Exploring The Use of OpenStreetMap Data (OSM) and GPS Traces for Validating Driving Routes and Identifying Prohibited Maneuvers in Direction Services

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Antonios Karatzoglou, Tijana Bekic, Vashutosh Agrawal, Mohit Khanna, Aleksandar Matejevic, Varun Kakkar, Michael R. Evans, Aleksandar Samardzija, Jacob Whitbeck, Dragomir Yankov, Nikola Perin, Nikola Todic, Goran Predovic

Published: 01 Jan 2023, Last Modified: 19 Sept 2025GeoIndstry@SIGSPATIAL 2023EveryoneRevisionsBibTeXCC BY-SA 4.0
Abstract: As people increasingly adopt a more flexible and mobile way of living, map applications and navigation services have gained increasingly in importance. In order for these services to be useful to their users, their providers need to ensure and maintain a high quality in terms of accuracy and reliability. Keeping track of the service quality is essential, especially in a dynamic domain like this, where the underlying road network data might change from one day to the next due for instance to temporal road closures or turn restrictions. Although the changes might be small and local, they can still have a big impact on the routing quality of direction services. The focus of this study lies on providing the means to avoid such high-impact scenarios in a cost-efficient manner. In particular, we focus on the legality aspect of routes that reflects the degree of prohibited by law maneuvers within a suggested route. First, we define a set of appropriate sample-based metrics to help us track a route's legality. Then, we introduce an automated pipeline based on OSM data and GPS traces that is able to support and reduce the load of our human judges to identify illegal maneuvers in a sample of candidate routes. Finally, we evaluate our pipeline using a random sample of 1,306 US routes while a local map data provider as well as human judgement via visual inspection serve as our ground truth. Our results show that our method is able to sufficiently cover most of the route sections in our candidate route samples and identify 90% of all illegal maneuvers while reducing the load of manual human judgement by 86%.