An Approach to Assess the Effect of Currentness of Spatial Data on Routing Quality

Abstract. During spatial decision making, the quality of the utilized data is of high importance. During navigation these decisions are crucial for being routed to the desired destination (usually going by the shortest or fastest route). Road networks, the main data source for routing, are prone to changes which can have a big impact on the computed route and therefore on travel time. For instance, routes computed using an outdated street network can result in longer travel times, in longer distance, as well in cases where the desired destination might not be anymore reachable via the computed route. Data from OpenStreetMap with different timestamps allows us to download road network snapshots from different years, i.e., from 2014 to 2020. On each of those datasets the fastest route between 500 randomly chosen point pairs in Vienna, Austria, was computed. These routes were also reconstructed on the most recent dataset for evaluation reasons. The resulting travel times, travel length as well as feasibility of the route were compared with the most recent dataset. The results provide a first assessment of temporal quality based on the currentness of a dataset.