Service Region Design for Urban Electric Vehicle Sharing Systems

Emerging collaborative consumption business models have shown promise in terms of both generating business opportunities and enhancing the efficient use of resources. In the transportation domain, car sharing models are being adopted on a mass scale in major metropolitan areas worldwide. This mode of servicized mobility bridges the resource efficiency of public transit and the flexibility of personal transportation. Beyond the significant potential to reduce car ownership, car sharing shows promise in supporting the adoption of fuel- efficient vehicles, such as electric vehicles (EVs), due to these vehicles special cost structure with high purchase but low operating costs. Recently, key players in the car sharing business, such as Autolib, Car2Go and DriveNow, have begun to employ EVs in an operations model that accommodates one-way trips. On the one hand (and particularly in free-floating car sharing), the one-way model results in significant improvements in coverage of travel needs and therefore in adoption potential compared with the conventional round-trip-only model (advocated by ZipCar, for example). On the other hand, this model poses tremendous planning and operational challenges. In this work, we study the planning problem faced by service providers in designing a geographical service region in which to operate the service. This decision entails trade-offs between maximizing customer catchment by covering travel needs and controlling fleet operations costs. We develop a mathematical programming model that incorporates details of both customer adoption behavior and fleet management (including EV repositioning and charging) under imbalanced travel patterns. To address inherent planning uncertainty with regard to adoption patterns, we employ a distributionally robust optimization framework that informs robust decisions to overcome possible ambiguity (or lacking) of data. Mathematically, the problem can be approximated by a mixed integer second-order cone program, which is computationally tractable with practical scale data. Applying this approach to the case of Car2Go’s service with real operations data, we address a number of planning questions and suggest that there is potential for the future development of this service

Convexification of Queueing Formulas by Mixed-Integer Second-Order Cone Programming: An Application to a Discrete Location Problem with Congestion

Mixed-Integer Second-Order Cone Programs (MISOCPs) form a nice class of mixed-inter convex programs, which can be solved very efficiently due to the recent advances in optimization solvers. Our paper bridges the gap between modeling a class of optimization problems and using MISOCP solvers. It is shown how various performance metrics of M/G/1 queues can be molded by different MISOCPs. To motivate our method practically, it is first applied to a challenging stochastic location problem with congestion, which is broadly used to design socially optimal service networks. Four different MISOCPs are developed and compared on sets of benchmark test problems. The new formulations efficiently solve large-size test problems, which cannot be solved by the best existing method. Then, the general applicability of our method is shown for similar optimization problems that use queue-theoretic performance measures to address customer satisfaction and service quality

Does Car Sharing Contribute to Urban Sustainability from User-Motivation Perspectives?

Funding Information: Funding: The paper was funded by Latvian Council of Science, the project ”The Impact of COVID-19 on Sustainable Consumption Behaviours and Circular Economy” (Nr. lzp-2020/2-0317). Publisher Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland.Mobility, its current state and development perspectives in the future creates challenges with respect to sustainability, the first of which is the uncontrolled increase in greenhouse gas emissions in the last few decades, while road transport is one of the “sinners” creating long-term negative impact. The second is the dominance of car travel and car usage in the passenger transportation segment before the latest COVID-19 pandemic accelerated environmental problems. Although recent trends show new, greener patterns in consumption, there is still a relatively low share of consumers acknowledging the importance of sustainable and green preferences. This research study aims to investigate car sharing from users’ perspectives and to determine the most significant factors influencing their choice of sharing services to ensure upscaling of car sharing and, thus, contribute to urban sustainability. This research study contributes to the overall scientific discussion on car sharing and its role within urban sustainability, particularly with the following: (1) deeper investigation of car sharing and its users motivation perspectives in Latvia; (2) analyses of the most significant motivational factors for car-sharing users and aspects of sustainability; and (3) the insight into the generational differences triggering a number of car-sharing users. The existing and potential users of car sharing were surveyed in order to determine the motivational factors for its usage and attitudes towards it. Socio-demographic variables in statistical analysis were used to identify economic and environmental factors that meaningfully influence the choice of car-sharing services. The results of this study can support further development in new car-sharing business models and the value proposition for consumers in Latvia, as well as preparing policy recommendations on the promotion of sustainable transport. These findings are also useful to academics for the investigation of recent trends in car sharing during the COVID-19 pandemic.publishersversionPeer reviewe

Combining Analytics and Simulation Methods to Assess the Impact of Shared, Autonomous Electric Vehicles on Sustainable Urban Mobility

Urban mobility is currently undergoing three fundamental transformations with the sharing economy, electrification, and autonomous vehicles changing how people and goods move across cities. In this paper, we demonstrate the valuable contribution of decision support systems that combine data-driven analytics and simulation techniques in understanding complex systems such as urban transportation. Using the city of Berlin as a case study, we show that shared, autonomous electric vehicles can substantially reduce resource investments while keeping service levels stable. Our findings inform stakeholders on the trade-off between economic and sustainability-related considerations when fostering the transition to sustainable urban mobilit

Joint Pricing, Operational Planning and Routing Design of a Fixed-Route Ride-sharing Service

Fixed-route ride-sharing services are becoming increasing popular among major metropolitan areas, e.g., Chariot, OurBus, Boxcar. Effective routing design and pricing and operational planning of these services are undeniably crucial in their profitability and survival. However, the effectiveness of existing approaches have been hindered by the accuracy in demand estimation. In this paper, we develop a demand model using the multinomial logit model. We also construct a nonlinear optimization model based on this demand model to jointly optimize price and operational decisions. Moreover, we develop a mixed integer linear optimization model to the routing design decision. And a genetic algorithm based approach is proposed to solve the optimization model. Two case studies based on a real world fixed-route ride-sharing service are presented to demonstrate how the proposed models are used to improve the profitability of the service respectively. We also show how this model can apply in settings where only limited public data are available to obtain effective estimation of demand and profit.Master of Science in EngineeringIndustrial and Systems Engineering, College of Engineering & Computer ScienceUniversity of Michigan-Dearbornhttps://deepblue.lib.umich.edu/bitstream/2027.42/146788/1/49698122_Wanqing's Graduate Thesis (final).pdfDescription of 49698122_Wanqing's Graduate Thesis (final).pdf : Thesi

Simulation study on the fleet performance of shared autonomous bicycles

Rethinking cities is now more imperative than ever, as society faces global challenges such as population growth and climate change. The design of cities can not be abstracted from the design of its mobility system, and, therefore, efficient solutions must be found to transport people and goods throughout the city in an ecological way. An autonomous bicycle-sharing system would combine the most relevant benefits of vehicle sharing, electrification, autonomy, and micro-mobility, increasing the efficiency and convenience of bicycle-sharing systems and incentivizing more people to bike and enjoy their cities in an environmentally friendly way. Due to the uniqueness and radical novelty of introducing autonomous driving technology into bicycle-sharing systems and the inherent complexity of these systems, there is a need to quantify the potential impact of autonomy on fleet performance and user experience. This paper presents an ad-hoc agent-based simulator that provides an in-depth understanding of the fleet behavior of autonomous bicycle-sharing systems in realistic scenarios, including a rebalancing system based on demand prediction. In addition, this work describes the impact of different parameters on system efficiency and service quality and quantifies the extent to which an autonomous system would outperform current bicycle-sharing schemes. The obtained results show that with a fleet size three and a half times smaller than a station-based system and eight times smaller than a dockless system, an autonomous system can provide overall improved performance and user experience even with no rebalancing. These findings indicate that the remarkable efficiency of an autonomous bicycle-sharing system could compensate for the additional cost of autonomous bicycles

Location of charging stations in electric car sharing systems

Electric vehicles are prime candidates for use within urban car sharing systems, both from economic and environmental perspectives. However, their relatively short range necessitates frequent and rather time-consuming recharging throughout the day. Thus, charging stations must be built throughout the system's operational area where cars can be charged between uses. In this work, we introduce and study an optimization problem that models the task of finding optimal locations and sizes for charging stations, using the number of expected trips that can be accepted (or their resulting revenue) as a gauge of quality. Integer linear programming formulations and construction heuristics are introduced, and the resulting algorithms are tested on grid-graph-based instances, as well as on real-world instances from Vienna. The results of our computational study show that the best-performing exact algorithm solves most of the benchmark instances to optimality and usually provides small optimality gaps for the remaining ones, whereas our heuristics provide high-quality solutions very quickly. Our algorithms also provide better solutions than a sequential approach that considers strategic and operational decisions separately. A cross-validation study analyzes the algorithms' performance in cases where demand is uncertain and shows the advantage of combining individual solutions into a single consensus solution, and a simulation study investigates their behavior in car sharing systems that provide their customers with more flexibility regarding vehicle selection