Carsharing Systems Research Articles

Simulation of One-way Carsharing Systems : Operating Parameters and Relocation Policy Analysis

The concept of carsharing involves sharing a small number of reserved cars to be used individually by a larger number of people as required. This study examines the operating parameters of one-way carsharing systems in order to determine the appropriate operating conditions that minimizes the lost sales rate. Five operating parameters are tested in this study: the number of stations, the average number of vehicles per station, the rate of one-way trip, the average number of staffs per station, and the relocation policy. The performance of round-trip carsharing systems is also compared to that of one-way carsharing systems. A simulation model is developed and simulations are performed to determine the appropriate combination of operating parameter and levels. The simulation results show that the average number of vehicles per station is the most critical parameter. Other key findings obtained from this research are as follows. First, applying the appropriate relocation policy to one-way carsharing systems can allow more customers to rent vehicles than the traditional round-trip carsharing systems. Second, the appropriate relocation policy should be selected based on the average number of vehicles per station in order to minimize the lost sales rate. Third, the number of stations does not affect the lost sales rate. This study findings will provide tools to understand impact of the carsharing system parameters on the efficiency of the carsharing operations.

Travelers’ Acceptance of Electric Carsharing Systems in Developing Countries: The Case of China

Emerging electric carsharing (EC) systems have demonstrated their advantages and attracted public attention. The number of EC systems is growing throughout the world, especially in metropolitan areas in developing countries. For successful implementation, developers need to understand the public acceptance of EC services. In this study, we sought to determine the factors that affect EC acceptance in the context of developing countries. The study involved 437 individuals, aged between 18 and 65 years, who were randomly sampled from an EC service area in China. The determinants of EC acceptance were investigated based on an extended version of the Unified Theory of Acceptance and Use of Technology (UTAUT) and tested by using Structural Equation Modeling. The results indicated that hedonic motivation (HM) has a powerful effect on behavioral intention (BI) to use the EC service in the future. Performance expectancy (PE), effort expectancy (EE), and familiarity with the carsharing concept (FM) also influenced EC’s acceptance. However, the impact of social influence (SI) did not emerge from this study. The results also revealed that gender moderates the effects of EE and FM on BI. Age moderated the effect of FM on BI and unexpectedly moderated the impact of HM on BI. The present study confirmed the validity of the UTAUT research model in predicting the intention to use an EC system in developing countries. Implications and recommendations for government and EC developers are also discussed.

Investigating the effect of temporal and spatial flexibility on the performance of one-way electric carsharing systems

One-way electric carsharing systems provide an environmentally friendly option for facilitating urban mobility needs. However, the management of one-way electric carsharing systems presents operational challenges stemming from the need to relocate cars in order to strike an optimum balance between demand and supply. As a result, the cost associated with vehicle relocation operations represents a significant proportion of the total operating cost. In the context of electric carsharing systems, the problem of vehicle relocation is further exacerbated by the car battery charging requirements. The introduction of temporal and spatial flexibility regarding the pick-up and drop-off of vehicles provides the means of improving the efficiency of one-way electric carsharing systems. However, the literature currently lacks models that can be used to investigate the effect of temporal and spatial flexibility on the performance of one-way electric carsharing systems. In this paper, we are introducing an integrated modeling and solution framework for investigating the effect of temporal and/or spatial flexibility, and different options for processing trip requests to the profitability and utilization of one-way electric carsharing systems. The application of the proposed framework to a realistic size system suggests that spatial flexibility has a stronger effect on the system performance than temporal flexibility. Furthermore, both spatial and temporal flexibility can increase the profitability of the system by serving more customers with fewer vehicle relocation needs.

Queueing Network with Moving Servers as a Model of Car Sharing Systems

We consider a queueing network with a finite number of nodes and servers moving between the nodes as a model of car sharing. The arrival process of customers to various nodes is defined by a marked Markovian arrival process. The customer that arrives at a certain node when there is no idle server (car) is lost. Otherwise, he/she is able to start the service. With known probability, which depends on the node and the number of available cars, this customer can balk the service and leave the system. The service time of a customer has an exponential distribution. Location of the server in the network after service completion is random with the known probability distribution. The behaviour of the network is described by a multi-dimensional continuous-time Markov chain. The generator of this chain is derived which allows us to compute the stationary distribution of the network states. The formulas for computing the key performance indicators of the system are given. Numerical results are presented. They characterize the dependence of some performance measures of the network and the nodes on the total number of cars (fleet size of the car sharing system) and correlation in the arrival process.

Optimal charging and repositioning of electric vehicles in a free-floating carsharing system

Carsharing has received increased attention from the Operations Research community in recent years. Currently, many systems are adopting electric vehicles that require charging when battery levels fall below a given level. To do this, staff is often used to move cars to charging stations. Repositioning cars, rather than simply moving them to the closest charging station, might provide a better distribution of cars and in turn generate increased revenue and customer service while only marginally increase the operational costs. We present a mathematical model for the problem of charging and repositioning a fleet of shared electric cars. The model considers the assignment of cars to charging stations and the routing of staff and service vehicles. The complexity of the resulting mixed integer program makes it impossible to solve real world instances using a commercial solver. Therefore, we propose a new Hybrid Genetic Search with Adaptive Diversity Control algorithm. Tests based on data from a real life carsharing organization demonstrate that the proposed method can handle real size instances and that combining repositioning and charging operations can give significant benefits.

Fleet sizing for one-way electric carsharing services considering dynamic vehicle relocation and nonlinear charging profile

This study aims to determine the electric vehicle fleet size for one-way carsharing services by maximizing the profit of carsharing operators while taking into account the vehicle relocation operations and nonlinear electric vehicle charging profile. We formulate a set partitioning model for the considered problem. A tailored branch-and-price (B&P) approach is proposed to find the exact optimal solution of the model. In particular, an effective multi-label correcting method is developed to solve the pricing problem (i.e., generate columns) within the B&P approach. A novel non-dominated charging strategy is put up to avoid the exponential growth of labels caused by the allowance of partial charging with a nonlinear charging profile. In addition to the B&P approach, two heuristic methods are put forward for solving the large-scale problems or reinforcing the B&P approach. Numerical experiments with randomly generated instances and a case study based on a one-way carsharing operator in Singapore are conducted to further assess the efficiency and applicability of the proposed solution methods. The effects of several key parameters, i.e., the fixed cost of EV, relocation cost, electricity cost, service charge, EV driving range, the charging efficiency, and the number of rentals on the performance of a one-way electric carsharing system are also examined.

Comparing Future Autonomous Electric Taxis With an Existing Free-Floating Carsharing System

When considering autonomous mobility on demand (AMoD) trends, it is probably safe to assume that they will have a large market share in the near future. In the introductory phases, users of current non-autonomous mobility on demand services such as ride-hailing and carsharing are expected to be among the first users of AMoD systems. The research presented in this paper aims to estimate fares for an AMoD system in this early stages based on rental and financial data provided by a free-floating CS provider. It demonstrates that an autonomous taxi (aTaxi) model requires less vehicles to serve the same demand resulting in the possibility to lower fares. In our model, user behavior is represented by defining three maximal waiting times and three monetary values reflecting their dissatisfaction in the case, where they cannot be served in due time. Two bipartite optimization problems for vehicle-to-user and relocation assignments build the core of the introduced aTaxi model. Fleet size and relocation parameters are chosen according to a utility function representing profit and opportunity costs of users not being served. We compute the reduction in fares to break-even with the current CS profit. Results of a case-study in Munich, Germany, indicate that one aTaxi can replace 2.8–3.7 CS vehicles. The aTaxi operator can therefore reduce fares by 29%–35% to achieve the same profit assuming the same cost structures as in free-floating CS.

What Kind of People Use Carsharing for Commuting? Case Study in Shanghai

Carsharing as a service has been growing rapidly worldwide. Its expansion has drawn wide attention in the research community with regard to the underlying driving factors and user characteristics. Despite these extensive investigations, there are still limited studies focusing on the examination of users using carsharing as a commuting mode. The answers to questions such as what kind of people would like to use carsharing for commuting and why they frequently use carsharing to commute are not clear. To enrich our understanding of these problems, this paper aims to investigate carsharing commuters in a mega city. Specifically, it intends to integrate the actual user order data with survey data from 1,920 participants to uncover the characteristics of carsharing commuters. Data from the Evcard carsharing systems in Shanghai were explicitly analyzed. Through descriptive analysis and logistic regression models, the characteristics and critical factors that affect the choice of carsharing as a commuting mode were captured. The results show that: 1. carsharing commuters mostly live or work in suburban areas in which public transport accessibility is limited; 2. carsharing commuters are more likely to be highly educated, in a higher income bracket, and older than other carsharing members; 3. high-frequency carsharing commuters own a reduced number of private cars; and 4. those high-frequency carsharing commuters with higher income are less sensitive to the carsharing costs caused by congestion. The findings in the study offer some insights into carsharing commuters and provide some supportive information for considering policies in developing carsharing systems in urban areas.

Mining Spatiotemporal Characteristics of Car-sharing Demand

Since car-sharing demand plays an essential role on the management and service of car-sharing system, this paper attempts to analyze the temporal and spatial characteristics of the car-sharing demand, aiming to discover spatial and temporal patterns and association rules. Based on a clustering algorithm (i.e., DBSCAN), the spatiotemporal characteristics of car-sharing demand are studied. On the basis, the demand is divided into various clusters in space. Furthermore, the correlations between any two clusters are studied. The results show that car-sharing demand is high on Friday, Saturday and Sunday, and has strong correlation with time and space. It is expected the results can support the management of car-sharing system, and further promote the service level for passengers.

Incentive-Based Approach to Control Demands for Operation of One-Way Carsharing System

One-way carsharing has been regarded as one of the innovative urban transportation modes since the last decade. A fleet imbalance problem frequently occurs in the system, which requires efficient vehicle relocation to cope with. An incentive-based approach could influence the users’ demands and could relieve the pressure on operator-based relocation. This paper presents an incentive-based approach involving a vehicle rewarding policy and a station rewarding policy to attract pick-up demands and drop-off demands respectively. A ranking method is proposed to determine the list of candidate rewarding vehicles and stations. The method acquires the user-app log data and the transaction data in a real operating environment for pick-up and drop-off demand prediction. Five factors for vehicles and four factors for stations are computed from the real-time data. The ranking indices are aggregated from the weighted sum of the factors. The rewarding policy and the ranking method were tested in the real operating environment of an electric vehicle sharing system in two districts of Shanghai. The result suggests that the rewarding policy with ranking method could shorten the vehicle idle time and increase the number of transactions per vehicle and per station, and also resulted in increments on profits.

Finding the relevance of staff-based vehicle relocations in one-way carsharing systems through the use of a simulation-based optimization tool

This paper proposes a real-time decision support tool based on the rolling-horizon principle that manages staff activities (relocations and maintenance) of a one-way carsharing system and considers carpooling the staff in the relocated carsharing vehicles for extra cost reduction. The decision support tool is composed of three elements: a forecasting model, an assignment model and a filter. Two assignment models are proposed and tested: rule-based and optimization. The rule-based model uses simple rules to respond to system status changes, and the optimization model is a mixed integer programing (MIP) model prepared to work in real-time. A simulator was designed to test the decision support tool and an application is done to the city of Lisbon, Portugal, showing that the benefits of staff relocations can be rather low. It was verified that the number of relocations that can physically be performed by each staff member in the case study provide only a small improvement in the revenues, which is unlikely to overcome the costs associated with hiring and staff activity.

Analysis of the Costs of Selected Car-sharing Services - a Case Study on Six Continents Analysis of the Costs of Selected Car-sharing Services - a Case Study on Six Continents

Different types of transport solutions that are to influence the sustainability of urban logistics are increasingly used around the world. The same applies to the opportunities offered by the implementation of shared mobility services. According to this idea, car-sharing systems are currently present on six continents of the world and it is anticipated that by 2025 there will be over 36 million users worldwide sharing car-sharing services. In line with this trend, it is worth paying attention to aspects that will encourage the public to use car-sharing services. Because the price is one of the main factors for the society during making a decision about using car-sharing services, the author decided to analyze the costs of car-sharing mobility. In the text basic assumptions about car-sharing services and their development were presented. Then, the article showed the analysis of the cost of using services on six continents in the line of temporary and parking fees. In addition, the work also refers to the impact of the type of vehicle fleet on the price level. The aim of the work was to assess the occurrence of differences between the services offered and the relation between costs and the assumptions of sustainable transport development.

Free floating electric car sharing design: Data driven optimisation

In this work we consider the design of a Free Floating Car Sharing (FFCS) system based on Electric Vehicles. We face the problems of finding the optimal placement of charging stations, and the design of smart car return policies, i.e, how many and where to place charging stations, and whether to ask or not customers to return the car to a charging pole.We leverage actual data containing rentals performed by Car2Go customers. We obtain information for several months worth of actual trips in the city of Turin, our use case. Via trace driven simulations, we replay the exact same trips while simulating electric car based FFCS, to accurately gauge battery discharging and recharging. With this, we compare different charging station placements, also driven by optimisation algorithms. Moreover, we observe the impact of collaborative or selfish car return policies.Results are surprisingly: just as few as 13 charging stations (52 poles) guarantee a fleet of 377 vehicles running in a 1 million inhabitant city to work flawlessly, with limited customer’s discomfort. We believe our data driven methodology helps researchers and car sharing providers discerning different design solutions. For this, we make available all data and tools to foster further studies in these directions.

Optimization of dynamic relocation operations for one-way electric carsharing systems

This study developed a new approach for the relocation operations of one-way electric carsharing systems without advanced reservation information. The developed model consists of two parts: relocation needs computation and execution plan generation. In the first part, thresholds and a Station Ruin Prevention Method for relocation computation are proposed. In the second part, an Integer Linear Programming model for relocation plan optimization is formulated. Both Vehicle relocation and the Staff rebalancing are optimized in a unified framework. Furthermore, in order to improve the efficiency of the programming, a zoning scheme was proposed to divide the large-scale network into separate zones. Finally, a simulation model, which was calibrated and validated using the field data, was built to conduct tests on the performance of the proposed method. The results showed that the number of allocation tasks and distance for allocation have been greatly reduced because of the proposed methods, and the computing time can be reduced significantly with a small loss of accomplished tasks under the zoning scheme.

Examining Travelers’ Acceptance towards Car Sharing Systems—Peshawar City, Pakistan

In recent years, car sharing has emerged as a novel alternative to private car ownership in urban areas worldwide. Potential benefits of this system include improved mobility and reduced congestion, vehicle ownership, parking issues, and greenhouse gas (GHG) emissions. This study aimed to investigate travelers’ acceptance of car sharing systems through a stated preference survey in the city of Peshawar, Pakistan. The questionnaires were distributed online via a Google form. Questions were designed from numerous aspects of car sharing systems, such as awareness of car sharing systems, attributes related to travel modes in the choice set, and demographic characteristics. A total of 453 valid responses were received. The Multinomial and Nested Logit models were employed for evaluation and analysis of survey responses. Demographic characteristics including gender, job, and income were found to be significant. Service attributes including travel time, travel cost, registration fees, and capital cost, were also significant. The multinomial logit model based on both car-owners and non-car-owners fit a little better than the nested logit model. Our findings in the present study could be beneficial for transport planners and policy makers to timely implement car sharing systems in cities in order to mitigate increased car ownership and traffic congestion.

Free-floating carsharing and extemporaneous public transit substitution

The importance of substitution patterns between public transit and carsharing has been discussed in prior literature. This study provides empirical evidence of significant interaction between the two modes. On November 24, 2015 the public transit rail system in Vancouver, Canada experienced a significant service disruption due to an unforeseen mechanical failure. This study exploits this event as a natural experiment and investigates to what extent the city's largest carsharing service was utilized as a substitute. Extensive carsharing vehicle location data are used to examine how the carsharing network responded to the transit service disruption. Empirical results show a dramatic increase in the use of carsharing vehicles during the transit outage, particularly in areas close to affected transit stations. Through regression analysis, the quantity of vehicle trips accommodated by the carsharing system in response to the outage is estimated to be approximately 800. There is clear evidence of extemporaneous substitution between public transit and carsharing, suggesting the two modes act as parts of an integrated network.

One-Way Car-Sharing Profit Maximization by Means of User-Based Vehicle Relocation

One-way car-sharing systems, which allow customers to return vehicles to the stations where they were picked up, offer greater flexibility compared to conventional car-sharing services. Nevertheless, to guarantee this flexibility, such systems have to face difficult problems of vehicle relocation in order to be ready to satisfy as many trip reservations as possible. This paper proposes a user-based relocation methodology in which the users may accept to leave the car in a different location in exchange for fare discounts. To this aim, a two-stage optimization problem is formulated for optimizing the alternative destinations proposed to users and for maximizing the profit of car-sharing operators. The extensive analysis of results shows that, with the proposed user-based relocation strategy and without the operator-based relocation, the number of rejected reservations can be significantly reduced, even with a relatively small number of vehicles and, at the same time, the operator’s profit can be increased.

Exploring Service Usage and Activity Space Evolution in a Free-Floating Carsharing Service

This paper proposes to empirically investigate the members’ behaviors over time in a free-floating carsharing system. With a continuously evolving service in terms of service area and fleet size, member usage intensity and activity space are explored with passive data streams. Members are labeled according to their usage intensity for various periods of analysis. Results show an increase in higher usage intensity classes and a change in demographic composition of new members adopting the service over time: new members are younger and a parity between both genders is reached. Activity space investigation shows that members perform a fair share of their trips to return home and that ultra-frequency members seem to perform a substantial number of symmetric trips, meaning they are more inclined to use free-floating cars for commute-like trips. Interaction with the metro network is also investigated, with a proportion of members using free-floating carsharing to access stations. When looking at the activity space formed by members’ trip ends, users seem to constantly discover new portions of the service area. Multiple clusters of activity locations are determined for each member, with a recurrence level showing a fair amount of variability among members.

Vehicle assignment and relays for one-way electric car-sharing systems

One-way electric car-sharing systems are expected to be an integral part of future transportation systems, playing an important role in reducing traffic congestion and carbon emissions. Owing to limited battery capacities and the lengthy charging process, an electric car-sharing system may not achieve the high vehicle utilization that a non-electric car-sharing system can achieve. We investigate two approaches to vehicle management, in order to circumvent battery constraints and to improve vehicle utilization rates in one-way electric car-sharing systems. In the first approach, we optimize vehicle assignment decisions, and in the second approach, we further allow vehicle relays, enabling users to complete longer trips by sequentially taking two vehicles. We propose a novel space–time–battery network flow model to determine optimal assignment and relay decisions. With an extra dimension for tracking each vehicle’s battery level, the proposed model is a single-commodity network flow model that is computationally amiable. To meet the requirement of quick responses, we propose an efficient algorithm that exploits an innovative diving heuristic to solve the resulting integer program. Numerical results show that when vehicle assignment and relays are optimized in an electric car-sharing system, we may achieve a comparable vehicle utilization rate as in a non-electric car-sharing system. In particular, optimizing vehicle assignment is essential when most trips are short trips, and vehicle relays are critical when users demand more long trips.

The Potential of Flexible Reservations in a Car Sharing System With an Auction Scheme

In the last 20 years car sharing has been a growing trend in personal mobility. Multiple aspects of these systems have been already discussed: different forms of car sharing, user's preferences and behavior, or benefits estimation. Nevertheless, the management of these systems needs to be continuously improved to remain a competitive alternative. In this work, we propose a reservation scheme to manage rental reservations of a two-way station-based car sharing system. It allows the operator to better plan the necessary vehicles at each station, and encourages the drivers to make better use of the existing vehicles, by showing flexibility in the starting rental time. The reservation scheme is organized with an auction, where drivers bet for their preferred rental start time. Drivers participating in the auction are offered a reduced rental fare, which is then complemented with the reservation fee that results from the auction. The auction is solved under Vickrey-Clarke-Groves (VCG) mechanism for combinatorial auctions, which guarantees the desired properties for the operator and a fair assignment for the drivers. The proposed scheme is tested on instances inspired by the Mobility car sharing system in Zurich, Switzerland. The results show that operators could decrease their fleets with low to no impacts on the overall rental revenues, especially when drivers show flexibility in their rental start times. For certain levels of demand price elasticity, even positive impacts on the overall rental revenues can be expected. Moreover, reservation fees are proven to partially compensate for the decrease in rental revenues provided to the auction users.