نتایج جستجو برای: road map
تعداد نتایج: 261637 فیلتر نتایج به سال:
Facing limited time and budgetary resources, state and local health departments need a practical, competency-based training approach to meet the all-hazards readiness requirements of their employees. The Road Map to Preparedness is a training tool designed to assist health departments in providing comprehensive, agency-tailored readiness instruction to their employees. This tool uses an incenti...
In this paper, we proposed a new segmentation-and-grouping framework for road map inference from sparsely-sampled GPS traces. First, we extended DBSCAN with the orientation constraint to partition the whole point set of traces to clusters representing road segments. Second, we proposed an adaptive k-means algorithm that the k value is determined by an angle threshold to reconstruct nearly strai...
Visualizations and simulations in an urban environment need 3D digital maps rather than traditional 2D maps, and constructing 3D maps from multiple data sources has become a promising and challenging research area nowadays. In this paper, we propose a method for modeling roads, buildings, and ground from 2D digital maps and Laser Range Finder(LRF) data. Firstly, road surfaces are created throug...
Numerous map-matching techniques have been developed to improve positioning, using Global Positioning System (GPS) data and other sensors. However, most existing map-matching algorithms process GPS data with high sampling rates, to achieve a higher correct rate and strong universality. This paper introduces a novel map-matching algorithm based on a hidden Markov model (HMM) for GPS positioning ...
Recent years witnessed popular use of various mobile devices, e.g., smart phones, vehicle networks and wearable watches. Such mobile devices generate massive trajectory data, and literature have proposed various algorithms to leverage the trajectory data for map inference. Unfortunately, such algorithms are hard to achieve both high map quality and computation efficiency. In this paper, we prop...
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