The potential of reducing greenhouse gases in transport sector attracted different groups to promote electric vehicles (EVs) as a component of sustainable mobility development. However, studies assert that the usage of EV is currently limited mainly to short-distance trips and the users are only those who have the opportunity of charging their car privately at home or workplaces. This research highlights the lack of public charging stations and tries to develop a demand-oriented location model for finding the optimal location of fast charging stations (FCSs) from a user's point of view. In urban areas the users can make use of activity time of their daily routine activities such as supermarket shopping for charging the battery of their EVs. Therefore, the proposed location model focuses on the interaction between people's travel behaviour and urban infrastructure. First, the potential of a facility for installation of FCS is determined by means of its different attributes such as number of attracted motorized individual trips, opening hours and parking lot availability, activity time of users in different facilities in relation to the charging time and synergy effect of closely allied facilities. In the second step, the study area is zoned and the calculated potential for facilities is transferred to the relevant zones, considering users' maximum detour acceptance, catchment area of facilities as well as spatial impact of existing charging stations. The input data, which rely mainly on open source and publically accessible data, are analysed and depicted as different georeferenced layers in the geographical information system (ArcGIS Software). The proposed location model aims to cover the growing demand for public FCS of current EV users as well as one step forward to increase the acceptance of electro mobility among potential users.
CITATION STYLE
Shirmohammadli, A., & Vallée, D. (2017). Developing a location model for fast charging infrastructure in urban areas. In International Journal of Transport Development and Integration (Vol. 1, pp. 159–170). WITPress. https://doi.org/10.2495/TDI-V1-N2-159-170
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