Abstract

Carsharing provides members access to a fleet of autos for short-term use throughout the day, reducing the need for one or more personal vehicles. This chapter reviews key terms and definitions for carsharing, common carsharing business models, and existing impact studies. Next, the chapter discusses the commodification and aggregation of mobility services and the role of Mobility on Demand (MOD) and Mobility as a Service (MaaS) on carsharing. Finally, the chapter concludes with a discussion of how the convergence of electrification and automation is changing carsharing, leading to shared automated and electric vehicle (SAEV) fleets.

Abstract

Replacing conventional vehicles by electric vehicles (EVs) and increasing the use of carsharing are two strategies to reduce the environmental impact of car driving. However such a societal transition towards the use of more sustainable modes of transport will strongly depend on citizens' willingness to support this process. For this reason, this paper tries to identify factors which are related to the individual likelihood to change to more sustainable modes of transport. It draws on an adapted version of Rogers' diffusion of innovation model (DOI) and earlier work by the authors. It presents new findings from an online survey (n=1548) in one of Germany's show case regions for electric vehicles. Findings point out that relatively small shares of respondents already use these sustainable modes of transport (.6% for EV ownership and 5.3% for carsharing). Similarly, the shares of individuals who are very likely to use them in the near future (4.2% and 4.6% respectively) are also small. Much more individuals are interested in EVs (55.9%) than in carsharing (21.2%), and large groups are not interested (37.7% for EVs, 68.9% for carsharing). There are several significant sociodemographic differences between the respective four adoption groups. Furthermore, consistently, evluations are significantly more positive the higher the likeliness of adoption across groups. Based on regression models, it turns out that perceived compatibility with daily life is the most important factor influencing the attitudes towards EVs or carsharing across all groups.

Abstract

This thesis aims at modelling and solving optimization problems related to the management of one-way-electric-car-sharing systems, where users can take a car from a station, use it, and then return it to another station. This generally leads to an imbalanced distribution of cars, with some full stations and other empty ones. A solution to this problem, implemented by car-sharing operators, is to employ staff agents to move cars as needed. However, identifying this need is a non-trivial optimization problem, especially since the system may be more constrained when the vehicles used are electric, which generates battery recharging and autonomy constraints. The global optimization problem addressed is then divided into two sub-problems. The first one is assigning the cars to customers, as well as their routing; it is denoted by ROCSP (Recharging OneWay Car Sharing Problem). The second problem involves agents planning and routing; it is denoted by ESRP (Employee Scheduling Routing Problem). 1. For the ROCSP, we propose two Mixed-integer linear programming (MILP) modelizations of the problem: One based on flows and the other based on paths. This means that the two models include the battery-recharging constraints in two different ways. As the exact resolution through the MILP models is quite expensive in terms of computational time and is not adapted for the resolution of real-size car-sharing instances, we introduce heuristics that enable the optimization of cars-redistribution and service management of the service within a reasonable amount of time. These heuristics allows the calculation of the number of cars and the various redistribution operations to be performed on a given day. 2. For the ESRP, this second problem is also addressed with MILP models for the exact resolution, and some heuristics are suggested for an approximate resolution. This process has reasonable calculation time and aims at finding the minimum number of agents to perform the necessary relocation operations that stem from the first problem, namely, the ROCSP. Once the ROCSP and ESRP solved in their static versions, we then focus on the ROCSP by exploring another variant of the problem : ROCSP with dynamic reservation. We also suggest to explore a new concept : Auto-CoPartage, which is a hybridization of car-sharing and carpooling. The stated algorithms are validated on the Auto Bleue electrical vehicles fleet in the network of the city of Nice, essentially by relying on flow generation models to estimate the demand, but also using other instances that we have generated for other cities. All the data are handled using a Geographical Information System.; Cette thèse a pour objectif de modéliser et résoudre des problèmes d’optimisation d’un système d’auto-partage avec des voitures électriques dit « à un seul sens », où les utilisateurs peuvent prendre une voiture dans une station et la laisser ensuite dans une autre. Ce fonctionnement conduit généralement à une situation de déséquilibre dans la répartition des voitures avec certaines stations pleines et d’autres vides. Une des solutions utilisées par les opérateurs d’autopartage pour pallier ce problème est le recours à des agents pour déplacer les voitures selon le besoin. Identifier et répondre à ce besoin est un problème d’optimisation non trivial, notamment à cause de l’usage de véhicules électriques, ce qui engendre des contraintes de rechargement de batteries et d’autonomie. Le problème d’optimisation est décomposé en deux sous-problèmes : le premier est le problème d’affectation des voitures aux clients, ainsi que leurs routages, que nous nommons ROCSP pour Recharging One way Car Sharing Problem ; le second problème est celui du planning des agents et leurs routages que nous nommons ESRP pour Employee Scheduling Routing Problem. 1. Résolution du ROCSP : deux modélisations en Programmation Linéaire en Nombres Entiers (PLNE) sont proposées, la première basée sur les flots et la deuxième sur les chemins, ce qui fait que les deux modèles intègrent de manière différente les contraintes de recharge électrique. Comme la résolution exacte à travers les modèles PLNE s’avère très gourmande en temps de calcul et non adaptée aux instances d’auto-partage de taille réelle, nous proposons des heuristiques qui permettent dans un temps raisonnable d’optimiser la redistribution des voitures et la gestion du service. Ces heuristiques permettent de calculer le nombre de voitures et les différentes opérations de relocalisation (redistribution des voitures) à réaliser sur une journée donnée. 2. Résolution du ESRP : un modèle PLNE est proposé pour la résolution exacte du ESRP, et, en complément, des heuristiques sont proposées pour une résolution approchée et relativement rapide. L’objectif est la détermination du nombre minimal d’agents nécessaire pour effectuer les opérations de relocalisation qui découlent du premier problème, le ROCSP. Dans une partie prospective, et une fois les ROCSP et ESRP résolus dans leur version statique, nous nous focaliserons sur une autre variante du problème avec réservation dynamique. Nous proposons également d’explorer un nouveau concept - l’auto-copartage - qui se veut une hybridation entre autopartage et covoiturage. Les algorithmes proposés ont été validés sur le réseau Auto Bleue de la ville de Nice essentiellement, qui gère une flotte de véhicules électriques, en s’appuyant sur des modèles de génération de flux pour estimer la demande, mais aussi d’autres instances que nous avons générées pour simuler d’autres villes, au sein d’un Système d’Information Géographique.

Abstract

Shared-use mobility systems, which enable users to have short-term access to transportation modes on an on-demand basis, have experienced tremendous growth over the last decade. However, most of the existing systems suffer from two confounding issues: the lack of modeling tools to understand, simulate and predict their behavior and the lack of integration with the existing transit network. To address those issues, this dissertation focuses on investigating the operational challenges of bikesharing systems, with an emphasis on the rebalancing operations and the modeling of a new mobility concept, Car2work, which builds upon existing carsharing ideas and successfully integrates with existing transit networks. A methodological framework to solve the bikesharing rebalancing problem is proposed. The novelties of the approach are that it is proactive instead of reactive, as the bike redistribution occurs before inefficiencies are observed, and uses the outputs of a demand-forecasting technique to decompose the inventory and the routing problem. The decomposition makes the problem scalable, responsive to operator inputs, and able to accommodate user-specific models. Simulation results based on data from the Hubway bikesharing system show that system performance improvements of 7% in the afternoon peak could be achieved.Car2work main goal is to connect commuters with workplaces while leveraging the line-haul capabilities of existing public transit systems and guaranteeing a trip back home, efficiently tackling the “last mile” problem that is a limiting characteristic of public transit. It differs from the traditional dynamic-ridesharing approaches because it is designed for recurrent commuting trips where commuters announce their (multiple) trips in advanced and an automated all-or-nothing matching strategy is performed, guaranteeing a ride home. The problem is formulated as a pure binary problem that is solved using an aggregation/disaggregation algorithm that renders optimal solutions. The solution approach is based on decomposing the problem into a master problem and a sub-problem, reducing the number of decision variables and constraints. As a result, various instances of the problem can be solved in reasonable amount of time, even when considering the transit network. The model can be used to simulate a widespread implementation of the concept.

Abstract

Carsharing is a service that allows the rental of a car to the minute, providing more flexibility to the user. This service appeared in 1948 in Switzerland but it has been growing due to the awareness of Shared Mobility. Carsharing concept has been approached by several researchers. However, there is not so much literature about Portuguese market. The impacts of carsharing are known to be the reduction of C02 emissions since it contributes to the reduction of the number of cars parked and in circulation in the big cities (1), the inclusion of all fixed costs in the final price, as the user only pays the miles he drives (2) and the flexibility carsharing gives the users to move around in the city (3). This study’s goal is to understand the Portuguese perception of carsharing use, taking into account benefits such as convenience, environmental impact and costs considerations. It is also crucial to understand the level of service knowledge among Portuguese people, since this factor can influence the perception and a possible intention to use it. It turns out that most of the Portuguese know what carsharing is but do not know very well how it works. In general, the Portuguese people, whether users or non-users, do not see carsharing as a cheap service, but consider the service convenient. As far as environmental impact is concerned, attitudes differ. Users have a positive attitude regarding carsharing impact on the environment, while non-users have a negative attitude. This perception impacts on the attitude of users and non-users. While the former has a positive attitude towards the use of carsharing, the same cannot be concluded from the latter. Most users intend to continue to use carsharing, unlike most non-users who do not want to try the service. O carsharing é um serviço que permite o aluguer de um carro ao minuto, proporcionando mais flexibilidade ao utilizador. Este serviço apareceu pela primeira vez em 1948 na Suíça e tem vindo a crescer devido à popularidade do fenómeno da Mobilidade Partilhada. O carsharing tem sido um conceito muito abordado por investigadores estrangeiros, nunca tendo sido muito investigado em Portugal. Os impactos do carsharing são conhecidos por ser a redução de emissões de C02 uma vez que contribui para a diminuição do número de carros estacionados e em circulação nas grandes cidades (1), a não inclusão de custos fixos que a utilização de carro próprio acarreta no preço final- o utilizador só paga os quilómetros que conduz (2) e a flexibilidade que dá aos utilizadores para se movimentarem na cidade (3). Este estudo tem como objetivo perceber qual é a perceção dos portuguese face à utilização do carsharing, tendo em conta benefícios como a conveniência, o impacto ambiental e fatores económicos. Torna-se crucial perceber também o nível de conhecimento do serviço por parte dos portugueses, uma vez que este fator pode influenciar a perceção e uma possível intenção de utilização do mesmo. Conclui-se que a maior parte dos portugueses sabe o que é carsharing mas não sabe muito bem como funciona. Em geral, os portugueses, quer utilizadores quer não utilizadores, não veem o carsharing como um serviço barato, mas consideram o serviço conveniente. No que diz respeito ao impacto ambiental, as atitudes divergem. Os utilizadores têm uma atitude positiva quanto ao impacto do carsharing no ambiente, enquanto os não utilizadores têm uma atitude negativa. Esta perceção impacta na atitude dos utilizadores e não utilizadores. Enquanto que os primeiros têm uma atitude positiva face à utilização de carsharing, já não se pode concluir o mesmo dos segundos. Na sua maioria os utilizadores pretendem continuar a utilizar carsharing, ao contrário da maioria dos não utilizadores que não pretende experimentar o serviço.

Abstract

International audience; —Technologic advancements have contributed to the spread of sharing economy concepts, a developing phenomenon that favors the shift from private mobility to service-use (shared mobility). One-way carsharing is a most recent and popular kind of shared mobility, that is growing and developing rapidly in various forms. These systems are considered to have a transformative impact on future urban transportation. Despite all of the benefits that have been reported from the use of new one-way carsharing (e.g. Autonomous Mobility-on-Demand systems), their impacts on the mobility are not certain yet. This comes from the fact that in such services supply and demand influence each other in a significant way in short-, mid-, and long-term. Also service characteristics at the level of each vehicle strongly affect the demand. In this paper methods, paradigms, toolkits and platforms used in the literature for the demand estimation of the new one-way carsharing systems, as well as their potential drawbacks are discussed. A review of the literature reveals that despite the considerable number of studies related to balancing vehicle stocks across stations in one-way systems, the investigation about demand estimation of such services for which the complex relationship between supply and demand is considered, remain very limited. The majority of current platforms and toolkits used for demand estimation of new one-way carsharing systems are based on activity-based multi-agent simulations. In these simulations several main components are not yet taken into account, which could dramatically change the results. Data detail, accessibility and reliability, high computational time, calibration and validation still remain major challenges for travel demand estimation for one-way carsharing systems.

Abstract

Most of the existing Carsharing business models mainly rely on gasoline vehicles and diesel vehicles, but in recent years there has been a significant increase in hybrid electric vehicles (HEVs) and a resurgence in electric vehicles (EVs). Within this framework, this paper investigates and models the choice to switch from a private car trip to a carsharing service available in peripheral parks as well as the propensity to choose an electric vehicle for such a service. In particular, three issues are addressed: (i) investigating and modelling the propensity to choose carsharing as a transport alternative within a neighbourhood residential carsharing business model; (ii) estimating the effect of also having an EV option available; (iii) measuring the “pure preference”, if any, in using electric vehicles over traditional ones, in a context excluding factors that may bias such users preference (e.g. purchase price, energy costs, recharging facilities etc). The analyses are based on a stated preferences survey undertaken on 600 car drivers entering the city centre of Salerno (Southern Italy), and on the estimation of a binomial Logit model with serial correlation. Results allow an interpretation of the main determinants of the short-term choice of carsharing services (i.e. without any car-ownership changes), give general behavioural insights, make it possible to quantify the “pure preference” for EV and the demand elasticity with regard to different pricing strategies of the carsharing services.

Abstract

Carsharing continues to grow worldwide as a powerful strategy to provide an alternative to solo driving. The viability of electric vehicles, or EVs, has been exam-ined in various carsharing business models. Moreover, new technologies have given rise to electromobility, or e-mobility, systems. This paper discusses the evolution of e-mobility in carsharing business models and the challenges and opportunities that EVs present to carsharing operators around the world. Operators are now anticipating in-creased EV proliferation into vehicle fleets over the next 5-10 years as technology, infrastructure, and public policy shift toward support of e-mobility systems. Thus, re-search is still needed to quantify impacts of EVs in changing travel behavior toward more sustainable transport.

Abstract

Electric vehicles require sufficient public charging infrastructure. This in turn necessitates detailed information on charging demand. In this paper we present a four-step approach to estimating public charging demand of electric vehicles. Previous methods are limited in their ability to provide differentiated results and adapt to future developments. Therefore, we account for user groups (private, carsharing, commercial), technical developments (vehicles, infrastructure), infrastructure availability, and carsharing development (operational area, business models, autonomous vehicles). Our approach also considers the interactions between these factors and allows for scenario analysis yielding the quantity and spatial distribution of public charging demand. We demonstrate our approach for Berlin, Germany. We find that the majority of public charging demand results from carsharing. This demand is concentrated in the city center, even when carsharing is available citywide. Public charging demand for commercial users is relatively low and located outside the city center. For private users, public charging demand shifts to the city center with an increasing market penetration of electric vehicles and technological advancements (increased range, charging speed). Public demand from private users increases dramatically when private infrastructure is absent. Finally, public charging demand shifts to the city center when private users do not have private infrastructure. Document type: Article

Abstract

Business environment at the beginning of the 21st century is characterized by disruptive and frequent changes. These changes lead to restructuring of some traditional industries, such as automotive industry. Great recession, technology innovations and changes in government regulations unfreezed the structure of automotive industry. Consequently, competitors in the industry have to fit their strategies to new reality. Great recession resulted in financial troubles or even bankruptcy of some significant incumbents and created market space for late followers from emerging markets. Digital innovations have created preconditions for emergence of autonomous cars and carsharing and ridesharing service industry. Global pollution puts pressure on national governments to establish higher gas emission standards and ban diesel vehicles in city centers, which encourages development of electric cars. It is hard to predict the course of development of carsharing service industry, autonomous and electric cars due to high risks and uncertainties. Therefore, auto companies focus their efforts on some smaller market segments, form strategic alliances and acquire targets with complementary resources. Document type: Article