1,720,986 research outputs found
Design research on sustainable mobility: an electric vehicle sharing service for Milano, Italy
No full text“Conflict analysis for environmental impact assessment: a case study of a transportation system in a tourist area”
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Cognitive mapping for decision aiding: an application to the design of a vehicle sharing service
No full textCognitive mapping and multi-criteria analysis for decision aiding: an application to the design of an electric vehicle sharing service
Full text linkThe paper presents a model for the design of an electric car sharing service for the city of Milano. Several options of service configurations have been analysed and evaluated according to indicators, to measure the performance of such options in respect to relevant dimensions (i.e., economic and financial costs and revenues, mobility, social benefits, environmental effects). We set up a multicriteria decision analysis, structured by means of cognitive maps. Causal networks to estimate the effects of the options have been identified and instantiated by means of simulation techniques and other qualitative and quantitative models. The focus of the paper is on the development and use of the causal maps and their integration with a multicriteria method. The use of cognitive maps allowed to capture the multiple values of the problem and the value trees of stakeholders objectives. The proposed method can be useful in general for design and planning of mobility service, especially at a strategic level
The vehicle redistribution problem for an electric vehicle sharing service
No full textThis work is inspired by the Green Move project that aims to design and implement a flexible service of vehicle sharing in Milan, based on electric vehicles (EVs) and open to a wide range
of dierent typology of users. Different optimization problems need to be solved to allow an efficient management of the user requests by part of a central unit. For instance the EVs
can be parked at several stations and the users would take an EV from a station and leave it in another one. To prevent a station from running out of EVs (or of parking slots) the central
unit needs to redistribute the EVs. Up till now the problem of the EV redistribution in a sharing system has been faced with a solution approach similar to that one adopted for the
bike sharing, i.e. collecting the exceeding vehicles through a
fleet of trucks (in the case of EV by way of tow-trucks) and move them in the stations where they are lacking. We think
that such approach is too drastic and not suitable to an urban setting where the EVs may be parked in stations not easily reachable by tow-trucks. Moreover the operation of loading
EVs in tow-trucks is very time consuming. For this reason we propose a completely different solution approach consisting in moving the EVs by way of a team of workers that directly drive
the EVs that need to be moved. Such a problem generates a challenging pickup and delivery problem with new features that to the best of our knowledge never has been considered in the
literature. For such a problem we yield a Mixed Integer Linear Programming formulation that we test on verisimilar instances built on the Milan road network
A Carpooling Service for Universities: A Case Study inMilan
No full textCarpooling is an ITS based on a shared use of private cars. We present the project to design, implement and test a car pooling service for Università Statale
and Politecnico di Milano universities. At the best of our knowledge no work in the literature faces the problem of designing a carpooling service for university students taking into account the needs of this particular class of users e.g. friend/enemy list, possible multiple destinations, imposition of partial pools.
For this reason we have put right a tailored matching algorithm to form the crews meeting all these needs
The vehicle relocation problem for the one-way electric vehicle sharing: an application to the Milan case
No full textTraditional car-sharing services are based on the two-way scheme, where the user picks up and returns the vehicle at the same parking station. Some services allow also one-way trips, where the user can return the vehicle in another station. The one-way scheme is more attractive for the users, but may pose a problem for the distribution of the vehicles, due to a possible unbalancing between the user demand and the availability of vehicles or free slots at the stations. Such a problem is more complicated in the case of electric car sharing, where the travel range depends on the level of charge of the vehicles. In a
previous work, we introduced a new approach to relocate the vehicles where cars are moved by personnel of the service operator to keep the system balanced. Such relocation method generates a new challenging pickup and delivery problem that we call the Electric Vehicle Relocation Problem (EVRP). In this work we focus on a method to forecast the unbalancing of a car-sharing system. We apply such method to the data yielded by the Milan transport agency taking into account the location
and capacity of the present charging stations in Milan. In this way, using a Mixed Integer Linear Programming formulation of EVRP, we can estimate the advantages of our relocation approach on verisimilar instances
The relocation problem for the one-way electric vehicle sharing
Full text linkTraditional car sharing services have been based on the two-way scheme, where the user picks up and returns the vehicle at the same parking station. Some innovative services permit also one-way trips, that is, the user is allowed to return the vehicle in another station. The one-way scheme is more attractive for the users, but may lead to an unbalance between the user demand, and the availability of vehicles or free lots at the stations. In such cases, the service provider could reallocate the fleet and restore a better distribution of the vehicles among the stations. In the case of electric car sharing, such a problem is more complex because the travel range depends on the level of the battery charge. This article presents a new approach for the relocation of electric vehicles (EVs), carried out by the staff of the service provider to keep the system balanced. Such an approach generates a challenging Paired Pickup and Delivery Problem with Time Windows with new features that to the best of our knowledge have never been considered in the literature. We call such a problem the EV relocation problem (EVRP). We yield a mixed integer linear programming (MILP) formulation of the EVRP and some techniques to speedup its solution through a state-of-the-art solver (CPLEX). Moreover, we develop a simple but effective heuristic based on such a formulation and four upper bound generation methods. We test the performances of both the MILP formulation and the heuristic on instances built on the Milan road network
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