Carsharing Systems Research Articles

Optimizing fleet, staff configuration and operational strategies in one-way mixed fleet carsharing systems: a Lagrangian relaxation-based approach

ABSTRACT This study explores enhancing carsharing services by integrating gasoline and electric vehicles into a one-way mixed fleet carsharing system (OMFCS). The focus is on optimizing configurations (fleet and staff size, initial deployment) and operational strategies (vehicle relocation and staff rebalancing) while considering carbon emission costs. Employing a space-time-electricity network modeling approach, we developed an integer linear programming model to tackle the configurations and operational strategies optimization problem. For solving this model, we introduce a Lagrangian relaxation-branch bound approach, which integrates subgradient, dynamic programming and greedy-based heuristics algorithm. An illustrative case and a real-world case are conducted to demonstrate the efficiency of the proposed solution method and the analysis sheds light on the configurations and operational strategies of OMFCS. The sensitive analysis results suggest that OMFCS is more profitable and balances user service quality and carbon emissions better than carsharing systems using only one type of vehicle.

Routing and charging scheduling for the electric carsharing system with mobile charging vehicles

Electric carsharing systems are expected to be an optional alternative to private vehicles for decreasing the urban traffic congestion and emissions. However, the temporal and spatial imbalance of the charging demand of shared electric vehicles adds to the managerial complexity of electric carsharing systems. This paper integrates mobile charging vehicles into the electric carsharing system to address this imbalance. Mobile charging vehicles can dwell at stations to provide elastic charging capacity, and thereby decrease both the waiting time of shared electric vehicles at busy stations and the investments in fixed charging piles at suburban stations. In this paper, a mixed integer linear programming formulation is proposed based on a time-space network, in which the routes of shared electric vehicles, charging schedules of shared electric vehicles, and routes of mobile charging vehicles are optimized simultaneously. Then, an algorithm based on Lagrangian relaxation is proposed. Specifically, the proposed formulation is decomposed into three independent subproblems. We propose three exact algorithms for these subproblems, and a tailored multistep repair algorithm is designed to generate feasible solutions. A case study in Hefei, China demonstrates the performance of the proposed algorithm and the effects of the number of SEVs, the number of MCVs, the number of fixed charging piles, trip component, battery capacity, and revenue on the operation of the electric carsharing system.

Efficiency-oriented vehicle relocation of shared autonomous electric fleet in station-based car-sharing system

Long waiting delays for users and significant imbalances in vehicle distribution are bothering traditional station-based one-way electric car-sharing system operators. To address the problems above, a “demand forecast-station status judgement-vehicle relocation” multistage dynamic relocation algorithm based on the automatic formation cruising technology was proposed in this study. In stage one, a novel trip demand forecast model based on the long short-term memory network was established to predict users' car-pickup and car-return order volumes at each station. In stage two, a dynamic threshold interval was determined by combining the forecast results with the actual vehicle distribution among stations to evaluate the status of each station. Then vehicle-surplus, vehicle-insufficient, vehicle-normal stations, and the number of surplus or insufficient vehicles for each station were counted. In stage three, setting driving mileage and carbon emission as the optimization objectives, an integer linear programming mathematical model was constructed and the optimal vehicle relocation scheme was obtained by the commercial solver Gurobi. Setting 43 stations and 187 vehicles in Jiading District, Shanghai, China, as a case study, results showed that rapid vehicle rebalancing among stations with minimum carbon emissions could be realized within 15 min and the users’ car-pickup and car-return demands could be fully satisfied without any refusal.

Dynamic Calibration of a Carsharing System in Multiagent Transport Simulation

Dynamic Calibration of a Carsharing System in Multiagent Transport Simulation

Factors influencing the car-sharing system through structural equation model in Guwahati city, Assam

ABSTRACT In parking management, car sharing has emerged as a viable approach in urban settings. Successful implementation requires a comprehensive understanding of factors influencing individuals’ inclination to use such systems. This research employs the Combined Technology Acceptance Model and Theory of Planned Behaviour (C-TAM-TPB) in an academic context to assess the engagement in car sharing from the perspectives of both providers and users. Assumption testing, using the partial least-square (PLS) style of structural equation modelling (SEM), was applied in a practical evaluation of the car-sharing system in Guwahati city. Findings reveal that perceived usefulness and subjective norms concerning contribution to the car-sharing system are impactful for both users and providers. Additionally, it is observed that prospective users’ attitudes towards car sharing significantly influence their intention to participate as users or providers. These results contribute to identifying intrinsic factors influencing individuals’ willingness to participate in car-sharing systems.

Personalization of the Car-Sharing Fleet Selected for Commuting to Work or for Educational Purposes—An Opportunity to Increase the Attractiveness of Systems in Smart Cities

Car-sharing services, which provide short-term vehicle rentals in urban centers, are rapidly expanding globally but also face numerous challenges. A significant challenge is the effective management of fleet selection to meet user expectations. Addressing this challenge, as well as methodological and literature gaps, the objective of this article is to present an original methodology that supports the evaluation of the suitability of vehicle fleets used in car-sharing systems and to identify the vehicle features preferred by users necessary for specific types of travel. The proposed methodology, which incorporates elements of transportation system modeling and concurrent analysis, was tested using a real-world case study involving a car-sharing service operator. The research focused on the commuting needs of car-sharing users for work or educational purposes. The study was conducted for a German car-sharing operator in Berlin. The research was carried out from 1 January to 30 June 2022. The findings indicate that the best vehicles for the respondents are large cars representing classes D or E, equipped with a combustion engine with a power of 63 to 149 kW, at least parking sensors, navigation, hands-free, lane assistant, heated seats, and high safety standards as indicated by Euro NCAP ratings, offered at the lowest possible rental price. The results align with market trends in Germany, which focus on the sale of at least medium-sized vehicles. This suggests a limitation of small cars in car-sharing systems, which were ideologically supposed to be a key fleet in those kinds of services. The developed methodology supports both system operators in verifying whether their fleet meets user needs and urban policymakers in effectively managing policies towards car-sharing services, including fleet composition, pricing regulations, and vehicle equipment standards. This work represents a significant step towards enhancing the efficiency of car-sharing services in the context of smart cities, where personalization and optimizing transport are crucial for sustainable development.

Optimizing Station Placement for Free-Floating Electric Vehicle Sharing Systems: Leveraging Predicted User Spatial Distribution from Points of Interest

Rapid growth rate indicates that the free-floating electric vehicle sharing (FFEVS) system leads to a new carsharing idea. Like other carsharing systems, the FFEVS system faces significant regional demand fluctuations. In such a situation, the rental stations and charging stations should be constructed in high-demand areas to reduce the scheduling costs. However, the planning of the FFEVS system includes a series of aspects of rental stations and charging stations, such as the location, size, and number, which interact with each other. In this paper, we first provide a method for forecasting the demand for car sharing based on the land characteristics of Beijing FFEVS station catchment areas. Then, the multi-objective MILP model for planning FFEVS systems is developed, which considers the requirements of vehicle relocation and electric vehicle charging. Afterward, the capabilities of the proposed models are demonstrated by the real data obtained from Beijing, China. Finally, the sensitivity analysis of the model is made based on varying demand and subsidy levels. From the results, the proposed model can provide decision-makers with useful insights about the planning of FFEVS systems, which bring great benefits to formulating more rational policies.

Enhancing public transport accessibility: Exploring hybrid car sharing systems for improved connectivity

Transportation networks within a region are vital. They link individuals from their starting points to their destinations. Nonetheless, even highly effective transportation systems may not ensure optimal performance, service quality, or fairness for users unless they are extensively interconnected and accessible to as many people as possible. In this context, we present a planning framework for complementing public transit (PT) systems with other cost-effective mobility systems, such as car-sharing (CS) systems, to improve performance in terms of connectivity and accessibility. These metrics are evaluated by using the suitably introduced Connectivity and Accessibility Index (CAI). Specifically, in the present research, we first introduce a novel methodology for assessing the connectivity and accessibility values of existing PT systems. Then, the proposed approach provides an optimization-based design model for a hybrid CS system comprising both one-way (OW) and two-way (TW) models. To evaluate the capabilities of the proposed approach, a real-world case study of the PT system of the city of Trento (Italy) is evaluated.

Carsharing Worldwide: Case Studies on Carsharing Development in China, Europe, Japan, and the United States

Carsharing has received considerable attention as a sustainable mobility paradigm. Various service designs and dynamic business environments have increased the decision complexity for the carsharing business. Therefore, carsharing operators require a tool for assessing business development from holistic perspectives. This research provides a framework for outlining the requirements of the carsharing system with holistic perspectives of stakeholders being considered, as well as to derive assessment metrics for examining carsharing development. To create the framework, the system modeling tool, context diagram, was adopted to map out the interactions of externalities with the system and the requirements of the system. Eight assessment metrics: the market condition, business advantage, parking condition, electric vehicle deployment, self-serving configuration, vehicle reservation, vehicle maintenance, and pricing scheme, were eventually identified from the system modeling. From these dimensions, we review 24 carsharing cases from China, Europe, Japan, and the United States, and we summarize discrepancies among different marketplaces and some managerial insights on carsharing development, such as carsharing motivators and inhibitors, innovations in respect of different business backgrounds, approaches of increasing parking privileges, approaches of increasing electrifications, essential digital features, reservation regimes, methods of vehicle maintenances, and service pricing regimes.

On the approximability and energy-flow modeling of the electric vehicle sharing problem

The electric vehicle sharing problem (EVSP) arises from the planning and operation of one-way electric car-sharing systems. It aims to maximize the total rental time of a fleet of electric vehicles while ensuring that all the demands of the customer are fulfilled. In this paper, we first show that the EVSP is NP-hard to approximate to within a factor of n1−ϵ in polynomial time, for any ϵ>0, where n denotes the number of customers. In addition, we also show that the problem does not have a monotone structure, which can be detrimental to the development of heuristics employing constructive strategies. Moreover, we propose a novel approach for modeling the EVSP based on energy flows. Based on this new model, we propose a relax-and-fix strategy and an exact algorithm. Our computational results show that our formulation outperforms the previous best-performing formulation in the literature in the number of optimal solutions obtained, optimality gaps, and computational times. Previously, 32.7% of the instances remained unsolved (within a time limit of one hour), while our formulation obtained optimal solutions for all instances. To stress our approaches, two more challenging new sets of instances are generated, for which we can solve 49.5% of the instances, with an average optimality gap of 2.91% for those not solved optimally.

Carsharing in China: Impact of system and urban factors on usage and efficiency

The carsharing service has experienced significant growth over the past few years in China, yet few studies have scrutinized the multi-city variations of this service. Using carsharing data from 61 cities in China, we analyzed the usage and efficiency of each city and investigated the impact of system and urban factors on the service performance. The study reveals vast differences in carsharing supply and demand across Chinese cities. Our results show the parking station density and the parking lot to vehicle ratio of the carsharing system are positively related to the usage. Urban factors such as public transportation availability, educational attainment levels, and vehicle restriction policies, are found to have significantly positive associations with the carsharing usage. However, no urban factors demonstrate significant associations with the efficiency measured by vehicle utilization rate. Moreover, the presence of other competing carsharing services within a city exhibits a positive impact on the performance of carsharing systems. This study also examined nonlinear effects of the factors. It provides valuable insights into the management of carsharing services in China, which can inform policy-making and operational strategies for sustainable development of carsharing.

Revealing spatiotemporal characteristics of EV car-sharing systems: A case study in Shanghai, China

Electric vehicle (EV) car sharing, as an emerging mode of transportation, has been paid increasingly amount of attention by scholars in recent years. However, at the operational level, we still know very little about its spatiotemporal characteristics, especially its temporal associations with land use patterns. In this study, we comprehensively investigated these two aspects by utilizing longitudinal data of the one-way EV car-sharing system in Shanghai, China, with a total count exceeding 50,000 vehicles in 2018. The results showed an unmatched coupling relationship in terms of the spatial dimension between EV car-sharing flow communities and intra-city administrative divisions. For the temporal dimension, we found that, unlike the conventional division of peak and non-peak hours, there are nine different types of car use behaviors in terms of temporal variations in EV car-sharing travel in Shanghai. We further revealed that such temporal variations, in terms of temporal attractiveness, can be significantly affected by land-use characteristics, including healthcare services, lifestyle services, companies and enterprises, and scientific, educational, and cultural services, indicating a strong and complex interaction between land use and the pattern in car use behaviors of this emerging mode of transportation. This data-driven study offers new insights into EV car-sharing operations and provides better understanding of EV car-use behaviors, which may promote evidence-informed decisions and policymaking for megacity authorities.

Privacy-Preserving Decentralized Biometric Identity Verification in Car-Sharing System

Privacy-Preserving Decentralized Biometric Identity Verification in Car-Sharing System

Optimization of vehicle charging and dynamic relocation in free-floating electric carsharing systems with advanced reservations

The electric free-floating carsharing (FFCS) system is an emerging type of shared mobility and is widely adopted in urban areas due to its eco-friendliness, effectiveness, and flexibility. However, as most trips in FFCS are uni-directional, the distributions of vehicles in FFCS are usually spatially uneven. The operators of FFCS must relocate cars during the operation period to maintain the availability of cars to the users and improve the profitability of the system for dealing with imbalanced vehicle distribution caused by uneven demand. To optimize the relocation operations for FFCS operators, this study formulates the problem as a mixed-integer linear programming (MILP) model, which simultaneously addresses dynamic relocation, battery charging of electric vehicles, and scheduling of relocation staff in an FFCS with advanced reservation. A novel, efficient algorithm using a new structure of relocation tuples (RTs) is proposed to solve the problem that facilitates the search for feasible solutions using an RT exchange-based local search algorithm. Acceleration techniques are introduced to strengthen the solution search in large-scale scenarios. Numerical examples are used to evaluate the performance of the solution algorithm and validate the benefit of the proposed methodology. This shows that the methodology could be used by operators for decision-making at different levels.

Preserving Privacy and Traceability in Car-sharing Blockchain Based on Attribute Cryptosystem

<p>A car-sharing system has been considered the most promising solution for mitigating the waste of natural resources, traffic congestion, and greenhouse gas emission in the city. However, conventional car-sharing method relies on a trusted third party, which is facing the challenge of maximizing the benefits of C2C model and privacy disclosure risk. This work aims to design a brand-new car-sharing version based on blockchain network and attribute-based cryptosystem. The demander and supplier can use the smart contract to share the vehicle without the help of a centralized node; thus, avoiding the collusion manipulation to reach the optimal profit. In particular, even if the vehicle has been damaged, the supplier can trace the responsibility by accessing the order information stored in the blockchain. Security analysis has demonstrated the confirmation of robustness and privacy essentials, while experimental outcomes have shown the satisfactory feasibility of the new method.</p> <p> </p>

Exploring the behavioral stage transition of traveler's adoption of carsharing: An integrated choice and latent variable model

This study investigates the process of stage transition in traveler's adoption of carsharing. The carsharing adoption behaviors are classified into five stages using the transtheoretical model: precontemplation (PC), contemplation (C), preparation (PA), action (A), and maintenance (M). In Beijing, a comprehensive survey combining stated preference and revealed preference methods is conducted to collect data on travelers' adoption attitudes and related attributes, such as socioeconomic characteristics, travel features, and carsharing usage environments. Specifically, ten latent attitudinal variables are derived from the technology acceptance model and theory of planned behavior. An integrated choice and latent variable model is developed to examine the impact of these explanatory factors on the transition processes between the five adoption stages. The findings reveal that the effects of explanatory factors on stage transitions in carsharing adoption behaviors differ across stages. Factors such as education level, income, having children, car ownership, familiarity with carsharing, personal condition, and social pressures play a crucial role in the early stages of understanding before carsharing adoption, namely the PC-C and C-PA stage transitions. Conversely, in the later stages (PA-A and A-M), travelers show a greater emphasis on carsharing infrastructure, including station reachability, vehicle availability, and perception toward rental stations and shared vehicles. Additionally, factors related to travel habits and expectations are influential throughout all stages of the carsharing adoption process. Lastly, three categories of measures, namely soft, neutral, and hard measures, are derived from the results to guide policymakers and carsharing operators in formulating strategies for the operation and management of carsharing systems.

A Unified Spatio-Temporal Inference Network for Car-Sharing Serial Prediction.

Car-sharing systems require accurate demand prediction to ensure efficient resource allocation and scheduling decisions. However, developing precise predictive models for vehicle demand remains a challenging problem due to the complex spatio-temporal relationships. This paper introduces USTIN, the Unified Spatio-Temporal Inference Prediction Network, a novel neural network architecture for demand prediction. The model consists of three key components: a temporal feature unit, a spatial feature unit, and a spatio-temporal feature unit. The temporal unit utilizes historical demand data and comprises four layers, each corresponding to a different time scale (hourly, daily, weekly, and monthly). Meanwhile, the spatial unit incorporates contextual points of interest data to capture geographic demand factors around parking stations. Additionally, the spatio-temporal unit incorporates weather data to model the meteorological impacts across locations and time. We conducted extensive experiments on real-world car-sharing data. The proposed USTIN model demonstrated its ability to effectively learn intricate temporal, spatial, and spatiotemporal relationships, and outperformed existing state-of-the-art approaches. Moreover, we employed negative binomial regression with uncertainty to identify the most influential factors affecting car usage.

Trip Pricing in User-Based Relocation for Station-Based Carsharing Systems

Trip Pricing in User-Based Relocation for Station-Based Carsharing Systems

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.

Joint relocation and pricing in electric car-sharing systems

In this paper we study the integrated planning problem of determining car-sharing prices between zones of the operating area and routing employees (operators) to relocate cars in preparation for future uncertain demand. We present a novel two-stage integer stochastic programming model for this problem together with a heuristic algorithm, based on Adaptive Large Neighborhood Search (ALNS), to obtain solutions to realistically sized instances. We test the ALNS heuristic on a set of instances generated based on data from a real car-sharing organization and show that it outperforms a commercial solver.