基于轨迹大数据的热点路径识别与查询处理关键技术研究

项目名称： 基于轨迹大数据的热点路径识别与查询处理关键技术研究

项目编号： No.61300031

项目类型： 青年科学基金项目

立项/批准年度： 2014

项目学科： 自动化技术、计算机技术

项目作者： 罗吴蔓

作者单位： 广州市香港科大霍英东研究院

项目金额： 28万元

中文摘要： 轨迹大数据的到来对传统的时空数据查询与管理提出了新的机遇与挑战。作为一种新型的路径查找技术，热点路径的识别与查询已成为城市规划、时空数据挖掘，以及各种基于位置的服务等应用的关键核心技术。然而它也面临着路径的"热度"主观性强，形式化定义难度大；轨迹数据索引机制不完整，查询速度慢；以及查询复杂，实时算法准确差等诸多难题与挑战。 针对这些问题，本项目将以尽量准确的反映大众寻径的经验和智慧为目的，分析和提取热点路径的关键特征，设计合理的最优路径识别技术。并以此为基础，研究各类热点路径的查询与优化。我们首先研究路径的热度计算方法与排名机制，然后分别研究基于时段的和带条件约束的热度路径查询方法。最后我们将搭建系统和测试平台，利用真实的轨迹大数据进行实验，验证所提方法与技术的有效性和查询效率。

中文关键词： 轨迹数据；路由决策；事务规划；；

英文摘要： The arrival of big trajectory data is changing the management of spatio-temporal datasets in many ways. As a novel path-finding query, finding the most frequent path (MFP) plays a crucial role in many real-world applications like urban planning, spatio-temporal data mining, and various location-based services. However, finding MFP is challenging due to three reasons. First, it is nontrivial to give a satisfactory definition of MFP. Second, existing data access methods become suboptimal when querying trajectory data. Finally, the accuracy of the real-time approximate algorithms for MFP finding tends to be poor. To address these issues, we will study how to extract key properties of MFP and how to reasonably identify the MFPs. The goal is to try to reveal the common routing preferences of the past travelers. Based on this, we proceed to investigate the querying methods for different MFP queries. Specifically, we first study the computation functions for "path frequency" and its ranking systems. Then we study the queries of finding time period-based most frequent paths and constraint-aware most frequent paths in big trajectory data, respectively. Finally, we conduct extensive experiments using real datasets to evaluate the effectiveness and the efficiency of our proposed approaches.

英文关键词： trajectory data；route planning；event planning；；