An optimization framework for the development of efficient one-way car-sharing systems

Environmental, energy, and societal considerations have given rise to the concept of shared-vehicle mobility systems. This concept postulates that the use of a fleet of vehicles made available on demand to the general public on a rental basis, can increase the mobility of certain population categories. In addition to mobility enhancement, shared-vehicle mobility systems have the potential to contribute to the sustainability of the transportation system through the decrease of environmental impacts, energy and space requirements (Duncan, 2011). As a consequence of the promises that shared-vehicle mobility systems hold, numerous such systems have been introduced in many cities around the world (Barth et al., 2006). However, most of the real-world applications of the car-sharing systems works in two ways, i.e. the vehicle should be returned to where it is rented from. Although there are some examples of one-way car-sharing systems in practice, they are not preferred by the operators because of their operational difficulties (e.g. relocations of vehicles). In this research we aim to propose a generic model for supporting the strategic (station location and size) and tactical (fleet size) decisions of a general one-way car-sharing system, with a direct application in a case study in Nice, France. For this purpose, a mathematical model is formulated and sensitivity analysis is conducted for different parameters. As a future work, we plan to work on the operational problem which considers requests on-line and updates vehicle rosters accordingly

Economic Freedom and One-Way Truck Rental Prices: An Empirical Note

This study examines the one-way truck rental prices for 378 cities. There are large price differentials in one-way rental prices between city pairs. The pull of people toward higher economic freedom locales and push away from lower economic freedom locales is found to be an important determinant of the city-pair price differentials

Costs and carbon emissions of shared autonomous electric vehicles in a Virtual Power Plant and Microgrid with renewable energy

5th International Conference on Power and Energy Systems Engineering (CPESE 2018), 19-21 September 2018, Nagoya, Japan.Shared autonomous electric vehicles (SAEVs) are expected to become commercially available within the next decade. This technology could transform transport paradigms and alter the availability of controllable storage from electrified transportation. This work describes a novel simulation methodology for investigating the potential for SAEVs to act as storage in the framework of a Virtual Power Plant or a microgrid with intermittent renewable energy. The model simulates aggregate storage availability from vehicles based on transport patterns and optimizes charging. We study the case of a grid-connected VPP with rooftop solar and the case of a isolated microgrid with solar, wind, and dispatchable generation. The results show that SAEVs offer significantly lower costs compared to private vehicles. SAEVs can also substantially increase renewable energy utilization in a microgrid

Modeling and solving a vehicle-sharing problem

Motivated by the change in mobility patterns, we present a new modeling approach for the vehicle-sharing problem. We aim at assigning vehicles to user-trips so as to maximize savings compared to other modes of transport. We base our formulations on the minimum-cost and the multi-commodity flow problem. These formulations make the problem applicable in daily operations. In the analysis we discuss an optimal composition of a shared fleet, restricted sets of modes of transport, and variations of the objective function

Generating Rental Data for Car Sharing Relocation Simulations on the Example of Station-Based One-Way Car Sharing

Developing sophisticated car sharing simulations is a major task to improve car sharing as a sustainable means of transportation, because new \ algorithms for enhancing car sharing efficiency are formulated using them. \ \ Simulations rely on input data, which is often gathered in car sharing systems or artificially generated. Real-world data is often incomplete and biased while artificial data is mostly generated based on initial assumptions. Therefore, developing new ways for generating testing data is an important task for future research. \ \ In this paper, we propose a new approach for generating car sharing data for relocation simulations by utilizing machine learning. Based on real-world data, we could show that a combined methods approach consisting of a Gaussian Mixture Model and two classification trees can generate appropriate artificial testing data

Categorizing Quality Determinants in Mining User-Generated Contents

User-Generated Contents (UGCs) are gaining increasing popularity as a source of valuable information for companies to manage the quality of their products, services and Product-Service Systems (PSS). This paper aims at proposing a novel approach to identify and categorize quality determinants through the analysis of an extensive database of UGCs. In detail, this paper applies a topic modeling algorithm (Structural Topic Model) to identify quality determinants and introduces the Mean Rating Proportion measurement for their classification into three categories: negative, positive and neutral quality determinants. The application of the proposed methodology is exemplified through the analysis of a PSS case study (car-sharing)

On-line proactive relocation and regulation strategies for one-way station-based car-sharing systems

This study examines the on-line proactive planning of relocations in a one-way station-based electric car-sharing system that implements a complete parking reservation policy. A Markovian model that utilizes reservation information is formulated in order to estimate the expected near-future shortages of vehicles and parking spots at each station. The outcome of the model is used in algorithms for staff-based and user-based relocations. The proposed algorithms are tested in a simulation environment using data derived from a real-world car-sharing system. In addition, in collaboration with a car-sharing operator, the algorithms are test in the field