Adam Rousell

A polygon-based approach for matching OpenStreetMap road networks with regional transit authority data

ABSTRACT Matching road networks is an essential step for data enrichment and data quality assessment, among other processes. Conventionally, road networks from two datasets are matched using a line-based approach that checks for the similarity of properties of line segments. In this article, a polygon-based approach is proposed to match the OpenStreetMap road network with authority data. The algorithm first extracts urban blocks that are central elements of urban planning and are represented by polygons surrounded by their surrounding streets, and it then assigns road lines to edges of urban blocks by checking their topologies. In the matching process, polygons of urban blocks are matched in the first step by checking for overlapping areas. In the second step, edges of a matched urban block pair are further matched with each other. Road lines that are assigned to the same matched pair of urban block edges are then matched with each other. The computational cost is substantially reduced because the proposed approach matches polygons instead of road lines, and thus, the process of matching is accelerated. Experiments on Heidelberg and Shanghai datasets show that the proposed approach achieves good and robust matching results, with a precision higher than 96% and a F1-score better than 90%.

Deriving incline values for street networks from voluntarily collected GPS traces

ABSTRACT When producing optimal routes through an environment, considering the incline of surfaces can be of great benefit in a number of use cases. For instance, steep segments need to be avoided for energy-efficient routes and for routes that are suitable for mobility-restricted people. Such incline information may be derived from digital elevation models (DEMs). However, the corresponding data capturing methods (e.g. airborne LiDAR, photogrammetry, and terrestrial surveying) are expensive. Current low-cost and open-licensed DEM (e.g. Shuttle Radar Topography Mission [SRTM] and Advanced Spaceborne Thermal Emission and Reflection Radiometer [ASTER]) generally do not have sufficient horizontal resolution or vertical accuracy, and lack a global coverage. Therefore, we have investigated an alternative low-cost approach which derives street incline values from GPS traces that have been voluntarily collected by the OpenStreetMap contributors. Despite the poor absolute accuracy of this data, the relative accuracy of traces seems to be sufficient enough to compute incline values with reasonable accuracy. A validation shows that the accuracy of incline values calculated from GPS traces slightly outperforms incline values derived from SRTM-1 DEM, though results depend on how many traces per street segment are used for computation.

The Role of Contextual Info-Marks in Navigating a Virtual Rural Environment

Navigation is a task performed in both large and small scale environments. Landmarks within an environment are of great benefit to these navigational tasks, but in large rural environments such landmarks may be sparse. It has been shown that landmarks need not be purely visual and that a change in context for a feature can make it become a landmark against its surroundings (such as being provided with significant meaning). Such meaning could be added through personal experience or by informing the observer via some form of communication. To investigate the effects of providing such contextual information on navigational performance, experiments were conducted in a large rural virtual environment where the delivery method of the information was varied between onscreen and PDA display. Users were instructed to perform a route tracing navigation task. In some instances users were presented with textual information about specific locations within the environment which appeared when they were in the vicinity of the location. Both quantitative and qualitative data were collected and analyzed, with results indicating that although the actual performance in the task was not significantly improved, users felt that their performance was better and the task easier when they were presented with the contextual information.