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Enabling Smart Transportation Systems: A Parallel Spatio-Temporal Database Approach
文献类型：期刊论文
| 作者 | Ding, ZM ; Yang, B ; Chi, YY ; Guo, LM |
| 刊名 | IEEE TRANSACTIONS ON COMPUTERS
 |
| 出版日期 | 2016 |
| 卷号 | 65期号:5页码:1377-1391 |
| 关键词 | Spatial temporal moving objects database parallel-distributed general SQL query large volume |
| ISSN号 | 0018-9340 |
| 中文摘要 | We are witnessing increasing interests in developing "smart cities" which helps improve the efficiency, reliability, and security of a traditional city. An important aspect of developing smart cities is to enable "smart transportation," which improves the efficiency, safety, and environmental sustainability of city transportation means. Meanwhile, the increasing use of GPS devices has led to the emergence of big trajectory data that consists of large amounts of historical trajectories and real-time GPS data streams that reflect how the transportation networks are used or being used by moving objects, e.g., vehicles, cyclists, and pedestrians. Such big trajectory data provides a solid data foundation for developing various smart transportation applications, such as congestion avoidance, reducing greenhouse gas emissions, and effective traffic accident response, etc. Instead of proposing yet another specific smart transportation application, we propose the parallel-distributed network-constrained moving objects database (PD-NMOD), a general framework that manages big trajectory data in a scalable manner, which provides an infrastructure that is able to support a wide variety of smart transportation applications and thus benefiting the smart city vision as a whole. The PD-NMOD manages both transportation networks and trajectories in a distributed manner. In addition, the PD-NMOD is designed to support general SQL queries over moving objects and to efficiently process the SQL queries on big trajectory data in parallel. Such design facilitates smart transportation applications to retrieve relevant trajectory data and to conduct statistical analyses. Empirical studies on a large trajectory data set collected from 3,500 taxis in Beijing offer insight into the design properties of the PD-NMOD and offer evidence that the PD-NMOD is efficient and scalable. |
| 英文摘要 | We are witnessing increasing interests in developing "smart cities" which helps improve the efficiency, reliability, and security of a traditional city. An important aspect of developing smart cities is to enable "smart transportation," which improves the efficiency, safety, and environmental sustainability of city transportation means. Meanwhile, the increasing use of GPS devices has led to the emergence of big trajectory data that consists of large amounts of historical trajectories and real-time GPS data streams that reflect how the transportation networks are used or being used by moving objects, e.g., vehicles, cyclists, and pedestrians. Such big trajectory data provides a solid data foundation for developing various smart transportation applications, such as congestion avoidance, reducing greenhouse gas emissions, and effective traffic accident response, etc. Instead of proposing yet another specific smart transportation application, we propose the parallel-distributed network-constrained moving objects database (PD-NMOD), a general framework that manages big trajectory data in a scalable manner, which provides an infrastructure that is able to support a wide variety of smart transportation applications and thus benefiting the smart city vision as a whole. The PD-NMOD manages both transportation networks and trajectories in a distributed manner. In addition, the PD-NMOD is designed to support general SQL queries over moving objects and to efficiently process the SQL queries on big trajectory data in parallel. Such design facilitates smart transportation applications to retrieve relevant trajectory data and to conduct statistical analyses. Empirical studies on a large trajectory data set collected from 3,500 taxis in Beijing offer insight into the design properties of the PD-NMOD and offer evidence that the PD-NMOD is efficient and scalable. |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000374891300005 |
| 公开日期 | 2016-12-09 |
| 源URL | [http://ir.iscas.ac.cn/handle/311060/17333]  |
| 专题 | 软件研究所_软件所图书馆_期刊论文 |
| 推荐引用方式 GB/T 7714 | Ding, ZM,Yang, B,Chi, YY,et al. Enabling Smart Transportation Systems: A Parallel Spatio-Temporal Database Approach[J]. IEEE TRANSACTIONS ON COMPUTERS,2016,65(5):1377-1391. |
| APA | Ding, ZM,Yang, B,Chi, YY,&Guo, LM.(2016).Enabling Smart Transportation Systems: A Parallel Spatio-Temporal Database Approach.IEEE TRANSACTIONS ON COMPUTERS,65(5),1377-1391. |
| MLA | Ding, ZM,et al."Enabling Smart Transportation Systems: A Parallel Spatio-Temporal Database Approach".IEEE TRANSACTIONS ON COMPUTERS 65.5(2016):1377-1391. |
入库方式： OAI收割
来源：软件研究所
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