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    • 11. 发明授权
    • System and method for fusing vector data with imagery
    • 用图像融合矢量数据的系统和方法
    • US08340360B2
    • 2012-12-25
    • US13158301
    • 2011-06-10
    • Ching-Chien ChenDipsy KapoorCraig A. KnoblockCyrus Shahabi
    • Ching-Chien ChenDipsy KapoorCraig A. KnoblockCyrus Shahabi
    • G06K9/46
    • G06T7/75G06T2207/10032G06T2207/30184
    • Automatic conflation systems and techniques which provide vector-imagery conflation and map-imagery conflation. Vector-imagery conflation is an efficient approach that exploits knowledge from multiple data sources to identify a set of accurate control points. Vector-imagery conflation provides automatic and accurate alignment of various vector datasets and imagery, and is appropriate for GIS applications, for example, requiring alignment of vector data and imagery over large geographical regions. Map-imagery conflation utilizes common vector datasets as “glue” to automatically integrate street maps with imagery. This approach provides automatic, accurate, and intelligent images that combine the visual appeal and accuracy of imagery with the detailed attribution information often contained in such diverse maps. Both conflation approaches are applicable for GIS applications requiring, for example, alignment of vector data, raster maps, and imagery. If desired, the conflated data generated by such systems may be retrieved on-demand.
    • 自动融合系统和技术,提供矢量图像融合和地图图像融合。 矢量图像混合是一种有效的方法,利用来自多个数据源的知识来识别一组精确的控制点。 矢量图像融合提供了各种矢量数据集和图像的自动和准确对齐,适用于GIS应用,例如,需要在大地理区域上对齐矢量数据和图像。 地图图像融合利用常用的矢量数据集作为胶水,自动将街道地图与图像整合。 这种方法提供自动,准确和智能的图像,将图像的视觉吸引力和准确性与通常包含在这种不同地图中的详细归属信息相结合。 两种融合方法都适用于需要例如矢量数据,光栅图和图像对齐的GIS应用。 如果需要,可以根据需要检索由这样的系统生成的混合数据。
    • 14. 发明授权
    • Traffic prediction using real-world transportation data
    • 使用现实世界交通数据的交通预测
    • US09286793B2
    • 2016-03-15
    • US14060360
    • 2013-10-22
    • Bei PanUgur DemiryurekCyrus Shahabi
    • Bei PanUgur DemiryurekCyrus Shahabi
    • G08G1/00G08G1/01
    • G06N5/04G08G1/00G08G1/0112G08G1/0129G08G1/0141
    • Real-time high-fidelity spatiotemporal data on transportation networks can be used to learn about traffic behavior at different times and locations, potentially resulting in major savings in time and fuel. Real-world data collected from transportation networks can be used to incorporate the data's intrinsic behavior into a time-series mining technique to enhance its accuracy for traffic prediction. For example, the spatiotemporal behaviors of rush hours and events can be used to perform a more accurate prediction of both short-term and long-term average speed on road-segments, even in the presence of infrequent events (e.g., accidents). Taking historical rush-hour behavior into account can improve the accuracy of traditional predictors by up to 67% and 78% in short-term and long-term predictions, respectively. Moreover, the impact of an accident can be incorporated to improve the prediction accuracy by up to 91%.
    • 运输网络上的实时高保真时空数据可用于了解不同时间和地点的交通行为,有可能大大节省时间和燃料。 从运输网络收集的现实数据可用于将数据的内在行为纳入时间序列挖掘技术,以提高其对流量预测的准确性。 例如,高峰时段和事件的时空行为可以用于对路段上的短期和长期平均速度进行更准确的预测,即使在出现偶然事件(例如事故)的情况下也是如此。 考虑到历史高峰时期的行为,可以将传统预测指标的准确度分别提高67%和78%,在短期和长期预测中。 此外,可以引入事故的影响,将预测精度提高高达91%。
    • 17. 发明申请
    • Hierarchical and Exact Fastest Path Computation in Time-dependent Spatial Networks
    • 时间依赖空间网络中的分层和精确最快路径计算
    • US20120283948A1
    • 2012-11-08
    • US13455035
    • 2012-04-24
    • Ugur DemiryurekCyrus Shahabi
    • Ugur DemiryurekCyrus Shahabi
    • G01C21/34
    • G01C21/3446G01C21/3492
    • With real-world spatial networks the edge travel-times are time-dependent, where the arrival-time to an edge determines the actual travel-time on the edge. To speed up the path computation, exact and approximate techniques for computation of the fastest path in time-dependent spatial networks are presented. An exact fastest path computation technique based on a time-dependent A* search can significantly improve the computation time and storage complexity of existing approaches. Moreover, for applications with which approximate fastest path is acceptable, the approximate fastest path computation technique can improve the computation time by an order of magnitude while maintaining high accuracy (e.g., with only 7% increase in travel-time of the computed path on average). With experiments using real data-sets (including a variety of large spatial networks with real traffic data) the efficacy of the disclosed techniques for online fastest path computation is demonstrated.
    • 利用现实世界的空间网络,边缘旅行时间是时间依赖的,其中到达边缘的到达时间确定边缘上的实际旅行时间。 为了加快路径计算,提出了用于计算时间依赖空间网络中最快路径的精确和近似技术。 基于时间依赖的A *搜索的确切最快的路径计算技术可以显着提高现有方法的计算时间和存储复杂性。 此外,对于近似最快路径可接受的应用,近似最快路径计算技术可以将计算时间提高一个数量级,同时保持高精度(例如,平均计算路径的行进时间仅增加7% )。 通过使用实际数据集(包括具有实际流量数据的各种大型空间网络)的实验,证明了所公开的在线最快路径计算技术的功效。
    • 18. 发明申请
    • System and Method for Fusing Geospatial Data
    • 用于融合地理空间数据的系统和方法
    • US20110280453A1
    • 2011-11-17
    • US13158301
    • 2011-06-10
    • Ching-Chien ChenDipsy KapoorCraig A. KnoblockCyrus Shahabi
    • Ching-Chien ChenDipsy KapoorCraig A. KnoblockCyrus Shahabi
    • G06K9/46
    • G06T7/75G06T2207/10032G06T2207/30184
    • Automatic conflation systems and techniques which provide vector-imagery conflation and map-imagery conflation. Vector-imagery conflation is an efficient approach that exploits knowledge from multiple data sources to identify a set of accurate control points. Vector-imagery conflation provides automatic and accurate alignment of various vector datasets and imagery, and is appropriate for GIS applications, for example, requiring alignment of vector data and imagery over large geographical regions. Map-imagery conflation utilizes common vector datasets as “glue” to automatically integrate street maps with imagery. This approach provides automatic, accurate, and intelligent images that combine the visual appeal and accuracy of imagery with the detailed attribution information often contained in such diverse maps. Both conflation approaches are applicable for GIS applications requiring, for example, alignment of vector data, raster maps, and imagery. If desired, the conflated data generated by such systems may be retrieved on-demand.
    • 自动融合系统和技术,提供矢量图像融合和地图图像融合。 矢量图像混合是一种有效的方法,利用来自多个数据源的知识来识别一组精确的控制点。 矢量图像融合提供了各种矢量数据集和图像的自动和准确对齐,适用于GIS应用,例如,需要在大地理区域上对齐矢量数据和图像。 地图图像融合利用常用的矢量数据集作为“胶合”,自动将街道地图与图像整合。 这种方法提供自动,准确和智能的图像,将图像的视觉吸引力和准确性与通常包含在这种不同地图中的详细归属信息相结合。 两种融合方法都适用于需要例如矢量数据,光栅图和图像对齐的GIS应用。 如果需要,可以根据需要检索由这样的系统生成的混合数据。