计算机科学 ›› 2021, Vol. 48 ›› Issue (3): 188-195.doi: 10.11896/jsjkx.200600134
刘嘉琛, 秦小麟, 朱润泽
LIU Jia-chen, QIN Xiao-lin, ZHU Run-ze
摘要： 随着射频识别(RFID)技术的不断发展,其相比全球定位系统(GPS)具有高精度、数据信息量大的优势,将其应用于智能交通以预测移动对象位置受到广泛关注。然而,由于其定位基站分布离散,并且不同基站对位置预测的影响权重不同,以及长期的历史信息会来带维数灾难等,移动对象的位置预测面临着严峻的挑战。针对这些挑战,在分析现有预测算法的不足的基础上,提出了一种长短期记忆网络(LSTM)和注意力(Attention)机制相结合的机器学习模型(LSTM-Attention)。该算法将one-hot编码后的输入向量通过神经网络进行降维处理后,利用注意力机制来发掘不同的定位基站对位置预测的权重影响,最后进行位置预测。在南京交管局提供的RFID数据集上进行的对比实验表明,与现有算法相比,所提算法在预测准确性上有明显的提升。
中图分类号:
| [1]BAO J,ZHENG Y,MOKBEL M F.Location-based and prefe-renceaware recommendation using sparse geo-social networking data[C]//Proceedings of the 20th International Conference on Advances in Geographic Information Systems.New York:ACM,2012:199-208. [2]PARK H,LEE Y J,CHAE J,et al.Online Approach for Spatio-Temporal Trajectory Data Reduction for Portable Devices[J].Computer Science and Technology,2013,28(4):597-604. [3]HSIEH H P ,LIN S D ,ZHENG Y.Inferring Air Quality for Station Location Recommendation Based on Urban Big Data[C]//the 21th ACM SIGKDD International Conference.ACM,2015. [4]YANG H,ZHANG Y H,GUO P.Vehicle trajectory recognition based on RFID data and GPS data [C]//Proceedings of 2017 China Urban Transportation Planning Annual Conference.2017. [5]GIANNOTTI F,NANNI M,PINELLI F,et al.Trajectory pattern mining [C]//Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.New York:ACM,2007:330-339. [6]MIKOŁA J,MORZY A.Prediction of Moving Object LocationBased on Frequent Trajectories[J].Lecture Notes in Computer Science,2006,4263:583-592. [7]KRUMM J.A Markov model for driver turn prediction[C]//Proceedings of the Society of Automotive Engineers World Congress.2016:1-7. [8]KILLIJIAN M O.Next place prediction using mobility Markov chains[C]//Proceedings of the Workshop on Measurement.Privacy,and Mobility,Helsinki,New York:ACM,2012:3. [9]LIU Q,WU S,WANG L,et al.Predicting the next location:a recurrent model with spatial and temporal contexts[C]//Proceedings of the 30th AAAI Conference on Artificial Intelligence.Menlo Park:AAAI,2016:194-200. [10]BENGIO Y,SIMARD P Y,FRASCONI P.Learning long-term dependencies with gradient descent is difficult[J].IEEE Transactions on Neural Networks,1994,5(2):157-166. [11]HOCHREITER S,SCHMIDHUBER J.Long short-term memory[J].Neural Computation,1997,9(8):1735-1780. [12]SU L M,LI L.Research on trajectory prediction method based on machine learning[C]//IOP Conference Series Materials Sci-ence and Engineering.2019. [13]LI Q,ZHENG Y,XIE X,et al.Mining user similarity based on location history[C]//ACM Sigspatial International Symposium on Advances in Geographic Information Systems.ACM,2008. [14]BIRANT D,KUT A.ST-DBSCAN:An algorithm for clustering spatial-temporal data[J].Data & Knowledge Engineering,2007,60(1):208-221. [15]PALMA,ANDREY T,BOGORN Y,et al.A clustering-basedapproach for discovering interesting places in trajectories[C]//Acm Symposium on Applied Computing.DBLP,2008. [16]KOREN Y,BELL R M,VOLINSKY C.Matrix factorizationtechniques for recommender systems[J].IEEE Computer,2009,42(8):30-37. [17]XIONG L,CHEN X,HUANG T K,et al.Temporal collaborative filtering with Bayesian probabilistic tensor factorization[C]//Proceedings of the SIAM International Conference on Data Mining.Philadelphia:SIAM,2010:211-222. [18]YING J J C,LEE W C.Semantic trajectory mining for location prediction[C]//Proceedings of the 19th ACM SIGSPATIAL International Symposium on Advances in Geographic Information Systems.New York:ACM,2011:34-43. [19]MONREALE A,PINELLI F,TRASARTI R,et al.WhereNext:a location predictor on trajectory pattern mining[C]//Procee-dings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.New York:ACM,2009:637-646. [20]MORZY M.Mining frequent trajectories of moving objects for location prediction[C]//Proceedings of the 5th International Conference on Machine Learning and Data Mining in Pattern Recognition.Berlin,Heidelberg:Springer,2007:667-680. [21]RENDLE S,FREUDENTHALER C,SCHMIDT-THIEME L.Factorizing personalized Markov chains for next-basket recommendation[C]//Proceedings of the Internationl Conference on World Wide Web.New York :ACM,2010:811-820. [22]MATHEW W,RAPOSO R,MARTINS B.Predicting future locations with hidden Markov models[C]//Proceedings of the 2012 ACM conference on ubiquitous computing.ACM,2012:911-918. [23]GAO Y,JIANG G H,QIN X L,et al.Position prediction algorithm of moving objects based on LSTM[J].Journal of Compu-ter Science and Exploration,2019,13(1):23-34. [24]PALANGI H,DENG L,SHEN Y L,et al.Deep sentence embedding using long short-term memory networks:analysis and application to information retrieval[J].IEEE/ACM Transactions on Audio,Speech and Language Processing,2016,24(4):694-707. [25]LUONG M T ,PHAM H ,MANNING C D.Effective Approaches to Attention-based Neural Machine Translation[J].arXiv:1508.04025v5,2015. [26]PAULUS R ,XIONG C ,SOCHER R .A Deep Reinforced Mo-del for Abstractive Summarization[J].arXiv:1705.04304v3,2017. [27]CHENG J P,LI D,LAPATA M.Long Short-Term Memory-Networks for Machine Reading[J].arXiv:1601.06733v7,2016. [28]CHEN T Q,GUESTRIN C.Xgboost:A scalable tree boosting system[C]//Proceedings of the 22nd ACM Sigkdd International Conference on Knowledge Discovery and Data Mining.New York:ACM,2016:785-794. [29]FAN W,KUN F,YANG W,et al.A Spatial-Temporal-Semantic Neural Network Algorithm for Location Prediction on Moving Objects[J].Algorithms,2017,10(2):37. |
| [1] | 周芳泉, 成卫青. 基于全局增强图神经网络的序列推荐 Sequence Recommendation Based on Global Enhanced Graph Neural Network 计算机科学, 2022, 49(9): 55-63. https://doi.org/10.11896/jsjkx.210700085 |
| [2] | 戴禹, 许林峰. 基于文本行匹配的跨图文本阅读方法 Cross-image Text Reading Method Based on Text Line Matching 计算机科学, 2022, 49(9): 139-145. https://doi.org/10.11896/jsjkx.220600032 |
| [3] | 周乐员, 张剑华, 袁甜甜, 陈胜勇. 多层注意力机制融合的序列到序列中国连续手语识别和翻译 Sequence-to-Sequence Chinese Continuous Sign Language Recognition and Translation with Multi- layer Attention Mechanism Fusion 计算机科学, 2022, 49(9): 155-161. https://doi.org/10.11896/jsjkx.210800026 |
| [4] | 熊丽琴, 曹雷, 赖俊, 陈希亮. 基于值分解的多智能体深度强化学习综述 Overview of Multi-agent Deep Reinforcement Learning Based on Value Factorization 计算机科学, 2022, 49(9): 172-182. https://doi.org/10.11896/jsjkx.210800112 |
| [5] | 饶志双, 贾真, 张凡, 李天瑞. 基于Key-Value关联记忆网络的知识图谱问答方法 Key-Value Relational Memory Networks for Question Answering over Knowledge Graph 计算机科学, 2022, 49(9): 202-207. https://doi.org/10.11896/jsjkx.220300277 |
| [6] | 汪鸣, 彭舰, 黄飞虎. 基于多时间尺度时空图网络的交通流量预测模型 Multi-time Scale Spatial-Temporal Graph Neural Network for Traffic Flow Prediction 计算机科学, 2022, 49(8): 40-48. https://doi.org/10.11896/jsjkx.220100188 |
| [7] | 王馨彤, 王璇, 孙知信. 基于多尺度记忆残差网络的网络流量异常检测模型 Network Traffic Anomaly Detection Method Based on Multi-scale Memory Residual Network 计算机科学, 2022, 49(8): 314-322. https://doi.org/10.11896/jsjkx.220200011 |
| [8] | 姜梦函, 李邵梅, 郑洪浩, 张建朋. 基于改进位置编码的谣言检测模型 Rumor Detection Model Based on Improved Position Embedding 计算机科学, 2022, 49(8): 330-335. https://doi.org/10.11896/jsjkx.210600046 |
| [9] | 朱承璋, 黄嘉儿, 肖亚龙, 王晗, 邹北骥. 基于注意力机制的医学影像深度哈希检索算法 Deep Hash Retrieval Algorithm for Medical Images Based on Attention Mechanism 计算机科学, 2022, 49(8): 113-119. https://doi.org/10.11896/jsjkx.210700153 |
| [10] | 孙奇, 吉根林, 张杰. 基于非局部注意力生成对抗网络的视频异常事件检测方法 Non-local Attention Based Generative Adversarial Network for Video Abnormal Event Detection 计算机科学, 2022, 49(8): 172-177. https://doi.org/10.11896/jsjkx.210600061 |
| [11] | 闫佳丹, 贾彩燕. 基于双图神经网络信息融合的文本分类方法 Text Classification Method Based on Information Fusion of Dual-graph Neural Network 计算机科学, 2022, 49(8): 230-236. https://doi.org/10.11896/jsjkx.210600042 |
| [12] | 金方焱, 王秀利. 融合RACNN和BiLSTM的金融领域事件隐式因果关系抽取 Implicit Causality Extraction of Financial Events Integrating RACNN and BiLSTM 计算机科学, 2022, 49(7): 179-186. https://doi.org/10.11896/jsjkx.210500190 |
| [13] | 熊罗庚, 郑尚, 邹海涛, 于化龙, 高尚. 融合双向门控循环单元和注意力机制的软件自承认技术债识别方法 Software Self-admitted Technical Debt Identification with Bidirectional Gate Recurrent Unit and Attention Mechanism 计算机科学, 2022, 49(7): 212-219. https://doi.org/10.11896/jsjkx.210500075 |
| [14] | 彭双, 伍江江, 陈浩, 杜春, 李军. 基于注意力神经网络的对地观测卫星星上自主任务规划方法 Satellite Onboard Observation Task Planning Based on Attention Neural Network 计算机科学, 2022, 49(7): 242-247. https://doi.org/10.11896/jsjkx.210500093 |
| [15] | 赵冬梅, 吴亚星, 张红斌. 基于IPSO-BiLSTM的网络安全态势预测 Network Security Situation Prediction Based on IPSO-BiLSTM 计算机科学, 2022, 49(7): 357-362. https://doi.org/10.11896/jsjkx.210900103 |
|
||
首页