A review of prediction methods for moving target trajectories

Wen LIU; Kunlin HU; Yan LI; Zhao LIU

doi:10.11959/j.issn.2096-6652.202115

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A review of prediction methods for moving target trajectories

Special Topic: Intelligent Transportation Systems and Applications | 更新时间：2024-06-05

- A review of prediction methods for moving target trajectories
- Chinese Journal of Intelligent Science and Technology Vol. 3, Issue 2, Pages: 149-160(2021)
- 作者机构：
  
  1. 武汉理工大学航运学院，湖北武汉 430063
  2. 国家水运安全工程技术研究中心，湖北武汉 430063
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-6652.202115
  CLC： TP391
- Published Online：2021-06，
  
  Published：15 June 2021
- 稿件说明：
移动端阅览
Wen LIU, Kunlin HU, Yan LI, et al. A review of prediction methods for moving target trajectories[J]. Chinese journal of intelligent science and technology, 2021, 3(2): 149-160.
DOI：

Wen LIU, Kunlin HU, Yan LI, et al. A review of prediction methods for moving target trajectories[J]. Chinese journal of intelligent science and technology, 2021, 3(2): 149-160. DOI： 10.11959/j.issn.2096-6652.202115.

摘要

随着智能交通系统领域大量移动终端设备的涌现，理解并准确预测移动目标轨迹有助于降低交通事故发生的概率，提高基于位置服务的智能交通应用的质量和水平。主要从数据驱动和行为驱动的角度对移动目标轨迹预测方法进行综述，首先对概率统计、神经网络、深度学习和混合建模等数据驱动方法进行比较；其次对动力学建模和目标意图识别等行为驱动方法的基本概念及研究现状进行概述；然后分别对目标轨迹重建、目标异常行为识别和导航路径规划等轨迹预测应用进行简要叙述；最后讨论了移动目标轨迹预测存在的主要问题以及未来的发展方向。

Abstract

With the rapid emergence of mobile terminal equipment in intelligent transportation system

the deep understanding and accurate prediction of moving target trajectories are capable of reducing the traffic accident probability

and promoting the location service-based intelligent transportation applications.The trajectory prediction methods prediction methods for moving target trajectories were reviewed from the data-driven prediction methods and the behavior-driven trajectories prediction methods.Firstly

the data-driven prediction methods were reviewed

including probabilistic statistics

neural networks

deep learning

and hybrid modeling.Then

the basic conceptions of target behavior-driven trajectories prediction methods were analyzed.The corresponding dynamical modeling and intention recognition methods were reviewed.The trajectory prediction applications were briefly analyzed and reviewed

such as target trajectory reconstruction

target abnormal behavior identification

and navigation route planning.Finally

the main problems and development directions related to prediction of moving target trajectories were discussed.

关键词

Keywords

references

高雅 . 移动对象轨迹数据的位置预测 [D ] . 南京:南京航空航天大学 , 2019 .

GAO Y . Location prediction on moving objects’ trajectory data [D ] . Nanjing:Nanjing University of Aeronautics and Astronautics , 2019 .

仇功达 , 何明 , 杨杰 , 等 . 异常轨迹数据预警与预测关键技术综述 [J ] . 系统仿真学报 , 2017 , 29 ( 11 ): 2608 - 2617 .

QIU G D , HE M , YANG J , et al . Key technologies of precaution and prediction of abnormal spatial-temporal trajectory:a review of recent advances [J ] . Journal of System Simulation , 2017 , 29 ( 11 ): 2608 - 2617 .

PERERA L P , FERRARI V , SANTOS F P , et al . Experimental evaluations on ship autonomous navigation and collision avoidance by intelligent guidance [J ] . IEEE Journal of Oceanic Engineering , 2015 , 40 ( 2 ): 374 - 387 .

孔玮 , 刘云 , 李辉 , 等 . 基于深度学习的行人轨迹预测方法综述 [J ] . 控制与决策 , 2021 :已录用。

KONG W , LIU Y , LI H , et al . A survey of pedestrian trajectory prediction methods based on deep learning [J ] . Control and Decision , 2021 :accepted.

PREVOST C G , DESBIENS A , GAGNON E . Extended Kalmanlter for state estimation and trajectory prediction of a moving object detected by an unmanned aerial vehicle [C ] // Proceedings of the 2007 American Control Conference . Piscataway:IEEE Press , 2007 : 1805 - 1810 .

BARTH A , FRANKE U . Where will the oncoming vehicle be the next second? [C ] // Proceedings of the 2008 IEEE Intelligent Vehicles Symposium . Piscataway:IEEE Press , 2008 : 1068 - 1073 .

乔少杰 , 韩楠 , 朱新文 , 等 . 基于卡尔曼滤波的动态轨迹预测算法 [J ] . 电子学报 , 2018 , 46 ( 2 ): 418 - 423 .

QIAO S J , HAN N , ZHU X W , et al . A dynamic trajectory prediction algorithm based on Kalman filter [J ] . Acta Electronica Sinica , 2018 , 46 ( 2 ): 418 - 423 .

邱洪生 . 基于卡尔曼滤波的船舶航行轨迹异常行为预测算法研究 [D ] . 天津:河北工业大学 , 2012 .

QIU H S . Research on forecasting ship sailed track behavioral abnormalities algorithm based on Kalman filter [D ] . Tianjin:Hebei University of Technology , 2012 .

WANG Z J , NIE Z Q , SHENG G . Dynamic position predicting of underactuated surface vessel with unscented Kalman filter [C ] // Proceedings of the 2018 Chinese Automation Congress . Piscataway:IEEE Press , 2018 : 4030 - 4033 .

VASHISHTHA D , PANDA M . Maximum likelihood multiple model filtering for path prediction in intelligent transportation systems [J ] . Procedia Computer Science , 2018 , 143 : 635 - 644 .

CARTLIDG E J , GONG S H , BAI R B , et al . Spatio-temporal prediction of shopping behaviours using taxi trajectory data [C ] // Proceedings of the 2018 IEEE 3rd International Conference on Big Data Analysis . Piscataway:IEEE Press , 2018 : 112 - 116 .

HAMED M M , AL-MASAEID H R , SAID Z M B . Short-term prediction of trafc volume in urban arterials [J ] . Journal of Transportation Engineering , 1995 , 121 ( 3 ): 249 - 254 .

DING Q Y , WANG X F , ZHANG X Y , et al . Forecasting trafc volume with space-time ARIMA model [C ] // Proceedings of the Advanced Materials Research .[S.l.:s.n. ] , 2011 : 979 - 983 .

高建 , 毛莺池 , 李志涛 . 基于高斯混合-时间序列模型的轨迹预测 [J ] . 计算机应用 , 2019 , 39 ( 8 ): 2261 - 2270 .

GAO J , MAO Y C , LI Z T . Trajectory prediction based on Gauss mixture time series model [J ] . Journal of Computer Applications , 2019 , 39 ( 8 ): 2261 - 2270 .

任宇翔 , 赵建森 , 刘卫 , 等 . 基于AIS数据和LSTM网络的船舶航行动态预测 [J ] . 上海海事大学学报 , 2019 , 40 ( 3 ): 32 - 37 .

REN Y X , ZHAO J S , LIU W , et al . Ship navigation behavior prediction based on AIS data and LSTM network [J ] . Journal of Shanghai Maritime University , 2019 , 40 ( 3 ): 32 - 37 .

岳聚财 . 基于LSTM-ARIMA的短期航迹预测方法研究及可视化系统开发 [D ] . 天津:中国民航大学 , 2020 .

YUE J C . Research on short-term trajectory prediction method based on LSTM-ARIMA and visualization system development [D ] . Tianjin:Civil Aviation University of China , 2020 .

MATHEW W , RAPOSO R , MARTINS B . Predicting future locations with hidden Markov models [C ] // Proceedings of the 2012 ACM Conference on Ubiquitous Computing . New York:ACM Press , 2012 : 911 - 918 .

QIAO S J , SHEN D Y , WANG X T , et al . A self-adaptive parameter selection trajectory prediction approach via hidden Markov models [J ] . IEEE Transactions on Intelligent Transportation Systems , 2015 , 16 ( 1 ): 284 - 296 .

ASAHARA A , MARUYAMA K , SATO A , et al . Pedestrian-movement prediction based on mixed Markov-chain model [C ] // Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems . New York:ACM Press , 2011 : 25 - 33 .

ZHANG X Y , LIU G , HU C , et al . Wavelet analysis based hidden Markov model for large ship trajectory prediction [C ] // Proceedings of the 2019 Chinese Control Conference . Piscataway:IEEE Press , 2019 : 2913 - 2918 .

DALSNES B R , HEXEBERG S , FLÅTEN A L ,, et al . The neighbor course distribution method with Gaussian mixture models for AIS-based vessel trajectory prediction [C ] // Proceedings of the 2018 21st International Conference on Information Fusion . Piscataway:IEEE Press , 2018 : 580 - 587 .

乔少杰 , 金琨 , 韩楠 , 等 . 一种基于高斯混合模型的轨迹预测算法 [J ] . 软件学报 , 2015 , 26 ( 5 ): 1048 - 1063 .

QIAO S J , JIN K , HAN N , et al . Trajectory prediction algorithm based on Gaussian mixture model [J ] . Journal of Software , 2015 , 26 ( 5 ): 1048 - 1063 .

WIEST J , HÖFFKEN M , KREBEL U , et al . Probabilistic trajectory prediction with Gaussian mixture models [C ] // Proceedings of the 2012 IEEE Intelligent Vehicles Symposium . Piscataway:IEEE Press , 2012 : 141 - 146 .

LIM Q , JOHARI K , TAN U X . Gaussian process auto regression for vehicle center coordinates trajectory prediction [C ] // Proceedings of the TENCON 2019-2019 IEEE Region 10 Conference . Piscataway:IEEE Press , 2019 : 25 - 30 .

王新立 . 基于GPR模型的船舶自适应轨迹预测及应用研究 [D ] . 武汉:武汉理工大学 , 2019 .

WANG X L . Research on ship adaptive trajectory prediction and application based on GPR model [D ] . Wuhan:Wuhan University of Technology , 2019 .

林毅 , 张建伟 , 武喜萍 , 等 . 基于 GMM 的航班轨迹预测算法研究 [J ] . 工程科学与技术 , 2018 , 50 ( 4 ): 104 - 109 .

LIN Y , ZHANG J W , WU X P , et al . Study on algorithm for flight trajectory prediction based on GMM [J ] . Advanced Engineering Sciences , 2018 , 50 ( 4 ): 104 - 109 .

张显炀 , 朱晓宇 , 林浩申 , 等 . 基于高斯混合-变分自编码器的轨迹预测算法 [J ] . 计算机工程 , 2020 , 46 ( 7 ): 50 - 57 .

ZHANG X Y , ZHU X Y , LIN H S , et al . Trajectory prediction algorithm based on Gaussian mixture-variational autoencoder [J ] . Computer Engineering , 2020 , 46 ( 7 ): 50 - 57 .

乔少杰 , 彭京 , 李天瑞 , 等 . 基于 CTBN 的移动对象不确定轨迹预测算法 [J ] . 电子科技大学学报 , 2012 , 41 ( 5 ): 759 - 763 .

QIAO S J , PENG J , LI T R , et al . Uncertain trajectory prediction of moving objects based on CTBN [J ] . Journal of University of Electronic Science and Technology of China , 2012 , 41 ( 5 ): 759 - 763 .

段炼 , 胡涛 , 朱欣焰 , 等 . 顾及时空语义的疑犯位置时空预测 [J ] . 武汉大学学报·信息科学版 , 2019 , 44 ( 5 ): 765 - 770 .

DUAN L , HU T , ZHU X Y , et al . Spatio-temporal prediction of suspect location by spatio-temporal semantics [J ] . Geomatics and Information Science of Wuhan University , 2019 , 44 ( 5 ): 765 - 770 .

王垒 , 宋庭新 . 内河航道船舶避碰轨迹规划与预测 [J ] . 湖北工业大学学报 , 2019 , 34 ( 2 ): 64 - 68 .

WANG L , SONG T X . Ship collision trajectory planning and prediction for inland waterway [J ] . Journal of Hubei University of Technology , 2019 , 34 ( 2 ): 64 - 68 .

李万高 , 赵雪梅 , 孙德厂 . 基于改进贝叶斯方法的轨迹预测算法研究 [J ] . 计算机应用 , 2013 , 33 ( 7 ): 1960 - 1963 .

LI W G , ZHAO X M , SUN D C . Prediction of trajectory based on modified Bayesian inference [J ] . Journal of Computer Applications , 2013 , 33 ( 7 ): 1960 - 1963 .

徐婷婷 , 柳晓鸣 , 杨鑫 . 基于 BP神经网络的船舶航迹实时预测 [J ] . 大连海事大学学报 , 2012 , 38 ( 1 ): 9 - 11 .

XU T T , LIU X M , YANG X . BP neural network-based ship track real-time prediction [J ] . Journal of Dalian Maritime University , 2012 , 38 ( 1 ): 9 - 11 .

杨淳 . 基于 BP 神经网络的 AUV 航迹预测方法研究 [D ] . 哈尔滨:哈尔滨工程大学 , 2014 .

YANG C . The research on AUV track prediction method based on BP neural network [D ] . Harbin:Harbin Engineering University , 2014 .

杨彬 , 贺正洪 . 一种 GRNN 神经网络的高超声速飞行器轨迹预测方法 [J ] . 计算机应用与软件 , 2015 , 32 ( 7 ): 239 - 243 .

YANG B , HE Z H . Hypersonic vehicle track prediction based on GRNN [J ] . Computer Applications and Software , 2015 , 32 ( 7 ): 239 - 243 .

马国兵 , 张楠 . 一种基于神经网络的机动目标轨迹预测方法 [J ] . 青岛理工大学学报 , 2006 , 27 ( 5 ): 108 - 111 .

MA G B , ZHANG N . Method based on neural network in predicting the track of maneuvering target [J ] . Journal of Qingdao University of Technology , 2006 , 27 ( 5 ): 108 - 111 .

高天航 , 徐力 , 靳廉洁 , 等 . 考虑航艏向与数据变化差异的船舶轨迹预测 [J ] . 交通运输系统工程与信息 , 2021 , 21 ( 1 ): 90 - 94 .

GAO T H , XU L , JIN L J , et al . Vessel trajectory prediction considering the difference between heading and data changes [J ] . Journal of Transportation Systems Engineering and Information Technology , 2021 , 21 ( 1 ): 90 - 94 .

刘创 , 梁军 . 基于注意力机制的车辆运动轨迹预测 [J ] . 浙江大学学报(工学版) , 2020 , 54 ( 6 ): 1156 - 1163 .

LIU C , LIANG J . Vehicle motion trajectory prediction based on attention mechanism [J ] . Journal of Zhejiang University (Engineering Science) , 2020 , 54 ( 6 ): 1156 - 1163 .

GUO X J , ZHU Q . A trafc ow forecasting model based on BP neural network [C ] // Proceedings of the 2009 2nd International Conference on Power Electronics and Intelligent Transportation System (PEITS) .[S.l.:s.n. ] , 2009 : 311 - 314 .

AKIYAMA T , INOKUCHI H . Long term estimation of trafc demand on urban expressway by neural networks [C ] // Proceedings of the 2014 Joint 7th International Conference on Soft Computing and Intelligent Systems (SCIS) and 15th International Symposium on Advanced Intelligent Systems (ISIS) . Piscataway:IEEE Press , 2014 : 185 - 189 .

刘莹 . 基于深度学习的轨迹预测 [D ] . 成都:电子科技大学 , 2019 .

LIU Y . Trajectory prediction based on deep learning [D ] . Chengdu:University of Electronic Science and Technology of China , 2019 .

VALSAMIS A , TSERPES K , ZISSIS D , et al . Employing traditional machine learning algorithms for big data streams analysis:the case of object trajectory prediction [J ] . Journal of Systems and Software , 2017 , 127 : 249 - 257 .

MIN K , KIM D , PARK J , et al . RNN-based path prediction of obstacle vehicles with deep ensemble [J ] . IEEE Transactions on Vehicular Technology , 2019 , 68 ( 10 ): 10252 - 10256 .

ALTCHÉ F , DE LA FORTELLE A , . An LSTM network for highway trajectory prediction [C ] // Proceedings of the 2017 IEEE 20th International Conference on Intelligent Transportation Systems . Piscataway:IEEE Press , 2017 : 353 - 359 .

XIN L , WANG P , CHAN C Y , et al . Intention-aware long horizon trajectory prediction of surrounding vehicles using dual LSTM networks [C ] // Proceedings of the 2018 21st International Conference on Intelligent Transportation Systems . Piscataway:IEEE Press , 2018 : 1441 - 1446 .

ALAHI A , GOEL K , RAMANATHAN V , et al . Social LSTM:human trajectory prediction in crowded spaces [C ] // Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition . Piscataway:IEEE Press , 2016 : 961 - 971 .

张志远 , 倪国新 , 徐艳国 . 轨迹预测技术的现状及发展综述 [J ] . 电子测量技术 , 2020 , 43 ( 13 ): 111 - 116 .

ZHANG Z Y , NI G X , XU Y G . Review of the status and development of trajectory prediction technology [J ] . Electronic Measurement Technology , 2020 , 43 ( 13 ): 111 - 116 .

谢新连 , 陈紫薇 , 魏照坤 , 等 . 基于极限学习机的船舶航行行为预测 [J ] . 重庆交通大学学报(自然科学版) , 2019 , 38 ( 8 ): 7 - 12 , 123 .

XIE X L , CHEN Z W , WEI Z K , et al . Ship navigation behavior prediction based on extreme learning machine [J ] . Journal of Chongqing Jiaotong University (Natural Science) , 2019 , 38 ( 8 ): 7 - 12 , 123 .

谭紫阳 , 高忠文 , 邓宇 . 基于改进极限学习机和深度神经网络融合的车辆轨迹长期预测 [J ] . 汽车技术 , 2020 ( 11 ): 16 - 20 .

TAN Z Y , GAO Z W , DENG Y . The long term prediction of vehicle trajectory based on improved extreme learning machine and deep neural network fusion [J ] . Automotive Technology , 2020 ( 11 ): 16 - 20 .

GUPTA A , JOHNSON J , LI F F , et al . Social GAN:socially acceptable trajectories with generative adversarial networks [C ] // Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Piscataway:IEEE Press , 2018 : 2255 - 2264 .

SADEGHIAN A , KOSARAJU V , SADEGHIAN A , et al . SoPhie:an attentive GAN for predicting paths compliant to social and physical constraints [C ] // Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Piscataway:IEEE Press , 2019 : 1349 - 1358 .

石庆研 , 岳聚财 , 韩萍 , 等 . 基于 LSTM-ARIMA 模型的短期航班飞行轨迹预测 [J ] . 信号处理 , 2019 , 35 ( 12 ): 2000 - 2009 .

SHI Q Y , YUE J C , HAN P , et al . Short-term flight trajectory prediction based on LSTM-ARIMA model [J ] . Journal of Signal Processing , 2019 , 35 ( 12 ): 2000 - 2009 .

PECHER P , HUNTER M , FUJIMOTO R . Data-driven vehicle trajectory prediction [C ] // Proceedings of the 2016 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation . New York:ACM Press , 2016 : 13 - 22 .

张凯 . 基于海量出租车轨迹数据的学习与预测 [D ] . 西安:长安大学 , 2019 .

ZHANG K . Learning and prediction over mass taxi trajectory data [D ] . Xi’an:Chang’an University , 2019 .

周于涛 , 吴华意 , 成洪权 , 等 . 结合自注意力机制和结伴行为特征的行人轨迹预测模型 [J ] . 武汉大学学报·信息科学版 , 2020 , 45 ( 12 ): 1989 - 1996 .

ZHOU Y T , WU H Y , CHENG H Q , et al . Pedestrian trajectory prediction model based on self-attention mechanism and group behavior characteristics [J ] . Geomatics and Information Science of Wuhan University , 2020 , 45 ( 12 ): 1989 - 1996 .

关克平 , 江靖楠 , 吴天裕 , 等 . 基于OSG和Bullet的船舶碰撞响应三维仿真 [J ] . 上海海事大学学报 , 2014 , 35 ( 4 ): 1 - 6 .

GUAN K P , JIANG J N , WU T Y , et al . Three-dimensional simulation on collision response of ships based on OSG and Bullet [J ] . Journal of Shanghai Maritime University , 2014 , 35 ( 4 ): 1 - 6 .

HOUENOU A , BONNIFAIT P , CHERFAOUI V , et al . Vehicle trajectory prediction based on motion model and maneuver recognition [C ] // Proceedings of the 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems . Piscataway:IEEE Press , 2013 : 4363 - 4369 .

HELBING D , MOLNÁR P , . Social force model for pedestrian dynamics [J ] . Physical Review E , 1995 , 51 ( 5 ): 4282 .

BAHDANAU D , CHO K , BENGIO Y . Neural machine translation by jointly learning to align and translate [J ] . Computer Science,arXiv preprint,2014,arXiv:1409.0473 .

李琳辉 , 周彬 , 任威威 , 等 . 行人轨迹预测方法综述 [J ] . 智能科学与技术学报 , 2021 :已录用.

LI L H , ZHOU B , REN W W , et al . Review of pedestrian trajectory prediction methods [J ] . Chinese Journal of Intelligent Science and Technology , 2021 :accepted.

张鹏鹏 , 房芳 , 马旭东 . 基于注意力机制的生成式对抗轨迹预测方法 [J ] . 工业控制计算机 , 2021 , 34 ( 4 ): 63 - 65 .

ZHANG P P , FANG F , MA X D . Pedestrian trajectory prediction method with generative adversarial network based on attention mechanism [J ] . Industrial Control Computer , 2021 , 34 ( 4 ): 63 - 65 .

李琳辉 , 周彬 , 连静 , 等 . 基于社会注意力机制的行人轨迹预测方法研究 [J ] . 通信学报 , 2020 , 41 ( 6 ): 175 - 183 .

LI L H , ZHOU B , LIAN J , et al . Research on pedestrian trajectory prediction method based on social attention mechanism [J ] . Journal on Communications , 2020 , 41 ( 6 ): 175 - 183 .

刘璐 . 基于意图识别的骑车人轨迹预测方法研究 [D ] . 北京:清华大学 , 2019 .

LIU L . Intent-based trajectory prediction approach of cyclists [D ] . Beijing:Tsinghua University , 2019 .

丁洁云 , 党睿娜 , 王建强 , 等 . 驾驶人换道决策分析及意图识别算法设计 [J ] . 清华大学学报(自然科学版) , 2015 , 55 ( 7 ): 769 - 774 .

DING J Y , DANG R N , WANG J Q , et al . Driver lane change decision analysis and intention recognition algorithm [J ] . Journal of Tsinghua University (Science and Technology) , 2015 , 55 ( 7 ): 769 - 774 .

HE G , LI X , LYU Y , et al . Probabilistic intention prediction and trajectory generation based on dynamic Bayesian networks [C ] // Proceedings of the 2019 Chinese Automation Congress . Piscataway:IEEE Press , 2019 : 2646 - 2651 .

刘志强 , 吴雪刚 , 倪捷 , 等 . 基于HMM和SVM级联算法的驾驶意图识别 [J ] . 汽车工程 , 2018 , 40 ( 7 ): 858 - 864 .

LIU Z Q , WU X G , NI J , et al . Driving intention recognition based on HMM and SVM cascade algorithm [J ] . Automotive Engineering , 2018 , 40 ( 7 ): 858 - 864 .

季学武 , 费聪 , 何祥坤 , 等 . 基于 LSTM 网络的驾驶意图识别及车辆轨迹预测 [J ] . 中国公路学报 , 2019 , 32 ( 6 ): 34 - 42 .

JI X W , FEI C , HE X K , et al . Intention recognition and trajectory prediction for vehicles using LSTM network [J ] . China Journal of Highway and Transport , 2019 , 32 ( 6 ): 34 - 42 .

王畅 . 基于隐形马尔可夫模型的驾驶员意图辨识方法研究 [D ] . 长春:吉林大学 , 2011 .

WANG C . Research on driver intention recognition method based on hidden Markov model [D ] . Changchun:Jilin University , 2011 .

郝慎学 . 城市道路交通系统的分布式优化控制策略研究 [D ] . 济南:山东大学 , 2019 .

HAO S X . Research on distributed optimal control strategy of urban road traffic system [D ] . Jinan:Shandong University , 2019 .

LI S C , LIANG M H , WU X Y , et al . AIS-based vessel trajectory reconstruction with U-Net convolutional networks [C ] // Proceedings of the 2020 IEEE 5th International Conference on Cloud Computing and Big Data Analytics . Piscataway:IEEE Press , 2020 .

CHEN X Q , LING J , YANG Y S , et al . Ship trajectory reconstruction from AIS sensory data via data quality control and prediction [J ] . Mathematical Problems in Engineering , 2020 : 1 - 9 .

BOMBERGER N A , RHODES B J , SEIBERT M , et al . Associative learning of vessel motion patterns for maritime situation awareness [C ] // Proceedings of the IEEE 2006 9th International Conference on Information Fusion . Piscataway:IEEE Press , 2006 : 1 - 8 .

RHODES B J , BOMBERGER N A , ZANDIPOUR M . Probabilistic associative learning of vessel motion patterns at multiple spatial scales for maritime situation awareness [C ] // Proceedings of the IEEE 2007 10th International Conference on Information Fusion . Piscataway:IEEE Press , 2007 : 1 - 8 .

FOSSEN T I . Guidance and control of ocean vehicles [M ] . Weinheim : Wiley , 1994 : 455 - 47 .

高曙 , 刘甜甜 , 初秀民 , 等 . 船舶异常行为研究进展及发展趋势 [J ] . 中国航海 , 2017 , 40 ( 2 ): 38 - 43 .

GAO S , LIU T T , CHU X M , et al . Status and research trends on abnormal ship behavior [J ] . Navigation of China , 2017 , 40 ( 2 ): 38 - 43 .

ZHAO L B , SHI G Y . Maritime anomaly detection using density-based clustering and recurrent neural network [J ] . Journal of Navigation , 2019 , 72 ( 4 ): 894 - 916 .

葛艳 , 王健 , 孟友新 , 等 . 车辆导航动态路径规划的研究进展 [J ] . 公路交通科技 , 2010 , 27 ( 11 ): 113 - 117 .

GE Y , WANG J , MENG Y X , et al . Research progress on dynamic route planning of vehicle navigation [J ] . Journal of Highway and Transportation Research and Development , 2010 , 27 ( 11 ): 113 - 117 .

DE FABRITIIS C , RAGONA R , VALENTI G . Traffic estimation and prediction based on real time floating car data [C ] // Proceedings of the 2018 11th International IEEE Conference on Intelligent Transportation Systems . Piscataway:IEEE Press , 2008 : 197 - 203 .

许涛 . 基于海量出租车轨迹数据的旅行时间预测 [D ] . 上海:华东师范大学 , 2017 .

XU T . Travel time prediction based on massive taxi trajectory data [D ] . Shanghai:East China Normal University , 2017 .

范静宏 . 基于大数据技术的船舶定位导航和航迹预测 [J ] . 舰船科学技术 , 2018 , 40 ( 14 ): 31 - 33 .

FAN J H . Research on ship positioning navigation and track prediction based on big data technology [J ] . Ship Science and Technology , 2018 , 40 ( 14 ): 31 - 33 .

应文健 . 自主车导航中提高目标行为预测能力的新方法 [J ] . 计算机测量与控制 , 2012 , 20 ( 8 ): 2048 - 2051 .

YING W J . Improved objects motion predication for autonomous vehicle navigation [J ] . Computer Measurement ＆ Control , 2012 , 20 ( 8 ): 2048 - 2051 .

DAI J , YANG B , GUO C J , et al . Personalized route recommendation using big trajectory data [C ] // Proceedings of the 2015 IEEE 31st International Conference on Data Engineering . Piscataway:IEEE Press , 2015 : 543 - 554 .

温美玲 , 路鹏远 , 蔡林 , 等 . 基于轨迹大数据的交通拥堵评估和预测 [J ] . 数字制造科学 , 2021 , 19 ( 1 ): 77 - 80 .

WEN M L , LU P Y , CAI L , et al . Traffic congestion assessment and prediction based on trajectory big data [J ] . Digital Manufacture Science , 2021 , 19 ( 1 ): 77 - 80 .

易先锋 . 基于车辆轨迹的个性化路径导航与行程时间预测 [D ] . 广州:华南理工大学 , 2018 .

YI X F . Personalized route navigation and travel time prediction based on vehicle trajectories [D ] . Guangzhou:South China University of Technology , 2018 .

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