OpenStreetMap(OSM) has recently emerged as a promising solution to the challenging task of creating an accurate and up-to-date map of the fast changing world. However, as this database is primarily created by amateurs, the quality of its data is unknown unless a comparison to ground truth is performed. Road networks form a significant feature of OSM and in the past several researchers have tried to assess their quality by using techniques which usually involve some sort of referencing to maps captured by the corresponding national mapping agency. However, finding some automated machine learning based solution to solve this problem of quality assurance seems to be a natural choice. Nevertheless, in order to apply any algorithm successfully, an appropriate representation of the underlying data is a prerequisite. The two predominant street network representations (namely, the primal and dual) fail to represent data in a form suitable for machine learning. Therefore, this paper presents a simple and intuitive representation for street networks where information like street name and category is easily available. This representation offers the combined advantages of primal and dual forms and allows for an efficient application of machine learning algorithms towards quality assessment of street networks datsets.
Recent technological trends enable modern traffic prediction and management systems in which the analysis and prediction of movements of objects is essential. To this extent the present paper proposes IncCCFR---a novel, incremental approach for managing, mining, and predicting the incrementally evolving trajectories of moving objects. In addition to reduced mining and storage costs, a key advantage of the incremental approach is its ability to combine multiple temporally relevant mining results from the past to capture temporal and periodic regularities in movement. The approach and its variants are empirically evaluated on a large real-world data set of moving object trajectories, originating from a fleet of taxis, illustrating that detailed closed frequent routes can be efficiently discovered and used for prediction.
Identifying spatial patterns of traffic collisions is critical for improving the efficiency and effectiveness of the deployment of traffic enforcement resources as well as road safety. In recent years, many studies have focused on finding locations with high collision concentration, so-called hotspots, without integrating the likely available non-spatial attributes into analysis. In this paper we propose a method for identifying the sets of non-spatial attribute-value pairs (AVPs) that together contribute significantly to the spatial clustering of the corresponding collisions. We call such a set of AVPs a Spatial Co-Clustering Pattern (SCCP). By applying our method on the city of Edmonton's historical collision data, we discovered a larger number of meaningful hotspot patterns than traditional hotspot analysis methods did, and revealed the relevant non-spatial indicators for explaining those hotspots.
This paper discusses the problem of wayfinding in complex environments. Movement is complicated in many ways, including the existence of obstacles that impede travel across continuous space. To support route planning and navigation for the blind, shipping, robotics, etc., methods have been developed to identify the shortest or most efficient path. This paper details a GIS based method for finding an optimal path for this problem. The developed approach offers substantial computational advances over existing approaches, making it appealing for real time travel support. Application results are presented to demonstrate the effectiveness of the approach.
