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International audience ; Smart mobility systems represent a new generation of transport systems that are strongly supported by information and communications technologies, allowing a continuous connection between the system administrators, the customers/users, the transport infrastructures and the vehicles. A major example of these systems is represented by carsharing. Carsharing can relieve people from the costly and non-sustainable burden of owning a car, especially when residing in a city. Furthermore, it can reduce pollution and traffic congestion and has been worldwide recognized as a fundamental component of smart cities by policy makers In this study, we provide an overview of relevant regulations for carsharing, highlighting in particular the importance of parking policies. Given this importance, we propose a mathematical optimization model that can be used by a local government to analytically choose the best subset of parking slots to rent to carsharing companies, in order to improve urban mobility. We test the model on realistic data of the city of Rome, showing that we can obtain a fair territorial distribution of the parking slots that satisfies population needs. The data were defined on the basis of our collaboration with professionals of the electric utility company Enel within EGo Car Sharing, an electrical vehicle carsharing service established at the University Roma Tre.

International audience ; Carsharing represents a major example of smart mobility service that allows a customer to rent a vehicle for a limited amount of time paying a per-minute fee. It may relieve people of the costly and non-sustainable burden of owning a car, especially when residing in a city. Though the spread of carsharing may bring significative benefits to (smart) cities, its penetration can be obstructed by nonup-to-date regulations, which can be still tied to a non-smart vision of mobility. In this study, we provide an overview of remarkable city regulations for carsharing, particularly highlighting the importance that parking policies can have in favouring the diffusion and use of carsharing services. Given such importance, we characterize the optimization problem of a local government that wants to analytically choose the best subset of parking slots to rent to carsharing companies, in order to improve urban mobility. To model and solve the problem we propose a new Binary Linear Programming problem and genetic-based matheuristic. Finally, we present results from computational tests referring to realistic data of the Italian city of Rome, This paper is an extended, improved version of the paper "An optimization model for renting public parking slots to carsharing services" published in Transportation Research Procedia vol. 45, pp. 499-506 (2020), as part of the Proceedings of the AIIT 2nd International Congress on Transport Infrastructure and Systems in a changing world (TIS ROMA 2019-see Carrese et al. 2020).

International audience ; Carsharing services allow customers to carry out short rents of vehicles, classically paying a per-minute fee, and have known a wide success in major cities all around the world in recent years. They represent an important case of shared mobility and are considered a crucial service in modern smart cities. In this work, we highlight the relevant role that parking slots reserved to carsharing vehicles may have in favouring the success and diffusion of such services, also referring to remarkable regulations of some major cities. Given such relevance, we address the problem of a Local Government that intends to select locations to deploy sets of parking slots reserved to carsharing, also selecting the configuration of the set (e.g., the number and orientation of the slots). In order to mathematically represent the problem, we propose an Integer Linear Programming model that includes whether to rent or not to rent a cluster of parking slots to carsharing companies as central decisions, while also comprising constraints that models lower and upper bounds on the number and type of reserved slots (e.g., modeling the tolerance of the local residents with respect to losing slots for parking their private vehicles). Since the problem is hard to solve from a theoretical point of view, we propose a metaheuristic solution algorithm that combines an improved ant colony optimization algorithm, exploiting suitable linear relaxations of the integer model, with an exact large neighborhood search. On the basis of realistic data instances referring to the city of Rome, we report results of computational tests, highlighting that our optimization method can grant good quality solutions associated with a fair distribution of the reserved parking slots.

International audience ; Smart mobility systems represent a new generation of transport systems that are strongly supported by information and communications technologies, allowing a continuous connection between the system administrators, the customers/users, the transport infrastructures and the vehicles. A major example of these systems is represented by carsharing. Carsharing can relieve people from the costly and non-sustainable burden of owning a car, especially when residing in a city. Furthermore, it can reduce pollution and traffic congestion and has been worldwide recognized as a fundamental component of smart cities by policy makers In this study, we provide an overview of relevant regulations for carsharing, highlighting in particular the importance of parking policies. Given this importance, we propose a mathematical optimization model that can be used by a local government to analytically choose the best subset of parking slots to rent to carsharing companies, in order to improve urban mobility. We test the model on realistic data of the city of Rome, showing that we can obtain a fair territorial distribution of the parking slots that satisfies population needs. The data were defined on the basis of our collaboration with professionals of the electric utility company Enel within EGo Car Sharing, an electrical vehicle carsharing service established at the University Roma Tre.

International audience ; Carsharing represents a major example of smart mobility service that allows a customer to rent a vehicle for a limited amount of time paying a per-minute fee. It may relieve people of the costly and non-sustainable burden of owning a car, especially when residing in a city. Though the spread of carsharing may bring significative benefits to (smart) cities, its penetration can be obstructed by nonup-to-date regulations, which can be still tied to a non-smart vision of mobility. In this study, we provide an overview of remarkable city regulations for carsharing, particularly highlighting the importance that parking policies can have in favouring the diffusion and use of carsharing services. Given such importance, we characterize the optimization problem of a local government that wants to analytically choose the best subset of parking slots to rent to carsharing companies, in order to improve urban mobility. To model and solve the problem we propose a new Binary Linear Programming problem and genetic-based matheuristic. Finally, we present results from computational tests referring to realistic data of the Italian city of Rome, This paper is an extended, improved version of the paper "An optimization model for renting public parking slots to carsharing services" published in Transportation Research Procedia vol. 45, pp. 499-506 (2020), as part of the Proceedings of the AIIT 2nd International Congress on Transport Infrastructure and Systems in a changing world (TIS ROMA 2019-see Carrese et al. 2020).

International audience ; In this study, we provide an overview of relevant national and local regulations and policies for smart mobility, focusing on carsharing services. In particular, we highlight the importance of parking policies. Given this importance, we propose a mathematical optimization model that can be used by a local government to analytically choose the best subset of parking slots to rent to carsharing companies, in order to improve urban mobility. Government that must choose which parking slots to rent to carsharing companies in a city, while finding an optimal balance between the interest of the population and those of the profit-oriented companies. Specifically, we propose to formulate this decision problem as a Binary Linear Programming problem, which includes boolean variables to represent the possibility of renting or not a cluster of parking slots. We present the results of testing the model using realistic data related to the City of Rome. Such data are defined on the basis of the experience gained within the collaboration with our industrial partners in EGo , a carsharing service launched at the University Roma Tre with the support of the electric utility company Enel. The application of the model to a set of realistic data of city of Rome shows that the model can return a fair territorial distribution of the parking slots, satisfying various families of constraints limiting the distribution.

International audience ; In this study, we provide an overview of relevant national and local regulations and policies for smart mobility, focusing on carsharing services. In particular, we highlight the importance of parking policies. Given this importance, we propose a mathematical optimization model that can be used by a local government to analytically choose the best subset of parking slots to rent to carsharing companies, in order to improve urban mobility. Government that must choose which parking slots to rent to carsharing companies in a city, while finding an optimal balance between the interest of the population and those of the profit-oriented companies. Specifically, we propose to formulate this decision problem as a Binary Linear Programming problem, which includes boolean variables to represent the possibility of renting or not a cluster of parking slots. We present the results of testing the model using realistic data related to the City of Rome. Such data are defined on the basis of the experience gained within the collaboration with our industrial partners in EGo , a carsharing service launched at the University Roma Tre with the support of the electric utility company Enel. The application of the model to a set of realistic data of city of Rome shows that the model can return a fair territorial distribution of the parking slots, satisfying various families of constraints limiting the distribution.