The technology we use to search and calculate dimensions of office location attractiveness and to estimate commuting times is built on OpenStreetMap source data. The match calculation is based on predefined POI (Points of Interest) categories and the weighting applied by the user to specific dimensions. To solve the routing problem we use the GraphHopper engine.
The OpenStreetMap we use is an open source, continuously updated project that produces a free, editable database of the world. Its creation is motivated by the limitations of extracting geographical data from certain regions, as well as the increasing availability of satellite devices. The calculation of travel times is based on reliable road network data provided by satellite and GPS devices. The routing engine analyses the OpenStreetMap file, extracts streets from it and converts it into a graph consisting of nodes (intersections) and edges (streets), where each road has its own weight. The routing optimization problem is solved using the Dijkstra or A algorithm.
The data analysed by our algorithm includes the surface and its slope, maximum speeds and road types. However, OpenStreetMap does not include traffic data – this is an element that we enrich the data with ourselves, taking into account the hourly specifics of commuting to the office (including rush hours). For each analysis area defined, we calculate a unique congestion coefficient based on publicly available traffic data and incorporate it into our calculations. The weights assigned to the edges allow us to create a map for different modes of transport and in different directions. We take into account that some streets are closed to car or pedestrian traffic, two-way traffic, and the slope of the surface.
Knowledge and experience.
Our mission is to create a tool that realistically improves the lives of teams and company efficiency. To achieve this, we combine our expertise in data-science and real estate. The result is a globally unique time finding solution.
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