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An Efficient Method of Sharing Mass Spatio-Temporal Trajectory Data Based on Cloudera Impala for Traffic Distribution Mapping in an Urban City

机译:基于Cloudera Impala的城市时空轨迹数据共享的高效方法

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摘要

The efficient sharing of spatio-temporal trajectory data is important to understand traffic congestion in mass data. However, the data volumes of bus networks in urban cities are growing rapidly, reaching daily volumes of one hundred million datapoints. Accessing and retrieving mass spatio-temporal trajectory data in any field is hard and inefficient due to limited computational capabilities and incomplete data organization mechanisms. Therefore, we propose an optimized and efficient spatio-temporal trajectory data retrieval method based on the Cloudera Impala query engine, called ESTRI, to enhance the efficiency of mass data sharing. As an excellent query tool for mass data, Impala can be applied for mass spatio-temporal trajectory data sharing. In ESTRI we extend the spatio-temporal trajectory data retrieval function of Impala and design a suitable data partitioning method. In our experiments, the Taiyuan BeiDou (BD) bus network is selected, containing 2300 buses with BD positioning sensors, producing 20 million records every day, resulting in two difficulties as described in the Introduction section. In addition, ESTRI and MongoDB are applied in experiments. The experiments show that ESTRI achieves the most efficient data retrieval compared to retrieval using MongoDB for data volumes of fifty million, one hundred million, one hundred and fifty million, and two hundred million. The performance of ESTRI is approximately seven times higher than that of MongoDB. The experiments show that ESTRI is an effective method for retrieving mass spatio-temporal trajectory data. Finally, bus distribution mapping in Taiyuan city is achieved, describing the buses density in different regions at different times throughout the day, which can be applied in future studies of transport, such as traffic scheduling, traffic planning and traffic behavior management in intelligent public transportation systems.
机译:时空轨迹数据的有效共享对于理解海量数据中的交通拥堵很重要。但是,城市公交网络的数据量正在快速增长,每天达到一亿个数据点。由于有限的计算能力和不完整的数据组织机制,在任何领域中访问和检索质量时空轨迹数据都是困难且效率低下的。因此,我们提出了一种基于Cloudera Impala查询引擎ESTRI的优化,高效的时空轨迹数据检索方法,以提高海量数据共享的效率。作为质量数据的出色查询工具,Impala可用于质量时空轨迹数据共享。在ESTRI中,我们扩展了Impala的时空轨迹数据检索功能,并设计了合适的数据分区方法。在我们的实验中,选择了太原北斗(BD)公交网络,其中包含2300辆带有BD定位传感器的公交车,每天产生2000万条记录,这导致了两个困难,如简介部分所述。此外,ESTRI和MongoDB也在实验中应用。实验表明,与使用MongoDB进行五千万,一亿,一亿五千万和两亿的数据量检索相比,ESTRI实现了最有效的数据检索。 ESTRI的性能大约是MongoDB的七倍。实验表明,ESTRI是一种检索质量时空轨迹数据的有效方法。最后,实现了太原市公交车分布图的绘制,描述了一天中不同时间不同区域公交车的密度,可用于未来的交通研究中,如智能公交中的交通调度,交通规划和交通行为管理等。系统。

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