时间依赖路网上的移动对象K近邻查询算法

doi:10.11896/jsjkx.181102231

计算机科学 ›› 2020, Vol. 47 ›› Issue (1): 79-86.doi: 10.11896/jsjkx.181102231

所属专题：数据库技术

• 数据库&大数据&数据科学 • 上一篇下一篇

时间依赖路网上的移动对象K近邻查询算法

张彤,秦小麟

(南京航空航天大学计算机科学与技术学院南京 210016)

收稿日期:2018-11-30 发布日期:2020-01-19
通讯作者: 秦小麟(qinxl@nuaa.edu.cn)
基金资助:
国家自然科学基金(61373015,61728204)

K Nearest Neighbors Queries of Moving Objects in Time-dependent Road Networks

ZHANG Tong,QIN Xiao-lin

(College of Computer Science and Technology,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China)

Received:2018-11-30 Published:2020-01-19
About author:ZHANG Tong,born in 1996,postgra-duate,is student member of China Computer Federation (CCF).Her main research interests include spatial database query technology;QIN Xiao-lin,born in 1953,Ph.D,professor,Ph.D supervisor,is member of China Computer Federation (CCF).His main reasearch interests include spatial and spatio-temporal databases,data management and security in distributed environment,etc.
Supported by:
This work was supported by the National Natural Science Foundation of China (61373015,61728204).

摘要/Abstract

摘要： 随着基于位置服务的广泛应用,时间依赖路网上的对象查询逐渐成为研究热点。以往研究大多只针对时间依赖路网上的静态对象(如加油站、餐厅等),未考虑到移动对象(如出租车)的情况,而移动对象的查询在日常生活中有着非常广泛的应用场景。因此,文中提出了一种针对时间依赖路网上的移动对象K近邻查询算法TD-MOKNN,该算法分为预处理阶段和查询阶段。在预处理阶段,通过建立路网和网格索引,提出了一种新的移动对象到路网的映射方法,解除了以往研究假设移动对象恰好在路网顶点上的限制;在查询阶段,采用启发式搜索,借助倒排网格索引计算了一种新的高效启发值,通过预处理信息和启发值设计了高效K近邻查询算法,并给出了算法的正确性证明和时间复杂度分析。实验验证了所提算法的有效性,相比现有算法,TD-MOKNN算法在遍历顶点数和响应时间上分别减少了55.91%和54.57%,查询效率平均提升了55.2%。

关键词: A^*算法, K近邻查询, 时间依赖路网, 网格索引, 移动对象

Abstract: With the wide application of location-based services,object-based query on time-dependent road network has gradually become a research hotspot.In the past,most of the researches only focused on static objects on time-dependent road networks (such as gas stations,restaurants,etc.),and did not take into account the situation of mobile objects (such as taxis).The query of mobile objects has a very wide range of applications in daily life.Therefore,the K nearest neighbor query algorithm TD-MOKNN of moving object is proposed for time-dependent road network.The algorithm is divided into pre-processing stage and query stage.In the pre-processing stage,the road network and grid index are established,and a new mapping method of moving objects to the road network is proposed,which removes the limitation of previous researches that moving objects happen to be on the intersection of the road networks.In the query stage,a new efficient heuristic value is calculated by using inverted grid index,and an efficient k-nearest neighbor query algorithm is designed by using pre-processing information and heuristic value.Experiments verify the effectiveness of the algorithm.Compared with existing algorithm,TD_MOKNN algorithm reduces the number of traversing vertices and response time by 55.91% and 54.57% respectively,and improves the query efficiency by 55.2% on average.

Key words: A^* algorithm, Grid index, K nearest neighbors query, Moving object, Time-dependent road network

中图分类号:

TP311

张彤,秦小麟. 时间依赖路网上的移动对象K近邻查询算法[J]. 计算机科学, 2020, 47(1): 79-86. https://doi.org/10.11896/jsjkx.181102231

ZHANG Tong,QIN Xiao-lin. K Nearest Neighbors Queries of Moving Objects in Time-dependent Road Networks[J]. Computer Science, 2020, 47(1): 79-86. https://doi.org/10.11896/jsjkx.181102231

参考文献

[1]YANG H M,WANG H Z,WU Y B.Observation and Characteri- stics Analysis of Traffic Flow in Nanjing [J].Environmental Science and Technology,2011,24(a02):98-101.
[2]CHO H J,CHUNG C W.An Efficient and Scalable Approach to CNN Queries in a Road Network [C]∥International Conference on Very Large Data Bases.Trondheim,Norway:DBLP,2005:865-876.
[3]PAPADIAS D,ZHANG J,MAMOULISM N,et al.Query Processing in Spatial Network Databases [J].Proc Vldb,2003,29:802-813.
[4]GOLDBERG A V,HARRELSON C.Computing the Shortest Path:A^* Search Meets Graph Theory:Technical Report MSR-TR-2004-24[R].Philadelphia,PA,USA:Society for Industrial and Applied Mathematics,2005:156-165.
[5]GEISBERGER R,VETTER C.Efficient Routing in Road Networks with Turn Costs [C]∥International Conference on Experimental Algorithms.Berlin:Springer-Verlag,2011:100-111.
[6]ABEYWICKRAMA T,CHEEMA M A.Efficient Landmark- Based Candidate Generation for kNN Queries on Road Networks[C]∥Proceedings of the 22nd International Conference on Database Systems for Advanced Applications.Berlin:Springer,2017:425-440.
[7]XUAN K,ZHAO G,TANIAR D,et al.Constrained Range Search Query Processing on Road Networks [J].Concurrency and Computation:Practice and Experience,2011,23(5):491-504.
[8]DIJKSTRA E W.A Note on Two Problems in Connection with Graphs [J].Numerische Mathematics,1959,1(1):269-271.
[9]ERWING M.The Graph Voronoi Diagram with Applications
[J].Networks,2015,36(3):156-163.
[10]KOLAHDOUZAN M,SHAHABI C.The Graph Voronoi Dia- gram with Applications [C]∥Proceedings of the Thirtieth International Conference on Very Large Data Bases.San Francisco,CA:Morgan Kaufmann,2004:840-851.
[11]HUANG X,JENSEN C S,ŠALTENIS S.The Islands Approach to Nearest Neighbor Querying in Spatial Networks [C]∥Proceedings of the 2005 International Symposium on Spatial and Temporal Databases(SSTD 2005).Berlin:Springer,2005:73-90.
[12]COOKE K L,HALSEY E.The Shortest Route Through a Network with Time-Dependent Internodal Transit Times [J].Journal of Mathematical Analysis & Applications,1966,14(3):493-498.
[13]GEORGE B,KIM S,SHEKHAR S.Spatio-temporal Network Databases and Routing Algorithms:a Summary of Results [C]∥International Conference on Advances in Spatial and Temporal Databases.Berlin:Springer-Verlag,2007:460-477.
[14]DEMIRYUREK U,BANAEIKASHANI F,SHAHABI C.To- wards K-Nearest Neighbor Search in Time-Dependent Spatial Network Databases [C]∥International Conference on Databa-ses in Networked Information Systems.Berlin:Springer-Verlag,2010:296-310.
[15]CRUZ L A,NASCIMENTO M A,MACÊDO J A F.K-nearest Neighbors Queries in Time-Dependent Road Networks [J].Journal of Information & Data Management,2012,3(3):211-216.
[16]KOMAI Y,NGUYEN D H,HARA T,et al.kNN Search Utilizing Index of the Minimum Road Travel Time in Time-Depen-dent Road Networks [J].Information Sciences,2014,255(1):135-154.
[17]CHUCRE M,NASCIMENTO S,MACEDO J A,et al.Taxi,Please! A Nearest Neighbor Query in Time-Dependent Road Networks [C]∥IEEE International Conference on Mobile Data Management.Piscataway,NJ:IEEE,2016:180-185.
[18]NIEVERGELT J,HINTERBERGER H,SEVCIKK C.The Grid File:An Adaptable,Symmetric Multi-Key File Structure [J].ACM TODS,1981,9(1):38-71.
[19]MENG N N,ZHOU X D.Realization on Spatial Index with Grids of Fixed Size [J].Beijing Surveying and Mapping,2003(1):7-11.
[20]ORDA A,ROM R.Shortest-Path and Minimum-Delay Algorithms in Networks with Time-Dependent Edge-Length [J].Journal of the Acm,1990,37(3):607-625.

相关文章 15

[1]	李冰荣, 皮德常, 候梦如. 基于CNN和LSTM的移动对象目的地预测 Destination Prediction of Moving Objects Based on Convolutional Neural Networks and Long-Short Term Memory 计算机科学, 2021, 48(4): 70-77. https://doi.org/10.11896/jsjkx.200200024
[2]	曹波, 陈锋, 成静, 李华, 李永乐. 基于全向路口模型的非结构化道路重复节点路径规划 Route Planning of Unstructured Road Including Repeat Node Based on Bidirectional Search 计算机科学, 2021, 48(11A): 77-80. https://doi.org/10.11896/jsjkx.201200193
[3]	韩京宇, 许梦婕, 朱曼. 路网上基于时空锚点的移动对象群体和个体运动监测方法 Detecting Group-and-individual Movements of Moving Objects Based on Spatial-Temporal Anchors of Road-network 计算机科学, 2020, 47(11): 294-303. https://doi.org/10.11896/jsjkx.191100083
[4]	张怀峰, 皮德常, 董玉兰. iBTC:一种基于独立森林的移动对象轨迹聚类算法 iBTC:A Trajectory Clustering Algorithm Based on Isolation Forest 计算机科学, 2019, 46(1): 251-259. https://doi.org/10.11896／j.issn.1002-137X.2019.01.039
[5]	王波涛,梁伟,赵凯利,钟汉辉,张玉圻. 基于HBase的支持频繁更新与多用户并发的R树 R-tree for Frequent Updates and Multi-user Concurrent Accesses Based on HBase 计算机科学, 2018, 45(7): 42-52. https://doi.org/10.11896／j.issn.1002-137X.2018.07.007
[6]	宫海彦,耿生玲. 复杂障碍空间中基于移动对象运动规律的不确定轨迹预测 Prediction of Uncertain Trajectory Based on Moving Object Motion in Complex Obstacle Space 计算机科学, 2018, 45(6A): 130-134.
[7]	董天阳, 尚跃辉, 程强. 方向感知的路网移动对象范围查询算法 Direction-aware Moving Object Range Query Algorithm in Road Network 计算机科学, 2018, 45(11): 210-219. https://doi.org/10.11896／j.issn.1002-137X.2018.11.033
[8]	刘凯洋. AP-I:一种快速预测路网中移动对象未来位置的索引 AP-I:An Index to Quickly Answer Predictive Queries for Moving Objects 计算机科学, 2017, 44(Z6): 314-318. https://doi.org/10.11896/j.issn.1002-137X.2017.6A.072
[9]	薛忠斌,白利光,何宁,周烜,周歆,王珊. 路网中高吞吐量移动对象实时查询算法 Throughput Oriented Real-time Query Processing Algorithm for Moving Objects in Road Network 计算机科学, 2017, 44(3): 16-19. https://doi.org/10.11896/j.issn.1002-137X.2017.03.004
[10]	周翔宇,程春玲,杨雁莹. 基于分布式内存数据库的移动对象全时态索引 Full-temporal Index of Moving Objects Based on Distributed Main Memory Database 计算机科学, 2016, 43(7): 203-207. https://doi.org/10.11896/j.issn.1002-137X.2016.07.037
[11]	杨阳,吉根林,鲍培明. 基于网格索引的时空轨迹伴随模式挖掘算法 Algorithm for Mining Adjoint Pattern of Spatial-Temporal Trajectory Based on Grid Index 计算机科学, 2016, 43(1): 107-110. https://doi.org/10.11896/j.issn.1002-137X.2016.01.025
[12]	易显天,徐展,张可,郭承军. 一种基于受限网络的移动对象索引结构 Index Structure for Moving Objects Based on Restricted Network 计算机科学, 2015, 42(5): 211-214. https://doi.org/10.11896/j.issn.1002-137X.2015.05.042
[13]	贲婷婷,秦小麟,王丽. 基于语义和访问权限的室内移动对象索引 Index of Indoor Moving Objects Based on Semantics and Access Permission 计算机科学, 2015, 42(3): 178-184. https://doi.org/10.11896/j.issn.1002-137X.2015.03.037
[14]	李实吉,秦小麟,施竣严. 障碍空间中的移动对象位置预测 Location Prediction of Moving Objects in Obstructed Space 计算机科学, 2014, 41(7): 216-221. https://doi.org/10.11896/j.issn.1002-137X.2014.07.045
[15]	汤志俊,樊明锁,何贤芒,陈华辉,董一鸿. 位置不确定移动对象的连续概率反Skyline查询 Continuous Probabilistic Reverse Skyline Query on Moving Objects with Uncertainty 计算机科学, 2013, 40(7): 147-152.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

时间依赖路网上的移动对象K近邻查询算法

K Nearest Neighbors Queries of Moving Objects in Time-dependent Road Networks

PDF (PC)

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

Metrics

本文评价

推荐阅读 0