A predictive location model for location-based services
A predictive location model for location-based services
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DOI：
10.1145/956676.956693
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发表时间：
2003-11
期刊：
影响因子：
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通讯作者：
H. Karimi;Xiong Liu
中科院分区：
文献类型：
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作者：
H. Karimi;Xiong Liu
基于位置的服务(LBSS)通过位置感知移动设备利用有关用户位置的信息来提供服务，例如他们所请求的最近的感兴趣的要素。这是LBSS中的一种常见策略，虽然它是需要的并且使用户受益，但当预测未来位置(例如，稍后的位置)时，还有额外的好处。位置预测的一个主要优点是它为LBS提供了扩展的资源，主要是时间，以提高系统可靠性，这又增加了用户的信心和对LBS的需求。然而，目前的位置预测研究大多集中在广义位置模型上，该模型将地理范围划分为规则形状的单元。这些模型不适用于其目标是计算和表示道路上服务的某些LBS，因为一个小区可能包含多个道路，而服务的计算和交付可能需要用户在其上行驶的确切道路。我们提出了一种新的模型，称为预测位置模型(PLM)，用于预测道路级别的LBS中的位置。PLM的前提是几何和拓扑技术，使用户能够及时地获得所需的服务。
Location-Based Services (LBSs) utilize information about users' locations through location-aware mobile devices to provide services, such as nearest features of interest, they request. This is a common strategy in LBSs and although it is needed and benefits the users, there are additional benefits when future locations (e.g., locations at later times) are predicted. One major advantage of location prediction is that it provides LBSs with extended resources, mainly time, to improve system reliability which in return increases the users' confidence and the demand for LBSs. However, much of the current location prediction research is focused on generalized location models, where the geographic extent is divided into regular-shape cells. These models are not suitable for certain LBSs whose objective is to compute and present on-road services, because a cell may contain several roads while the computation and delivery of a service may require the exact road on which the user is driving. We propose a new model, called Predictive Location Model (PLM), to predict locations in LBSs with road-level granularities. The premise of PLM is geometrical and topological techniques allowing users to receive timely and desired services.