基于多尺度路网的移动对象建模与索引

道路网络（简称路网）中的移动对象数据管理是近年研究热点。路网具有多尺度特征，移动对象在不同尺度路网具有不同的运行特征，然而，已有研究并未考虑路网的多尺度特征以及移动对象在不同尺度的运行特征，现有的移动对象数据管理技术也不能直接应用于多尺度路网下的移动对象处理。本项目首次提出基于多尺度路网的移动对象建模与索引方法。多尺度路网移动对象数据模型包括多尺度路网模型以及多尺度移动对象模型，实现了路网的多尺度表达、移动对象在多尺度路网中的表达，并给出移动对象的尺度转换方法，得到基于多尺度路网移动对象数据模型整体框架。在多尺度路网移动对象数据模型基础上，提出基于多尺度路网的移动对象轨迹索引，利用双层扩展R树结构索引多尺度路网及移动对象轨迹，索引结构存储多尺度路网以及移动对象信息。本课题的研究将为多尺度移动对象表达与索引提供理论和技术支持，对移动对象数据管理和GIS时空分析具有重要的学术意义。

Data management for moving objects on road networks is a research hotspot in recent years. Road networks have multi-scale feature, and moving objects have distinctive movement feature among different scale road networks, however, the multi-scale feature for road networks and the movement feature for moving objects in different scale haven't been taken into account in the current research; furthermore, the existing technology for moving objects data management can't be directly applied in moving objects on the multi-scale road networks. The project firstly proposes indexing methods and data model for moving objects based on multi-scale road networks.Data Model for moving objects based on multi-scale road networks includes data model for multi-scale road network and for multi-scale moving objects, realizes the multi-scale representation of road networks and the representation of moving objects in multi-scale road networks. The scale transformation method for moving objects is presented, then the integrated framework for data model for moving objects based on multi-scale road networks is given. Based on the data model for multi-scale moving objects， the project proposes trajectory index for moving objects using multi-scale road networks, and accesses multi-scale road networks and trajectory of moving objects by dual extended R-tree structure. The indexing structure store multi-scale road networks and moving objects. The research can provide theoretical and technical support for access methods and representation of moving objects, and it is of great academic significance to manage moving objects and analyze spatio-temporal data of GIS.