A Decision Support System for the Optimization of Electric Car Sharing Stations

Electric car sharing is a mobility alternative addressing the world’s growing need for sustainability and allowing to reduce pollution, traffic congestion, and shortage of parking in cities. The positioning and sizing of car sharing stations are critical success factors for reaching many potential users. This represents a multi-dimensional challenge that requires decision makers to address the conflicting goals of fulfilling demands and maximizing profit. To provide decision support in anticipating optimal locations and to further achieve profitability, an optimization model in accordance to design science research principles is developed. The integration of the model into a decision support system (DSS) enables easy operability by providing a graphical user interface that helps the user import, edit, export, and visualize data. Solutions are illustrated, discussed, and evaluated using San Francisco as an application example. Results demonstrate the applicability of the DSS and indicate that profitable operation of electric car sharing is possible

Optimizing Strategic Allocation of Vehicles for One-Way Car-sharing Systems Under Demand Uncertainty

Car-sharing offers an environmentally sustainable, socially responsible and economically feasible mobility form in which a fleet of shared-use vehicles in a number of locations can be accessed and used by many people on as-needed basis at an hourly or mileage rate. To ensure its sustainability, car-sharing operators must be able to effectively manage dynamic and uncertain demands, and make the best decisions on strategic vehicle allocation and operational vehicle reallocation both in time and space to improve their profits while keeping costs under control. This paper develops a stochastic optimization method to optimize strategic allocation of vehicles for one-way car-sharing systems under demand uncertainty. A multi-stage stochastic linear programming model is developed and solved for use in the context of car-sharing. A seven-stage experimental network study is conducted. Numerical results and computational insights are discussed

CASSI: Designing a Simulation Environment for Vehicle Relocation in Carsharing

Simulations offer an efficient solution to comprehensive represent operational services and to track the impact of changing systematic factors and business constraints. Carsharing services provide users with mobility services on demand. Although research has introduced strategies to optimize efforts to set up and operate such a system, they lack reusable and flexible simulation environments. For instance, carsharing research applies simulations to better understand and solve the problem of balancing vehicle supply and demand, which operators need to solve to prevent operational inefficiencies and ensure customer satisfaction. Hence, one cannot feasibly test new balancing mechanisms directly in a real-world environment. As for now, researchers have implemented simulations from scratch, which results in high development efforts and a limited ability to compare results. In this paper, we address this gap by designing a versatile carsharing simulation tool that researchers can easily use and adapt. The tool simplifies the process of modeling a carsharing system and developing operation strategies. Furthermore, we propose various system performance measures to increase the developed solutions’ comparability

Profit and utility optimization through joint dynamic pricing and vehicle relocation in carsharing operations

peer reviewedPricing is one of the main determinants of a successful carsharing business plan. Companies develop different pricing strategies to increase attractiveness, profit, and service usage. Using dynamic pricing strategies can lead to service improvement in terms of profit and better customer satisfaction. This paper presents a novel research contribution to the field of transportation policy by introducing a new framework for designing dynamic pricing strategies in carsharing operations. We develop two hybrid-pricing strategies to increase profit and user utility in car sharing and analyze the service key performance indicators. These two different hybrid-pricing strategies are based upon two different approaches: one relying on demand related information (i.e., fixed price and time-based dynamic price) and one relying on supply related characteristics (i.e., maximum profit price and availability-based dynamic price). By considering both user utility and company indicators, this model features a bi-level structure that allows for rapid implementation. The framework relies on real-world data, typically available to carsharing companies, including membership data, geographic distribution of users, fleet composition, and the location of vehicles and stations. Additionally, we propose a relocation procedure that relocates vehicles on a day-to-day adjustment process. We study the impact of these strategies in an agent-based environment capable to accurately replicate a real carsharing service that operated in the city of Munich, Germany. Once these policies are in place, results show how it is possible to increase profit and customers’ utility. Moreover, we show how an increment in profit corresponds to a reduction of the utility and vice versa. Overall, the effectiveness of the proposed hybrid-pricing strategies in improving key performance indicators such as profit and score in carsharing services is demonstrated through the positive impact of demand-based pricing combined with relocation operations, while supply-based pricing strategies were found to be ineffective in enhancing profit and booking time.Supporting Tool For Empowering Advanced Mobility Services11. Sustainable cities and communitie

Optimization of profits in one-way free-floating car-sharing services, with a user-based relocation strategy that apply dynamic pricing and urban area demand defined gathering real vehicle-sensor data.

Rapid growing in urbanization and miles driven in the city will triple urban mobility by 2050. This explosion in demand requires switching to Mobility-as-a-Service (MaaS) models, such as Car-sharing. However, a critical issue for Car-sharing one-way free-floating services is the imbalance problem that requires to solve the conflict between the positioning of vehicles “at the right place and time” and the freedom for customers to return vehicles where and when they want. To better understand the impact of the imbalance problem, we propose to use a grid partition of the served city into zones with different demand potentials. To this aim as first step of the research real data related to vehicle positions of three Car-sharing services have been collected for approximately three months in the cities of Rome, Milan, Turin and Florence (Italy). In the experimental results data of the city of Rome have been used. This part of the research focuses on analysing user behaviour by using the number of stops in selected city zones (Stop Density) and the duration of any stop (Average Stop Duration); in fact, all the stops of each vehicle belonging to any car-sharing operator, are uniquely associated and mapped to exactly one cell of the city grid representing the Urban Areas, also tracking stop start/end time and trip start/end time. This spatial association is used to calculate Stop Density and Average Stop Duration of each urban area and to map stops to specific time-slots. Consequently, in each urban area, the Urban Area Value is calculated as a function of Stop Density and Average Stop Duration belonging to the urban area; the results of this research confirm that Urban Area Value is high where high values of Stop Density and low value of Average Stop Duration occurs. Urban Areas are ranked using the Urban Area Value calculated by considering all Car-sharing services operating in the eco-system; a spatial analysis with a thermographic map of Urban Area Value allows to visualize the existence of city zones with crucial different demand potentials. The analysis derived from such Urban Area Value and from a time-slot dynamic of the Urban Areas Values themselves, that suggested to split the standard operating day in five hourly ranges, is then used to construct a flexible and dynamic pricing mathematical programming model that has been used to derive an optimal setting of tariffs and to perform a validation phase. In this model the trip fare is defined, based on a trip planning trigger, applying a bonus/malus mechanism to a basic tariff, which considers vehicle service cost, staff relocation saving and the difference of demand value between origin and destination Urban Areas. If the user desired destination is planned in an urban area which is adjoining urban areas with higher values, alternatives with lower fees are proposed. This approach is applicable, in the reality, to several Car-sharing operators and mobility-sharing aggregators such as Urbi. The model and the outcomes of Urban Area Values have been validated in a study based on real data collected in the city of Rome (Italy) during an observation period of 49 days from April 28th to June 16th, in 2016, and where 287.975 stops observation referring to 1.271 distinct vehicles have been collected. All the stops have been observed in the city of Rome whose grid representation has been partitioned in 636 cells. These results have been presented to the 2017 COMPSAC Conference, July 7th, 2017 in the Workshop “Smart Sharing Mobility in Smart Cities” 1. These data have been used to construct an integer linear programming model where only a grid of 25 cells has been considered over the same period of 49 days. The resulting model (which has 84.500 variables and 87.750 constraints) has been solved using AMPL/CPLEX and validated by simulating a trip demand over an observed period. The result of this pricing scheme seems to produce interesting results with a business applicability in urban car–sharing market. The thesis is organized as follows. Chapter 1 is focused on the analysis of main challenges of urban mobility, and the role that car-sharing systems can play. Chapters 2, 3, 4 are devoted to the introduction and a systematic review of the literature. In Chapter 5 the data collection and cleaning are described and the final Data set is presented. Chapter 6 includes the grid partition of a city and the procedure to evaluate the Urban Area Value. Chapter 7 presents a review of the up-to-date pricing models for Car sharing that are used for defining some parameters in the optimization model presented in Chapter 8. Finally, in Chapter 9 the results obtained on the available Data set for the city of Rome are presented

Increasing the Business Value Of Free-Floating Carsharing Fleets By Applying Machine-Learning Based Relocations

Free-floating carsharing (CS) services provide customers with a fleet of vehicles distributed within an operation area. These services gained popularity because of their positive impact on societal and personal mobility. Understanding determinants of customer demand is a key challenge for developing and applying vehicle relocation strategies to prevent the formation of undersupply areas. In this study, we merge possible features from publicly available data sources with historical demand from CS services situated in three different-sized cities. We train and test a Random Forest (RF) regressor estimating demand based on the enhanced dataset. Building on this demand prediction, we developed a relocation strategy that optimizes vehicle availability at anticipated demand points. Our strategy improved the reservation acceptance ratio in all three reference systems between 7.1 % and 15.6 %. Furthermore, the number of relocations compared to a deterministic relocation strategy could be reduced by 82.3 % and 20.6 % in two cities

Designing a Crowd-Based Relocation System—The Case of Car-Sharing

Car-sharing services promise environmentally sustainable and cost-efficient alternatives to private car ownership, contributing to more environmentally sustainable mobility. However, the challenge of balancing vehicle supply and demand needs to be addressed for further improvement of the service. Currently, employees must relocate vehicles from low-demand to high-demand areas, which generates extra personnel costs, driven kilometers, and emissions. This study takes a Design Science Research (DSR) approach to develop a new way of balancing the supply and demand of vehicles in car-sharing, namely crowd-based relocation. We base our approach on crowdsourcing, a concept by which customers are requested to perform vehicle relocations. This paper reports on our comprehensive DSR project on designing and instantiating a crowd-based relocation information system (CRIS). We assessed the resulting artifact in a car-sharing simulation and conducted a real world car-sharing service system field test. The evaluation reveals that CRIS has the potential for improving vehicle availability, increasing environmental sustainability, and reducing operational costs. Further, the prescriptive knowledge derived in our DSR project can be used as a starting point to improve individual parts of the CRIS and to extend its application beyond car-sharing into other sharing services, such as power bank- or e-scooter-sharing

Evaluating Mobility Service Providers’ Strategies in an Activity-Based Supernetwork

A Mathematical Problem with Equilibrium Constraints (MPEC) is formulated to capture the relationships between multiple Mobility Service Providers (MSPs) and the users of a multimodal transport network. The network supply structure is represented as a supernetwork where users’ daily activity chains are represented sequentially and their modal choices to reach different destinations are based on the mobility services active in each connection. At the upper level, a profit maximization formulation is introduced to describe MSPs’ behaviour. At the lower level, groups of users choose the routes with the lowest cost, according to Wardrop’s first equilibrium principle. Due to non-separable interactions between supernetwork links, the equilibrium conditions defining users travel behaviour are written as Variational Inequality (VI). Finally, a numerical example is presented in order to show the characteristics of the model when car-sharing, bus and private car are available in the network