Site selection for electric cars of a car-sharing service

Ion, Luminita; Cucu, Tatiana; Boussier, Jean-Marie; Teng, Fei; Breuil, Dominique

doi:10.3390/wevj3030531

Cited by 10 publications

(4 citation statements)

References 9 publications

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“…To extend ECs service on a city scale or to other urban areas and to guarantee its success in the long term, the operational and management decision-making support and data collection studying the location of stations are of utmost importance. Ion et al [19] have discussed this framework for small urban areas based on the modelling of the preferences of potential users. This ensures the successful transfer of these systems on a large scale or to other cities.…”

Section: A Strategic and Tactical Decision Levelmentioning

confidence: 99%

Modeling, Relocation, and Real-Time Inventory Control of One-Way Electric Cars Sharing Systems in a Stochastic Petri Nets Framework

Benarbia

Axhausen

Farooq

2021

IEEE Trans. Intell. Transport. Syst.

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In recent years, one-way Electric Car sharing (ECs) systems have been introduced in many cities. One-way trips, as well as battery range issues, directly influence the quality and dynamics of such systems. Due to the demand and supply imbalance at stations, the ECs operators are faced with crucial operational challenges to reduce the relocation costs and increase the number of users. An agent-based relocation strategy based on real-time inventory control within the framework of generalized stochastic Petri Nets (PN) and a discrete event simulation has been proposed in this paper. Furthermore, an associated system performance evaluation was also developed. This model further assesses the effects of system characteristics such as the battery charging level availability threshold on the behavior and dynamics of the system. Moreover, the developed model and simulation show the potential of using PN models to predict critical situations, analyze relocation strategy efficiency, and improve system performance. Results from the simulation indicate that the overall relocation trips are reduced by estimating the time to launch the relocation process, as well as the conflicts between agents (controlling the assignment of agents among stations) during the balancing process are efficiently resolved. The proposed model and simulation algorithm have been applied to the BlueSG network in downtown Singapore.

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Section: A Strategic and Tactical Decision Levelmentioning

confidence: 99%

Modeling, Relocation, and Real-Time Inventory Control of One-Way Electric Cars Sharing Systems in a Stochastic Petri Nets Framework

Benarbia

Axhausen

Farooq

2021

IEEE Trans. Intell. Transport. Syst.

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“…Brandeau and Chiu make a first attempt to classify and categorize different kinds of location problems (5). Ion et al apply MCA with fuzzy logic to select sites for electric car-sharing stations (6). Current et al analyze 45 papers of facility location cases based on multiobjective location and conclude that the complexity in modeling the location problem objectives entails four categories (cost minimization, profit maximization, demand orientation, and environmental concerns) and their subcategories (7).…”

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confidence: 99%

Spatially Aware Model for Optimal Site Selection

Efthymiou

Antoniou

Tyrinopoulos

2012

Transportation Research Record: Journal of the Transportation R

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People's perceptions about the appropriate location of mobility centers are modeled. A case study for the Municipality of Kalamaria in Thes-saloniki, Greece, is presented. The results of a stated preference survey, which revealed residents' perceptions about the optimal locations for these centers, as well as the spatial and other characteristics of the region, were used as inputs. Models were then developed and estimated: a linear regression model, a spatial simultaneous autoregressive (SAR) model, and a geographically weighted regression (GWR) model. The exploratory analysis used the respondents' perception of optimal location as the response variable, and the explanatory variables are information about the population; points of interest; bus routes; average income; road network (i.e., length, number of lanes, and average speeds); and distances from stadium, pedestrian shopping zone, and paid parking facility. Results indicated that the selection of the appropriate location for a mobility center was influenced primarily by number of residents, number of points of interest, number of bus routes, average vehicle speed, and length of the road network. The GWR model seemed to fit the data best, whereas Moran's I-test showed that the model solved the autocorrelation of the residuals, something that the other models [ordinary least squares (OLS) and SARmix] fail to accomplish. The model's predicted values were localized (unlike OLS) and made the decision easier. The proposed method is generic (not system dependent) and can be applied to different optimal location selection schemes, especially those with similar demand characteristics (e.g., for automobile- or bike-sharing stations).

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“…Although location determination is a factor that directly affects the sustainability of carsharing services, only a few studies have focused on methods to identify optimal carsharing locations. Ion et al (2009) investigated optimal locations for electric carsharing services by modeling user preferences. Celsor and Millard-Ball (2007) analyzed potential carsharing locations by using a geographic information system (GIS) tool, based on the characteristics of existing locations.…”

Section: Introductionmentioning

confidence: 99%

Correlation between optimal carsharing locations and carbon dioxide emissions in urban areas

Lee

Byun²,

Lee

et al. 2014

Int. J. Environ. Sci. Technol.

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Carsharing, an alternative to car ownership, is being encouraged by many national governments as a means to alleviate air pollution and traffic congestion. Previously, many carsharing companies determined service locations through trial and error, but they currently define their parking locations in metropolitan cities for maximum customer coverage. However, identifying carsharing locations according to the experiences of the pioneering cities might not yield valid results in some Asian countries where carsharing systems are unknown. Hence, this study examines the characteristics of carsharing users in Daejeon, a small Korean city, to determine that city's optimal carsharing service locations. A geographic information system was used to analyze and determine the best spatial areas according to two data categories: internal and external demand factors. Suitable carsharing locations were ranked by the results of a grid analysis. Thirty optimal locations were then determined from the location-allocation model in a network analysis module. Determining optimal carsharing locations should also be directly correlated with the reduction of carbon dioxide emissions. Carbon dioxide emission reduction from carsharing was predicted at 62,070 tCO2eq for the year 2013; emission reductions were predicted to increase further to 172,923 tCO2eq by 2020. Thus, carsharing is an innovative strategy for traffic demand management that can alleviate air pollution. The results of this study indicate that further research is necessary to examine the relationship between optimal carsharing locations and carbon dioxide emission reduction from using lower-emission carsharing vehicles, such as electric vehicles.

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Site selection for electric cars of a car-sharing service

Cited by 10 publications

References 9 publications

Modeling, Relocation, and Real-Time Inventory Control of One-Way Electric Cars Sharing Systems in a Stochastic Petri Nets Framework

Modeling, Relocation, and Real-Time Inventory Control of One-Way Electric Cars Sharing Systems in a Stochastic Petri Nets Framework

Spatially Aware Model for Optimal Site Selection

Correlation between optimal carsharing locations and carbon dioxide emissions in urban areas

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