Carsharing Systems Research Articles

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.

A hybrid adaptive large neighborhood search and tabu search algorithm for the electric vehicle relocation problem

With the raising environmental worries, the sharing economy and electric vehicles gain popularity rapidly. It is crucial to relocate vehicles to make them available for customers in the car-sharing system. In this paper, we study the electric vehicle relocation problem where the triangle inequality may not hold. The objective is to determine the routing schedules for staffs to re-balance the spatial and temporal distribution of electric vehicles with the highest total profit of the relocated requests. Constraints including time windows, limited durations and charging requirements are considered. To solve this problem, we develop a hybrid adaptive large neighborhood search and tabu search algorithm, where a tailored duration computation scheme is embedded. Extensive computational experiments are conducted based on randomly generated instances. The results demonstrate the competitive performance of our algorithm both in solution quality and solving time.

Station Location Optimization for the One-Way Carsharing System: Modeling and a Case Study

Carsharing is regarded as a new mode of transportation that can meet the diversity of travel demands. Carsharing systems have different operating modes, and one-way systems are more widely used since cars can be dropped off at any station. However, their planning involves a series of joint decisions regarding the number, size, and location of stations, as well as the fleet size. This paper develops a data-driven mixed-integer linear programming (MILP) model for planning one-way carsharing systems that consider the spatial distribution of demand and the interacting decisions between stations. The characteristics of existing stations and their spatiotemporal correlations are an important part of the model. To solve the MILP model, the extension of the Benders decomposition algorithm is adopted. The practicality of the proposed approach is demonstrated in a case study in Beijing, China. The results show that the existing planning of carsharing could result in a serious waste of resources. In contrast, the proposed method can obtain effective results in a reasonable time. The location results corresponding to a different rate of satisfied demand show that increasing the parking spots to improve the interaction between stations can effectively reduce the cost of operations. It should be noted that this paper only considers the benefit of operators. Future works will be carried out to optimize the one-way carsharing system by considering the benefits of operators, as well as the benefits of users and society. In addition, the impact of COVID-19 will be taken into account in future modeling and case studies.

Electric car sharing stations site selection from the perspective of sustainability: A GIS-based multi-criteria decision making approach

It is undoubtedly that the environmental and economic benefits will increase by integrating electric vehicles, which are the vehicles of the future, with the car-sharing system. The problem of determining the locations of electric vehicle charging stations (EVCS) and service areas of the car-sharing system appear with the integration. Environmental and energy concerns are the biggest driving factor behind electric car-sharing systems. In this study, Geographic Information Systems (GIS) based fuzzy multi-criteria decision making (MCDM) method is proposed for the solution of site selection problems of electric car-sharing stations (ECSS). To do so, a three-step solution methodology is developed: (i) determining 20 sub-criteria and weighting the criteria with fuzzy Analytic Hierarchy Process (f-AHP), (ii) obtaining a suitability map for potential ECSS via GIS, (iii) sorting the performance levels of ECSSs assigned according to the suitability map by Elimination and Choice Translating Reality (ELECTRE). The proposed methodology is applied for Istanbul, a metropolitan city in Turkey as a case study. The results show that the most suitable areas for ECSSs in the European and Anatolian side, Istanbul is an intercontinental city, are the south-east part and south-western part, respectively.

One-way carsharing service design under demand uncertainty: A service reliability-based two-stage stochastic program approach

This study proposes a two-stage stochastic program approach to jointly optimize the planning and operational decisions for one-way carsharing systems accounting for demand uncertainty. The two-stage stochastic model is constructed in the way that stage-one determines the strategic deployments, including the number of vehicles, personnel and parking lots at each carsharing station, which is referred to as the regular service. Upon the realization of the random demand, stage-two solves the ad hoc service and optimizes vehicle relocation and personnel assignment at the operational level. The ad hoc service is integrated into the model, which serves the excess demand that cannot be covered by the regular service. For computational efficiency, we introduce the concept of service reliability (SR) to decompose the original two-stage stochastic formulation into separable sub-problems. A SR-based gradient solution procedure is then applied to efficiently solve the problem. The performance of the proposed model and algorithm is demonstrated with numerical experiments on a toy and a realistic network. Sensitivity analysis on demand variations and ad hoc service costs is further conducted to evaluate impacts of these key factors on the system performance. Results show that up to 20.49% improvement in the profit can be achieved by the proposed stochastic approach compared to the solution obtained from the historical average model.

Branch-and-price-and-cut for the electric vehicle relocation problem in one-way carsharing systems

We study the electric vehicle (EV) relocation problem that arises in the practice of one-way carsharing systems. In this problem, users are allowed to pick up an EV from one parking station and return it to another station. To maintain a spatially balanced distribution of vehicles, the system creates a set of pickup and delivery requests of EVs, which are to be served by a team of workers. The objective of this problem is to find a set of routes for workers to fulfill EV pickup and delivery requests, such that the total operational profit is maximized. The battery level of EV and the working duration limit for workers make the problem complicated and challenging. We formulate a set-packing model for this problem, and design a branch-and-cut-and-price algorithm to solve it. Particularly, we design a bi-directional label-setting algorithm to deal with the pricing subproblem, and implement two acceleration techniques, namely, decremental space relaxation and heuristic column generator. We also consider another objective of minimizing the total route duration for this problem, and the proposed algorithm still works with some adaptations applied. On a set of randomly generated instances, we conduct extensive computational experiments, and the results demonstrate the effectiveness of the proposed algorithm. Besides, the impacts of instance parameters and the efficiency of acceleration techniques have been analyzed.

Assessing Equity in Carsharing Systems: the case of Munich, Germany

This paper shows an application of a multi-agent transport simulation to evaluate equity effects of the introduction of a carsharing system. Using vehicles, members, and planning data of Oply, a carsharing service that operated in Munich until March 2020, we analyze the evolution of the distribution of costs and benefits among the inhabitants of this city. By explicitly introducing the income as an active part of the utility calculation we evaluate how offering a new mode of transport impacts the score of the agents. Two scenarios are employed to assess equity in economic terms and accessibility terms. Two different outcomes are expected: firstly, as a high pricing service, carsharing will favor high-income agents, thus skewing benefits towards them; secondly, we show that the granularity of this agent-based simulator makes it a handy tool when conducting policy evaluations on the introduction of a carsharing system.

Safety and Security of the Car-Sharing System

In the SECREDAS project, the car-sharing system embodies one of the laboratory demonstrators (pilots). The system consists of an embedded device for car access, a backend server, and a mobile app. In addition, the system can be easily adapted to an access control system in general, such as car fleet management. Our team designed, implemented, and validated the system in a laboratory environment, therefore TRL (Technology Readiness Level) 4 was achieved. The implementation was also supported by design patterns developed in the project. Moreover, we addressed the security, safety, and privacy issues. This article reports the progress of work and the results achieved. Key aspects such as Identity management, Representative State Transfer (REST) API (Application Programming Interface), threat assessment and key transfer methods are also described in more detail. In this work, the symmetric rent token (car key) is heavily used; however, other techniques are presented.

Dynamic Inventory Relocation for a One-way Electric Car-Sharing System with Uncertain Demands

Dynamic Inventory Relocation for a One-way Electric Car-Sharing System with Uncertain Demands

EtherRider: A Decentralized Intercity Ride-Sharing Platform using Block-Chain Technology

Ridesharing entails the sharing of journeys in order to make optimal use of fuel by allowing people to go along the same route to share rides. It allows regular passengers to share trips with others, having the additive benefit of lowering travel costs and reducing traffic congestion. Most current ride choices rely on a centralized authority to enable the system, leaving it vulnerable to faults at a specific point in the system and raising concerns about privacy disclosure to attackers acting both within and outside. Furthermore, they are vulnerable to external threats and fraud, and the payment made by the current ride-sharing service provider is rather costly. As a result, we have proposed the system named EtherRider, based on the Ethereum blockchain technology. EtherRider enables drivers to provide transportation services without the need for a central system. Both the passenger and the driver will know about sharing ride details, secure their travel details, such as pick-up and drop-off locations, arrival/departure times, and secure payment through the ethereum blockchain.With a distributed ledger, drivers and riders could create a more user-driven,value-oriented marketplace.In the context of car-sharing systems, our work also indicates that the design of such an integrated platform is dependent on striking the correct balance between important design concepts (such as security and privacy, authenticity, traceability and reliability, scalability, and interoperability).

Real-time Vehicle Relocation and Charging Optimization for One-way Electric Carsharing Systems

Real-time Vehicle Relocation and Charging Optimization for One-way Electric Carsharing Systems

Routing and Charging Scheduling for the Electric Carsharing System with Mobile Charging Vehicles

Routing and Charging Scheduling for the Electric Carsharing System with Mobile Charging Vehicles

Collaborative strategic and tactical planning for one-way station-based carsharing systems with trip selection and vehicle relocation

ABSTRACT Carsharing emerges as a flexible and sustainable transportation mode to deal with severe urban challenges. This paper addresses a joint design problem of carsharing systems by an integer linear programming (ILP) model. Strategic planning of stations’ number, location and parking capacities and tactical planning of the fleet size and initial vehicle distribution are collaboratively optimized by considering all-day vehicle relocation and dynamic trip selection at the operational level. For the first time, vehicle relocation and demand selection are integrated with the joint optimization of strategic and tactical planning. We present a greedy heuristic algorithm embedding ILP to solve the problem. The case study of Chengdu demonstrates that the algorithm is computationally efficient when dealing with large-scale problems. The results identify how vehicle relocation and trip selection work in different scenes of travel time and demands and we conclude operators should target at medium-long trips by discount or promotion.

A Simulation-optimization framework of vehicle relocation for one-way electric carsharing systems

In one-way electric carsharing systems (ECSs), customers can pick up an electric vehicle (EV) at one location and return it to any location, causing an imbalance of EVs and revenue loss for ECSs. In order to address this problem, a simulation-optimization framework is introduced to determine optimal vehicle relocation strategies. In this framework, the discrete event simulation model considers stochasticity and EV-dependent road congestion to simulate the operation of ECSs. And the optimization module is triggered by the simulation model to provide relocation strategies to the operator based on the genetic algorithm with the optimal computing budget allocation. The results of numerical experiments demonstrate that the simulation model can reflect the operation of ECSs in the real world, and the joint relocation strategies achieve improvements in both the profit and the vehicle utilization rate and reduce the number of relocations concerning the single relocation strategies used in the ECS.

Utilization rate of the fleet: a novel performance metric for a novel shared mobility

Car-sharing systems have irrupted in our cities following the shared mobility paradigm. They have evolved the personal mobility market from product-based into service-oriented, which ultimately provides a positive impact on the city’s sustainability. Car sharing systems are a complex interactive service, whose dynamics can dramatically affect its operational viability. In order to better asses this viability, we must rely on data to produce novel metrics that characterize both the user behavior and the service performance. Up to date, research has focused on modeling the demand on the basis of the number of rentals that start within a specific time slot. However, this approach seems unable to provide a representative metric of the performance of a car-sharing system. In this paper, we propose a novel metric, the utilization rate of the fleet, which considers the precise number of vehicles within a fleet that are in service every minute of the day. From this basic metric, we derive a key performance indicator (KPI) to reflect the viability of any car-sharing system in economic and sustainability terms. We have applied this new metric and KPI to a dataset with 449 days of car2go data, collected in 10 European cities.

Aligning stakeholders’ mental models on carsharing system using remote focus group method

The novelties of new mobility solutions, such as carsharing, may instill different expectations and understanding of the concepts among stakeholders. These differences in their ‘mental models’ can hamper the wider implementation of the concept and delay a transition toward a more sustainable transport system. In this study, we implemented a participatory group modeling building approach (GMB) to explore the differences and to integrate the mental models of stakeholders concerning the carsharing operation in Bangkok, Thailand. Through the process, we identified apparent differences in how participants visioned a successful carsharing operation and created an initial shared understanding in the form of a causal loop diagram. The qualitative model included attributes influencing the success of carsharing and possible policy interventions. The results illustrated the effectiveness of GMB as a participatory approach for transport planning.

Research on Dynamic Scheduling of Unbalanced Supply and Demand of the SROC Based on the Customers Priority Principle

The problem of imbalance between supply and demand in car-sharing scheduling has greatly restricted the development of car-sharing. This paper first analyzes the three supply and demand modes of car-sharing scheduling systems. Secondly, for the station-based with reservation one-way car-sharing problem (SROC), this article establishes a dynamic scheduling model under the principle of customer priority. The model introduces balance coefficients to predict the balance mode, and systematically rebalance the fleet networks in each period. In the case of meeting customer needs, the model objective function is to maximize the total profit and minimize the scheduling and loss costs. Then, in view of the diversity and uncertainty of scheduling schemes, a scheme information matrix is constructed. In the iterative process of genetic algorithm, individuals are selected and constructed according to the pheromone matrix, and evolution probability is proposed to control the balance between global search and local search of genetic algorithm. Finally, the data of Haikou City is used for simulation experiment.

Optimization of Carsharing Fleet Placement in Round-Trip Carsharing Service

In a round-trip carsharing system, stations must be located in such a way that allow for maximum user coverage with the least walking distance as well as offer certain degrees of flexibility for returning. Therefore, a balance must be stricken between these factors. Providing a satisfactory system can be translated into an optimization problem and belongs to an NP-hard class. In this article, a novel optimization model for the round-trip carsharing fleet placement problem, called Fleet Placement Problem (FPP), is proposed. The optimization in this work is multiobjective and its NP-hard nature is proven. Three different optimization algorithms: PolySCIP (exact method), heuristics, and NSGA-II (metaheuristic) are investigated. This work adopts three real instances for the study, instead of their abstracts where they are most commonly used. They are two instance:, in the city of Luxembourg (smaller and larger) and a much larger instance in the city of Munich. Results from each algorithm are validated and compared with solution from human experts. Superiority of the proposed FPP model over the traditional methods is also demonstrated.

A New Hybrid Online and Offline Multi-Factor Cross-Domain Authentication Method for IoT Applications in the Automotive Industry

Connected vehicles have emerged as the latest revolution in the automotive industry, utilizing the advent of the Internet of Things (IoT). However, most IoT-connected cars mechanisms currently depend on available network services and need continuous network connections to allow users to connect to their vehicles. Nevertheless, the connectivity availability shortcoming in remote or rural areas with no network coverage makes vehicle sharing or any IoT-connected device problematic and undesirable. Furthermore, IoT-connected cars are vulnerable to various passive and active attacks (e.g., replay attacks, MiTM attacks, impersonation attacks, and offline guessing attacks). Adversaries could all use these attacks to disrupt networks posing a threat to the entire automotive industry. Therefore, to overcome this issue, we propose a hybrid online and offline multi-factor authentication cross-domain authentication method for a connected car-sharing environment based on the user’s smartphone. The proposed scheme lets users book a vehicle using the online booking phase based on the secured and trusted Kerberos workflow. Furthermore, an offline authentication phase uses the OTP algorithm to authenticate registered users even if the connectivity services are unavailable. The proposed scheme uses the AES-ECC algorithm to provide secure communication and efficient key management. The formal SOV logic verification was used to demonstrate the security of the proposed scheme. Furthermore, the AVISPA tool has been used to check that the proposed scheme is secured against passive and active attacks. Compared to the previous works, the scheme requires less computation due to the lightweight cryptographic algorithms utilized. Finally, the results showed that the proposed system provides seamless, secure, and efficient authentication operation for the automotive industry, specifically car-sharing systems, making the proposed system suitable for applications in limited and intermittent network connections.

The Competitive Pickup and Delivery Orienteering Problem for Balancing Car-Sharing Systems

Competition between one-way car-sharing operators is currently increasing. Fleet relocation as a means to compensate demand imbalances constitutes a major cost factor in a business with low profit margins. Existing decision support models have so far ignored the aspect of a competitor when the fleet is rebalanced for better availability. We present mixed-integer linear programming formulations for a pickup and delivery orienteering problem under different business models with multiple (competing) operators. Structural solution properties, including existence of equilibria and bounds on losses as a result of competition, of the competitive pickup and delivery problem under the restrictions of unit-demand stations, homogeneous payoffs, and indifferent customers based on results for congestion games are derived. Two algorithms to find a Nash equilibrium for real-life instances are proposed. One can find equilibria in the most general case; the other can only be applied if the game can be represented as a congestion game, that is, under the restrictions of homogeneous payoffs, unit-demand stations, and indifferent customers. In a numerical study, we compare different business models for car-sharing operations, including a merger between operators and outsourcing relocation operations to a common service provider (coopetition). Gross profit improvements achieved by explicitly incorporating competitor decisions are substantial, and the presence of competition decreases gross profits for all operators (compared with a merger). Using a Munich, Germany, case study, we quantify the gross profit gains resulting from considering competition as approximately 35% (over assuming absence of competition) and 12% (over assuming that the competitor is omnipresence) and the losses because of the presence of competition to be approximately 10%.