Establishing a bike-sharing system in the city of Salzburg Results Figure 3 shows the service areas with a walking distance of 400 m for the stations. A higher value stands for a higher density. Figure 4 shows the number of provided bikes per station. From the survey results, bike trips were derived. Figure 5 shows the most important trip patterns. About 50% of persons with home dwelling, 53% of persons with secondary residence, 66,5 % of the working population respectively 62% of the daily population can reach one of the planned bike-sharing stations in a walking distance of 400 meters. Introduction Bike-sharing „can be described as „short-term bicycle rental service for inner-city transportation providing bikes at unattended stations (Vogel, Greiser et al. 2011)“. A bike-sharing system „consists of a number of stations where users can rent and return bikes (Rainer - Harbach, Papazek et al. 2013)“. Bike-sharing stations with available bikes need to be distributed over the whole city area (Vogel, Greiser et al. 2011). The goal of this project is to establish a bike-sharing system in the city of Salzburg with 70 stations. Materials and Methods The following datasets were provided by the ZGIS and used in the analysis: Results from a survey where users could set possible locations for stations Points of interest like important bus stops, university buildings, student hostels and accomodation facilities Raster layer from Statistik Austria containing the number of persons with home dwelling and secondary residence, working and daily populaiton After Garcia-Palomares, Gutierrez et al. (2012), four steps are necessary to determine optimal locations of bike-sharing stations: The distribution of possible users was calculated through the generation of Kernel Density Maps for the attributes of the raster layer from Statistik Austria, for the resulting points from the survey and the accomodation facilities (attribute field: number of beds). The resulting raster layers were reclassified for doing a Weighted Overlay. Figure 2 gives an overview of layers and weights in the Weigthed Overlay: Based on the result from the Weighted Overlay, 70 bike-sharing stations were set manually in the study area and their accessibility was analysed through the generation of Service Areas with a walking distance of 400 meters (see Figure 3). To determine the properties of the stations, the tool „Tabulate Intersection“ was for an intersection of the layers from Statistik Austria, accomodation facilities, student hostels and university buildings (number of students and teachers). The resulting tables were converted into Excel files to calculate the properties for the bike-sharing stations. Figure 4 shows the number of provided bikes per station. Figure 3: Service areas for the bike – sharing stations Investigation of the distribution of possible users Definition of locations for the bike-sharing stations Analysis of accessibility of the stations Determine properties of the stations (provided bikes and docks) Figure 1: Four steps to determine optimal locations of bike-sharing staions Figure 4: Number of provided bikes per station Persons with home dwelling (20% weight) Working population (20% weight) Daily population (20% weight) Weighted Overlay Persons with secondary residence (20% weight) Number of beds accomodation facilities (10% weight) Locations from the survey (10% weight) Figure 5: Cost Matrix Lines for the most important trip patterns Figure 2: Layers and weights in the Weighted Overlay References Garcia-Palomares, J. C., et al. (2012). "Optimizing the location of stations in bike-sharing programs: A GIS approach." Applied Geography 35: 235 - 246. Rainer - Harbach, M., et al. (2013). Balancing Bike-Sharing Systems: A Variable Neighborhood Search Approach. Evolutionary Computation in Combinatorial Optimization: 13th European Conference, EvoCOP 2013, Vienna, Austria, April 3-5, 2013. Proceeding. Vogel, P., et al. (2011). "Understanding Bike-Sharing Systems using Data Mining: Exploring Activity Patterns." Procedia Social and Behavioural Sciences 20: 514 - 523. Student: Gabriel Schendl Used software: ArcMap 10.4 and ArcGIS Pro 1.3 University of Salzburg – Department of Geoinformatics (Z_GIS) I3 Project Winter term 2016 Supervision and data provision: Mag. Dr. Bernhard Zagel