Modeling public charging infrastructure considering points of interest and parking potentials

Abstract

Abstract. Considering climate change, it is essential to reduce CO2 emissions. The provision of charging infrastructure in public spaces for electromobility – along with the substitution of conventional power generation by renewable energies – can contribute to the energy transition in the transport sector. Scenarios for the spatial distribution of this charging infrastructure can help to exemplify the need for charging points and their impact, for example on power grids. We model two kinds of demand for public charging infrastructure. First, we model the demand for public charging points to compensate for the lack of home charging points, which is derived from a previously developed and published model addressing electric-vehicle ownership (with and without home charging options) in households. Second, and in the focus of the work presented here, is the demand for public charging infrastructure at points of interest (POIs). Their locations are derived from OpenStreetMap (OSM) data and weighted based on an evaluation of movement profiles from the Mobilität in Deutschland survey (MiD, German for “Mobility in Germany”). We combine those two demands with the available parking spaces and generate distributions for possible future charging points. We use a raster-based approach in which all vector data are rasterized and computations are performed on a municipality's full grid. The presented application area is Wiesbaden, and the methodology is generally applicable to municipalities in Germany. The model is compared with three other models or model variants in a correlation comparison in order to determine the influence of certain model assumptions and input data. The identification of potential charging points in public spaces plays an important role in modeling the future energy system – especially the power grid – as the rapid adoption of electric vehicles will shift locations of electrical demand. With our investigation, we would like to present a new method to simulate future public charging point locations and show the influences of different modeling methods.

Dynamic Traffic Assignment Model Based on GPS Data and Point of Interest (POI) in Shanghai

“…Little literature meticulously analysed the impact of realistic factors such as POI (Point of Interest) and intersection impedance on traffic flow in the road network. In recent years, more and more studies notice static traffic impact factors such as schools, shopping malls, and parks (i.e., POIs), which can be summarized into three categories: identification of urban functional areas [25][26][27][28], analysis of the spatial distribution of a specific type of public facilities [29][30][31], and analysis of urban traffic characteristics based on POI data [22,25,32,33]. It is concluded that the impact of static traffic factors on cities is not negligible, and they have significant value for research on sustainable urban development.…”