in credibly estimating the travel demand effects of mobility services

Mobility services including carsharing and transportation network company (TNC) services have been growing rapidly in North America and around the world. Measuring the effects of these services on traveler behavior is challenging because the results of any such analysis are sensitive to how (1) outcomes are measured and (2) counterfactuals are constructed. The lack of good control groups or randomization of assignment leaves lingering uncertainty over the contributions of selection bias and treatment effects to reported differences in travel behavior between users and non-users of these services. This paper reports on two approaches for measuring the effects of mobility service adoption on travel rate and car ownership. We first tried a pretest-posttest randomized encouragement experiment to deal with the shortcomings of poor control groups. Then, we turned to the approach of self-reported effects based on hypothetical controls to investigate whether variations in survey question presentation could influence respondents’ answers and thus lead to changes in estimated effects. The data to conduct this study came from two sources: a panel survey administered by the authors at the University of Washington (UW), and a survey by Populus Technologies, Inc. (Populus). Various statistical tests were applied to analyze the data, and the results highlight the pivotal role that the research design plays in influencing the outcomes, and manifest the fundamental challenge of establishing credible estimates of the causal effects of adopting mobility services on travel behaviors.

The construction of a high-active EVCARD online community based on user content adoption and generation model

Constructing a high-active EVCARD online community (EOC)(HEOC) is a particularly effective approach for the emerging car-sharing company to trumpet its new products/services and stabilize its customer base. Although there is plenty of research on the model of user-generated content (UGC), the model of interaction between users and marketers on the generation and adoption of UGC and marketer- generated content (MGC) in the online marketing community is very much under-explored. To fill this gap, this study proposes the user content adoption and generation fuzzy system dynamic model (UCAAGFSDM) for building a HEOC where offline activities and online experience topics are mixed in a dynamic way, namely Offline-to-Online (O2O) content continuous interaction (OCCI). In UCAAGFSDM, user content adoption model for MGC is constructed based on the information adoption theory and user content generation model is developed according to the social cognition theory. And subsequently, the system dynamic model (SDM) is built for completely and meticulously describing mechanisms of user content adoption and generation and the interaction between users and marketers on the content in EOC. Since the cognitive behaviors of user in the process of adoption and generation content are uncertain and vague, to further improve the accuracy of the proposed model, Zadeh’s extension principle and α-cut concept are used to solve the uncertain factors, which can flexibly and accurately descript the fuzzy cognitive behaviors of user variables. Based on UCAAGFSDM, the complex mechanism of OCCI in EOC is accurately depicted and optimal construction strategies for EOC are provided for community managers by comprehensively analyzing the cost, income and profit of EOC.

Adoption of Electric Vehicles in Car Sharing Market

In this study, we examine whether it is optimal to use electric vehicles (EVs) in the car sharing market and investigate the environmental impact of pulling the EVs from the market. We develop a model consisting of a profit‐maximizing car sharing company (CSC) and a population of utility‐maximizing customers. The CSC sets the number of EVs, the number of fuel vehicles (FVs), and the rental price jointly to maximize its profit. Customers decide whether to use EVs, FVs, or public transportation to complete their trips considering the rental price. We show that it is optimal to use EVs only if the charging speed, the number of charging stations, and the range of EVs are high enough. Among these three conditions, the recharging speed is the most important and the number of charging stations is more important than the range of EVs. We also find that including EVs in the car sharing market may lead to a higher total emission when ignoring customers’ other transportation choices (due to a lower rental price that results in a higher usage rate). Moreover, we consider the problem with the objective of maximizing the social welfare and find that when considering the environmental impact, governments should tax the CSC to induce a higher rental price and when ignoring this impact, they should subsidize the CSC to reduce the rental price. We demonstrate our results with the case study of Car2go. These results are in line with that the slow recharging speed may have been one of the contributing factors to that Car2go replaced EVs with FVs in San Diego.

Mining the Value Characteristics of One-Way Car-Sharing Users Based on Empirical Data

Car-sharing has been emergent in many cities all over the world due to the environmental sustainability and the diverse service for users. However, the huge investment and the long payback period hinder the development of car-sharing. To this end, this study attempts to mine the value of car-sharing users in terms of empirical data, aiming to provide suggestions for companies to enhance user management. Based on the order data of a car-sharing company, several indicators are defined. On the basis, the users are divided into three categories, namely high-value users, low-value users and potential users. Furthermore, with the proposed indicators and the personal attributes, a prediction model is developed based on random forest method to predict the category of users in advance. The results show that with the observation period of 7 weeks, the long-term value type of a user can be predicted with accuracy of more than 80%.

Adoption of Electric Vehicles in Car Sharing Market

Motivated by the news that Car2go replaced all of its electric vehicle fleet with gasoline-powered cars starting from 1 May 2016, we examine whether it is optimal to use electric vehicles (EVs) in the car sharing market and investigate the environmental impact of pulling the EVs from the market. We develop a model consisting of a profit-maximizing car sharing company (CSC) and a population of utility-maximizing customers. The CSC sets the number of EVs, the number of fuel vehicles (FVs), and the rental price jointly to maximize its profit. Customers decide whether to use EVs, FVs, or public transportation to complete their trips considering the rental price. We show that it is optimal to use EVs only if the charging speed, the number of charging stations, and the range of EVs are high enough. Among these three conditions, the recharging speed is the most important and the number of charging stations is more important than the range of EVs. We also find that including EVs in the car sharing market may lead to a higher total emission (due to a lower rental price that results in a higher usage rate). Moreover, we consider the problem with the objective of maximizing social welfare and find that when considering the environmental impact, the government should tax the CSC to induce a higher rental price and when ignoring this impact, the government should subsidize the CSC to reduce this price. We demonstrate our results with the case study of Car2go in San Diego and find that Car2go replaced EVs with FVs likely due to the slow recharging speed.

Trip chain based usage patterns analysis of the round-trip carsharing system: A case study in Beijing

In recent years, the concept of carsharing is rapidly gaining popularity in China, and the round-trip carsharing has become a common mode. However, few studies have revealed the role of round-trip carsharing in users’ travel. In this study, the round-trip GPS data provided by a carsharing company in Beijing, China is used to analyze the users’ usage patterns based on their trip chains. Through the extraction and analysis of trip information, all trip chains are grouped into three clusters, each of which has a different usage pattern. Then the consumption features and the shared car pick-up and return time of these three patterns are discussed. Further, the Bayes’ rule is used to predict the activity purpose, and the proportion and spatial distribution of different purposes are analyzed. Results reveal that the carsharing program presents multiple usage patterns to meet the different travel needs of users. Price incentives like coupons, discounts, and packages can attract more shared car trips. Users' demand for price incentives increases with longer travel distance and time. Also, users’ usage of vehicles and parking spaces has obvious peak hours. The spatial distribution of user activities has distinctly different hotspots. This paper can be beneficial for operators to set a reasonable pricing plan and provide better services.

Growth of Sharing Economy in Hungary; Long Distance Car Sharing - A Case Study of Oszkár

Rapid growth of sharing economy in the last two decades is the outcome of a paradigm shift in global capitalism and societal values. Based on digital identity and the Trust and Reputation Index, IT platforms have brought together strangers who under new social construct, share under-utilized capacities and assets with those who need them. Radius of trust which was initially confined to family and friends; now encompasses strangers who speak no common language and who live oceans apart. Hungary is no exception to this global shift. Sharing economy in Hungary has registered healthy growth specially in the areas of transportation and accommodation. Oszkár, a long-distance car-sharing company presents a good example of this paradigm shift in societal values and sharing with strangers. This platform has recorded impressive growth of over 67% between 2015-2018 with very positive customer reviews. Moreover, this represents an environmentally-friendly sustainable practice which successfully reduces carbon foot-print and traffic congestion.