*2.2. Tra*ffi*c Analysis, Mobility Pattern and Tra*ffi*c Model*

Traffic analysis enables firstly the identification of potential locations and their respective connection points to the power grid of charging stations. They provide secondly information for the energy demand at these charging stations, which can be derived based on vehicles driving distances. In addition, they provide the necessary data for modelling synthetic charging load profiles for e-mobility.

The identification of potential locations for charging stations is based on the so-called traffic demand. To support the identification of potential locations, two basic parameters are defined: the number of motor vehicles at one location and the duration of stay at the location. In addition to the analysis of the mobility behaviour of people (individual person-related consideration), the field of traffic planning also deals with the location-related calculation of traffic volumes.

Traffic is caused by the fact that people travel to different locations in order to pursue certain activities. These activities can be summed up for the "purpose of the stay" of the people on site. The required change of location is assigned to the "purpose of stay", also called user group. Therefore, the traffic analysis differs between seven various user groups: a trip home, a trip to work with a private or company car, a trip for shopping, a trip for execution (e.g., visit to the doctor), a trip to leisure activities and a trip to education. [23–25]

The mobility pattern of each user group includes original destination matrices according to Bosserhoff [23]. These matrices describe the relative proportion of arriving and departing vehicles in each hour of the day in relation to the total amount of vehicles of one day (24 h). Finally, cumulative distribution functions of arriving and departing vehicles are generated for these daily distributions. Two examples of original daily distributions according to Bosserhoff for the purpose "trip home" and "trip to work-shift operation" as well as the respective cumulative distributions are shown in Figure 1 [11]. Furthermore, by using the daily distributions of arrivals and departures, as step 1, relative distributions of the present vehicles can be derived for every hour: from the vehicles present at the beginning of an hour, the departing vehicles are added and the departing vehicles are subtracted. In step 2, the average duration of stay at the considered location will be calculated. From the daily distribution of the vehicles present, the sum of the duration of stay of all vehicles can be calculated. This sum divided by the number of arrived vehicles gives the average duration of stay per vehicle.

**Figure 1.** Original destination matrices according to Bosserhoff [23] and cumulative distribution function (**a**) trip home; (**b**) trip to work—shift operation. Reproduced from [11].

The distances travelled are needed for the charging station design (number of charging points, charging power) as well as for the determination of the energy demand. They are the result of the individual behaviour of persons (choice of destination) and the distance between the locations. This means that they cannot be derived directly from the location-related information. Therefore, surveys on mobility behaviour are carried out to obtain such average data like the distance travelled. Such surveys usually deal with the mobility behaviour of the inhabitants of a geographical unit (municipality, city, etc.). While the private sector is well represented in such surveys, commercial traffic is not treated so intensively. In addition, the commercial traffic differs very clearly between the various sectors. A determination of the distances travelled for commercial traffic is made possible by the evaluation of sector-specific "driving profiles" [24]. While the consideration of "driving profiles" is especially important for commercial traffic, as this is the only way to describe its characteristics and dimensions, this is of negligible interest in the private sector for an average working day. This is due to the fact that in the private sector there are usually fixed residences and thus trip purposes, for example "living - working - shopping – living". Such trip chains are used in this paper to illustrate charging at workplaces.

In traffic modelling, the division of an area usually takes place in smaller geographical units, so-called traffic cells. The formation of traffic cells is geographically based on contiguous road network sections and administrative borders. Traffic cells combine several (postal) addresses into one larger unit. In a traffic model, the traffic between the cells (and not within the cells) is essentially calculated. Since the boundaries of the traffic cells do not necessarily match with those of the energy cells, which are created during the development of the simplified cell-based grid model, an approach had to be found to transfer the information from the traffic cells into the energy cells. For this approach, the "grid point's method" is used in this work. Grid point data are available in Austria in the smallest unit in a 100 by 100 meter square. For the case study Leoben, the traffic model is therefore built based on this 100 by 100 meter grid unit. The cell boundaries of the energy cells can therefore be defined taking into account the grid topology and the guidelines described in [11] to achieve the best possible accuracy of the cell-based grid model. As shown in Figure 2, each grid unit has a grid point, in which the statistical data such as number of trips, average duration of stay and average distance travelled are stored for the seven various user groups. A more detailed breakdown of the data can be found in Appendix A. All data is created for each hour of the day in an average weekday. The grid units enable each energy cell to incorporate the specific grid points within its boundaries, with the result that the statistical data and the resulting traffic data can be clearly allocated to the energy cells. All grid points within an energy cell are finally aggregated to an energy node.

**Figure 2.** Visualization grid point, energy cell and energy node.

However, the statistics stored in the grid points do not fully cover the need for data to determine the energy demand for charging. The missing data is supplemented by comprehensive reports on mobility behaviour, such as "Österreich unterwegs 2013/2014" [25] and "Mobilität in Deutschland" [26].

In addition to the traffic analyses and mobility patterns, vehicle-related specifications such as battery capacity, average energy consumption and charging efficiency are required to model the synthetic charging load profiles for e-mobility. Therefore, based on the EV-models registered in Germany [27], 15 different EV models for passenger traffic are identified and a distribution function of the EV type is derived. An overview of the considered 15 different EV models is given in Appendix B, Table A2. Subsequently, the specific vehicle parameters mentioned above are assigned to each EV type based on the ADAC eco-test [28]. The specified average energy consumption is defined for an ambient temperature of 20 ◦C [29]. In order to take into account the seasonal variations in temperature and average energy consumption, the average energy consumption for the seasons are calculated based on average ambient temperatures for summer, winter and transition [30].
