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公开(公告)号：US08566030B1
公开(公告)日：2013-10-22
申请号：US13278060
申请日：2011-10-20
IPC分类号： G08G1/00
CPC分类号： G01C21/3476 , G01C21/3446 , G01C21/3492 , G08G1/0116 , G08G1/0129 , G08G1/096816
摘要： The class of k Nearest Neighbor (k NN) queries in spatial networks has been studied in the literature. Existing approaches for k NN search in spatial networks assume that the weight of each edge in the spatial network is constant. However, real-world edge-weights are time-dependent and vary significantly in short durations, hence invalidating the existing solutions. The problem of k NN search in time-dependent spatial networks, where the weight of each edge is a function of time, is addressed herein. Two indexing schemes (Tight Network Index and Loose Network Index) are proposed to minimize the number of candidate nearest neighbor objects and reduce the invocation of the expensive fastest-path computation in time-dependent spatial networks. We demonstrate the efficiency of our proposed solution via experimental evaluations with real-world data-sets, including a variety of large spatial networks with real traffic-data.
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2.发明授权公开(公告)号：US09062985B2
公开(公告)日：2015-06-23
申请号：US14059212
申请日：2013-10-21
IPC分类号： G01C21/34 , G08G1/01 , G08G1/0968
CPC分类号： G01C21/3476 , G01C21/3446 , G01C21/3492 , G08G1/0116 , G08G1/0129 , G08G1/096816
摘要： The class of k Nearest Neighbor (k NN) queries in spatial networks has been studied in the literature. Existing approaches for k NN search in spatial networks assume that the weight of each edge in the spatial network is constant. However, real-world edge-weights are time-dependent and vary significantly in short durations, hence invalidating the existing solutions. The problem of k NN search in time-dependent spatial networks, where the weight of each edge is a function of time, is addressed herein. Two indexing schemes (Tight Network Index and Loose Network Index) are proposed to minimize the number of candidate nearest neighbor objects and reduce the invocation of the expensive fastest-path computation in time-dependent spatial networks. We demonstrate the efficiency of our proposed solution via experimental evaluations with real-world data-sets, including a variety of large spatial networks with real traffic-data.
摘要翻译： 文献中已经研究了空间网络中k个最近邻（k NN）查询的类。 空间网络中k NN搜索的现有方法假设空间网络中每个边缘的权重是恒定的。 然而，现实世界边际权重是时间依赖性的，并且在短时间内显着变化，因此使现有解决方案无效。 这里解决了时间依赖空间网络中k NN搜索的问题，其中每个边缘的权重是时间的函数。 提出了两个索引方案（紧密网络索引和松散网络索引），以最小化候选最近邻居对象的数量，并减少时间依赖空间网络中昂贵的最快路径计算的调用。 我们通过实际数据集的实验评估来展示我们提出的解决方案的效率，包括具有实际流量数据的各种大型空间网络。
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3.发明申请公开(公告)号：US20140046593A1
公开(公告)日：2014-02-13
申请号：US14059212
申请日：2013-10-21
IPC分类号： G01C21/34
CPC分类号： G01C21/3476 , G01C21/3446 , G01C21/3492 , G08G1/0116 , G08G1/0129 , G08G1/096816
摘要： The class of k Nearest Neighbor (k NN) queries in spatial networks has been studied in the literature. Existing approaches for k NN search in spatial networks assume that the weight of each edge in the spatial network is constant. However, real-world edge-weights are time-dependent and vary significantly in short durations, hence invalidating the existing solutions. The problem of k NN search in time-dependent spatial networks, where the weight of each edge is a function of time, is addressed herein. Two indexing schemes (Tight Network Index and Loose Network Index) are proposed to minimize the number of candidate nearest neighbor objects and reduce the invocation of the expensive fastest-path computation in time-dependent spatial networks. We demonstrate the efficiency of our proposed solution via experimental evaluations with real-world data-sets, including a variety of large spatial networks with real traffic-data.
摘要翻译： 文献中已经研究了空间网络中k个最近邻（k NN）查询的类。 空间网络中k NN搜索的现有方法假设空间网络中每个边缘的权重是恒定的。 然而，现实世界边际权重是时间依赖性的，并且在短时间内显着变化，因此使现有解决方案无效。 这里解决了时间依赖空间网络中k NN搜索的问题，其中每个边缘的权重是时间的函数。 提出了两个索引方案（紧密网络索引和松散网络索引），以最小化候选最近邻居对象的数量，并减少时间依赖空间网络中昂贵的最快路径计算的调用。 我们通过实际数据集的实验评估来展示我们提出的解决方案的效率，包括具有实际流量数据的各种大型空间网络。
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