Institutional Repository
| Enabling Smart Transportation Systems: A Parallel Spatio-Temporal Database Approach | |
| Ding, ZM; Yang, B; Chi, YY; Guo, LM | |
| 2016 | |
| Source | IEEE TRANSACTIONS ON COMPUTERS
 |
| ISSN | 0018-9340 |
| Volume | 65Issue:5Pages:1377-1391 |
| English Abstract | 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.; 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. |
| Indexed Type | SCI |
| Keyword | Spatial Temporal Moving Objects Database Parallel-distributed General Sql Query Large Volume |
| Department | Beijing Univ Technol, Coll Comp Sci, Beijing 100124, Peoples R China. Aalborg Univ, Dept Comp Sci, DK-9220 Aalborg, Denmark. Beijing Univ Technol, Coll Econ & Management, Beijing 100124, Peoples R China. Chinese Acad Sci, Inst Software, Beijing 100190, Peoples R China. |
| Language | 英语 |
| WOS ID | WOS:000374891300005 |
| Citation statistics | |
| Content Type | 期刊论文 |
| URI | http://ir.iscas.ac.cn/handle/311060/17333 |
| Collection | 中国科学院软件研究所 |
| Recommended Citation 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. |
| Files in This Item: | ||||||
| File Name/Size | DocType | Version | Access | License | ||
| 07271016.pdf（1146KB） | 开放获取 | License | Application Full Text | |||
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