

浏览全部资源
扫码关注微信
1. 厦门大学航空航天学院自动化系,福建 厦门 361100
2. 中国科学院自动化研究所复杂系统管理与控制国家重点实验室,北京 100190
3. 青岛智能产业技术研究院平行智能创新中心,山东 青岛 266114
4. 美国印第安纳大学-普渡大学印第安纳波利斯分校电子与计算机工程系,美国 印第安纳州 IN 46204
Published Online:2022-03,
Published:15 March 2022
移动端阅览
Yuanwen CHEN, Xiao WANG, Lingxi LI, et al. Traffic situational awareness research and development enhanced by social media data: the state of the art and prospects[J]. Chinese journal of intelligent science and technology, 2022, 4(1): 1-13.
Yuanwen CHEN, Xiao WANG, Lingxi LI, et al. Traffic situational awareness research and development enhanced by social media data: the state of the art and prospects[J]. Chinese journal of intelligent science and technology, 2022, 4(1): 1-13. DOI: 10.11959/j.issn.2096-6652.202220.
交通态势感知是智能交通系统的重要研究方向。已有研究大多关注如何使用物理传感器感知当下交通态势并预测未来交通状况。然而,物理传感器性能易因天气影响、电磁干扰、能源限制等问题出现不稳定或失效情况,导致其采集的数据稀疏或缺失,使其对交通态势感知滞后且不准确。社会媒体数据为及时感知完善的交通态势信息提供了新的增强方式。面向当下异常交通情况频发的城市交通管控现状,社会传感与物理传感数据互为补充,可进一步满足城市交通高效管理需求。基于此,对基于社会媒体数据的交通事件检测和交通状况预测工作展开分析研究,探讨社会媒体数据增强的交通态势感知研究工作如何为交通管理部门提供决策支持,以合理规划、引导交通,缓解交通拥堵,最后提出社会媒体数据增强的交通态势感知还需进一步探索的方向。
Traffic situational awareness is an important research direction of intelligent transportation systems.Most of the existing research focused on how to use physical sensors to perceive the current traffic situation and predict the future traffic state.However
the performance of physical sensors is prone to instability or failure due to adverse weather
electromagnetic interference
energy limitation and other problems
resulting in sparse or missing collected data
which makes the perception of traffic situation lagging and inaccurate.Social media data provides a new and enhanced way of perceiving comprehensive traffic situation information in a timely manner.Facing with the current traffic situation where sudden abnormal traffic events occur frequently
social sensing and physical sensing data can complement with each other to further improve the efficiency of urban traffic management.The related work of traffic event detection and traffic state prediction enhancing based on social media data were analyzed
and how those research works provide decision support for the traffic management departments to plan and guide traffic reasonably and alleviate traffic congestion were explored.Finally
some future research directions of traffic situational awareness enhanced by social media data were proposed.
高德地图 . 2021年度中国主要城市交通分析报告 [R ] . 2021 .
Amap . Traffic analysis report of China’s major cities in 2021 [R ] . 2021 .
高德地图 . 2020年度中国主要城市交通分析报告 [R ] . 2020 .
Amap . Traffic analysis report of China’s major cities in 2020 [R ] . 2020 .
高德地图 . 2019年度中国主要城市交通分析报告 [R ] . 2019 .
Amap . Traffic analysis report of China’s major cities in 2019 [R ] 2019 .
高德地图 . 2018年度中国主要城市交通分析报告 [R ] . 2018 .
Amap . Traffic analysis report of China’s major cities in 2018 [R ] 2018 .
高德地图 . 2017年度中国主要城市交通分析报告 [R ] . 2017 .
Amap . Traffic Analysis report of China’s Major Cities in 2017 [R ] 2017 .
SHEN D Y , ZHANG L F , CAO J P , et al . Forecasting citywide traffic congestion based on social media [J ] . Wireless Personal Communications , 2018 , 103 ( 1 ): 1037 - 1057 .
YIN J , LAMPERT A , CAMERON M , et al . Using social media to enhance emergency situation awareness [J ] . IEEE Intelligent Systems , 2012 , 27 ( 6 ): 52 - 59 .
WANG F Y . Scanning the issue [J ] . IEEE Transactions on Intelligent Transportation Systems , 2014 , 15 ( 3 ): 909 - 914 .
WANG D , AMIN M T , LI S , et al . Using humans as sensors:an estimation-theoretic perspective [C ] // Proceedings of the 13th International Symposium on Information Processing in Sensor Networks . Piscataway:IEEE Press , 2014 : 35 - 46 .
WANG D , KAPLAN L , LE H , et al . On truth discovery in social sensing:a maximum likelihood estimation approach [C ] // Proceedings of 2012 ACM/IEEE 11th International Conference on Information Processing in Sensor Networks . Piscataway:IEEE Press , 2012 : 233 - 244 .
王飞跃 . 万维社会媒体在防灾应急中的作用 [J ] . 科技导报 , 2008 , 26 ( 10 ): 30 - 31 .
WANG F Y . Web social media in disaster reduction and emergence management [J ] . Science & Technology Review , 2008 , 26 ( 10 ): 30 - 31 .
王晖 , 姜志宏 , 李沛 , 等 . 基于 Web 社会媒体的社会传感器网络 [C ] // 第二届全国社会计算会议 . 北京:中国自动化学会 , 2010 : 1 - 5 .
WANG H , JIANG Z H , LI P , et al . Social sensor network based on Web social media [C ] // Proceedings of the 2nd Chinese Conference on Social Computing . Beijing:Chinese Association of Automation , 2010 : 1 - 5 .
SHETH A . Citizen sensing,social signals,and enriching human experience [J ] . IEEE Internet Computing , 2009 , 13 ( 4 ): 87 - 92 .
王飞跃 . 社会信号处理与分析的基本框架:从社会传感网络到计算辩证解析方法 [J ] . 中国科学:信息科学 , 2013 , 43 ( 12 ): 1598 - 1611 .
WANG F Y . A framework for social signal processing and analysis:from social sensing networks to computational dialectical analytics [J ] . Scientia Sinica (Informationis) , 2013 , 43 ( 12 ): 1598 - 1611 .
WANG X , ZENG K , ZHAO X L , et al . Using Web data to enhance traffic situation awareness [C ] // Proceedings of the 17th International IEEE Conference on Intelligent Transportation Systems . Piscataway:IEEE Press , 2014 : 195 - 199 .
ZHENG Z H , WANG C C , WANG P , et al . Framework for fusing traffic information from social and physical transportation data [J ] . PLoS One , 2018 , 13 ( 8 ): e0201531 .
ZENG K , LIU W L , WANG X , et al . Traffic congestion and social media in China [J ] . IEEE Intelligent Systems , 2013 , 28 ( 1 ): 72 - 77 .
WANG S Z , ZHANG X M , CAO J P , et al . Computing urban traffic congestions by incorporating sparse GPS probe data and social media data [J ] . ACM Transactions on Information Systems , 2017 , 35 ( 4 ): 1 - 30 .
NALLAPERUMA D , NAWARATNE R , BANDARAGODA T , et al . Online incremental machine learning platform for big data-driven smart traffic management [J ] . IEEE Transactions on Intelligent Transportation Systems , 2019 , 20 ( 12 ): 4679 - 4690 .
RASHID M T , ZHANG D , WANG D . DASC:Towards a road damage-aware social-media-driven car sensing framework for disaster response applications [J ] . Pervasive and Mobile Computing , 2020 ,67:101207.
ZHANG D Y , HAN R G , WANG D , et al . On robust truth discovery in sparse social media sensing [C ] // Proceedings of 2016 IEEE International Conference on Big Data . Piscataway:IEEE Press , 2016 : 1076 - 1081 .
XU S S , LI S N , WEN R . Sensing and detecting traffic events using geosocial media data:a review [J ] . Computers,Environment and Urban Systems , 2018 , 72 : 146 - 160 .
ZHANG S , TANG J J , WANG H , et al . Enhancing traffic incident detection by using spatial point pattern analysis on social media [J ] . Transportation Research Record:Journal of the Transportation Research Board , 2015 , 2528 ( 1 ): 69 - 77 .
SINNOTT R O , GONG Y K , CHEN S P , et al . Urban traffic analysis using social media data on the cloud [C ] // Proceedings of 2018 IEEE/ACM International Conference on Utility and Cloud Computing Companion . Piscataway:IEEE Press , 2018 : 134 - 141 .
STEIGER E , RESCH B , DE ALBUQUERQUE J P , et al . Mining and correlating traffic events from human sensor observations with official transport data using self-organizing-maps [J ] . Transportation Research Part C:Emerging Technologies , 2016 , 73 : 91 - 104 .
ZHANG Y , LU Y W , ZHANG D , et al . RiskSens:a multi-view learning approach to identifying risky traffic locations in intelligent transportation systems using social and remote sensing [C ] // Proceedings of 2018 IEEE International Conference on Big Data . Piscataway:IEEE Press , 2018 : 1544 - 1553 .
WANG S Z , ZHANG X M , LI F X , et al . Efficient traffic estimation with multi-sourced data by parallel coupled hidden Markov model [J ] . IEEE Transactions on Intelligent Transportation Systems , 2019 , 20 ( 8 ): 3010 - 3023 .
CHEN P T , CHEN F , QIAN Z . Road traffic congestion monitoring in social media with hinge-loss Markov random fields [C ] // Proceedings of 2014 IEEE International Conference on Data Mining . Piscataway:IEEE Press , 2014 : 80 - 89 .
YAO W R , QIAN S A . From Twitter to traffic predictor:next-day morning traffic prediction using social media data [J ] . Transportation Research Part C:Emerging Technologies , 2021 ,124:102938.
郑治豪 , 吴文兵 , 陈鑫 , 等 . 基于社交媒体大数据的交通感知分析系统 [J ] . 自动化学报 , 2018 , 44 ( 4 ): 656 - 666 .
ZHENG Z H , WU W B , CHEN X , et al . A traffic sensing and analyzing system using social media data [J ] . Acta Automatica Sinica , 2018 , 44 ( 4 ): 656 - 666 .
ZHANG Z H , HE Q , GAO J , et al . A deep learning approach for detecting traffic accidents from social media data [J ] . Transportation Research Part C:Emerging Technologies , 2018 , 86 : 580 - 596 .
FU K Q , LU C T , NUNE R , et al . Steds:social media based transportation event detection with text summarization [C ] // Proceedings of 2015 IEEE 18th International Conference on Intelligent Transportation Systems . Piscataway:IEEE Press , 2015 : 1952 - 1957 .
GU Y M , QIAN Z S , CHEN F . From Twitter to detector:real-time traffic incident detection using social media data [J ] . Transportation Research Part C:Emerging Technologies , 2016 , 67 : 321 - 342 .
DAVIS C A , VAROL O , FERRARA E , et al . BotOrNot:a system to evaluate social bots [C ] // Proceedings of the 25th International Conference Companion on World Wide Web . New York:ACM Press , 2016 : 273 - 274 .
CHE W X , LI Z H , LIU T . LTP:a Chinese language technology plat form [C ] // Proceedings of Coling 2010:Demonstrations .[S.l.:s.n. ] , 2010 : 13 - 16 .
PORTER M F . An algorithm for suffix stripping [J ] . Program , 1980 , 14 ( 3 ): 130 - 137 .
D'ANDREA E , DUCANGE P , LAZZERINI B , et al . Real-time detection of traffic from twitter stream analysis [J ] . IEEE Transactions on Intelligent Transportation Systems , 2015 , 16 ( 4 ): 2269 - 2283 .
MIKOLOV T , CHEN K , CORRADO G , et al . Efficient estimation of word representations in vector space [J ] . arXiv preprint , 2013 ,arXiv:1301.3781.
AGRAWAL R , SRIKANT R . Fast algorithms for mining association rules [C ] // Proceedings of Very Large Data Bases Conferences .[S.l.:s.n. ] , 1994 : 487 - 499 .
KHAN S M , CHOWDHURY M , NGO L B , et al . Multi-class twitter data categorization and geocoding with a novel computing framework [J ] . Cities , 2020 ,96:102410.
CRAMÉR H . Mathematical methods of statistics (PMS-9) [M ] .[S.l ] : Princeton University Press , 1946 .
GUTIÉRREZ C , FIGUERIAS P , OLIVEIRA P , et al . Twitter mining for traffic events detection [C ] // Proceedings of 2015 Science and Information Conference . Piscataway:IEEE Press , 2015 : 371 - 378 .
CUI J , FU R , DONG C H , et al . Extraction of traffic information from social media interactions:methods and experiments [C ] // Proceedings of the 17th International IEEE Conference on Intelligent Transportation Systems . Piscataway:IEEE Press , 2014 : 1549 - 1554 .
CHEN Y Y , LYU Y S , WANG X , et al . Detecting traffic information from social media texts with deep learning approaches [J ] . IEEE Transactions on Intelligent Transportation Systems , 2019 , 20 ( 8 ): 3049 - 3058 .
WANG D , AL-RUBAIE A , DAVIES J , et al . Real time road traffic monitoring alert based on incremental learning from tweets [C ] // Proceedings of 2014 IEEE Symposium on Evolving and Autonomous Learning Systems . Piscataway:IEEE Press , 2014 : 50 - 57 .
JAIN A K , KUMAR A , GARG J , et al . TraffTrend:real time traffic updates and traffic trends using social media analytics [C ] // Proceedings of the 2nd IKDD Conference on Data Sciences .[S.l.:s.n. ] , 2015 : 1 - 4 .
FRANK E , WITTEN I . Generating accurate rule sets without global optimization [C ] // Proceedings of the 15th International Conference on Machine Learning .[S.l.:s.n. ] , 1998 .
LU H , SHI K Z , ZHU Y F , et al . Sensing urban transportation events from multi-channel social signals with the Word2Vec fusion model [J ] . Sensors (Basel,Switzerland) , 2018 , 18 ( 12 ): 4093 .
ALKOUZ B , AL AGHBARI Z . SNSJam:road traffic analysis and prediction by fusing data from multiple social networks [J ] . Information Processing & Management , 2020 , 57 ( 1 ): 102139 .
TEJASWIN P , KUMAR R , GUPTA S . Tweeting traffic:analyzing Twitter for generating real-time city traffic insights and predictions [C ] // Proceedings of the 2nd IKDD Conference on Data Sciences . New York:ACM Press , 2015 .
SCARPONCINI P , . Generalized model for linear referencing [C ] // Proceedings of the 7th ACM International Symposium on Advances in Geographic Information Systems . New York:ACM Press , 1999 .
GELERNTER J , BALAJI S . An algorithm for local geoparsing of microtext [J ] . GeoInformatica , 2013 , 17 ( 4 ): 635 - 667 .
ERKAN G , RADEV D R . LexRank:graph-based lexical centrality as salience in text summarization [J ] . Journal of Artificial Intelligence Research , 2004 , 22 : 457 - 479 .
PAN B , ZHENG Y , WILKIE D , et al . Crowd sensing of traffic anomalies based on human mobility and social media [C ] // Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems . New York:ACM Press , 2013 : 344 - 353 .
WANG F Y . Scanning the issue [J ] . IEEE Transactions on Intelligent Transportation Systems , 2015 , 16 ( 1 ): 1 - 8 .
WANG X , ZHENG X H , ZHANG Q P , et al . Crowdsourcing in ITS:the state of the work and the networking [J ] . IEEE Transactions on Intelligent Transportation Systems , 2016 , 17 ( 6 ): 1596 - 1605 .
YIN X Y , WU G Z , WEI J Z , et al . Deep learning on traffic prediction:methods,analysis and future directions [J ] . IEEE Transactions on Intelligent Transportation Systems , 2021 , 4840 ( 99 ): 1 - 17 .
LIU S Y , YUE Y S , KRISHNAN R . Non-myopic adaptive route planning in uncertain congestion environments [J ] . IEEE Transactions on Knowledge and Data Engineering , 2015 , 27 ( 9 ): 2438 - 2451 .
ALEMAZKOOR N , MEIDANI H . A data-driven multi-fidelity approach for traffic state estimation using data from multiple sources [J ] . IEEE Access , 2021 , 9 : 78128 - 78137 .
GHOSH B , BASU B , O'MAHONY M , . Multivariate short-term traffic flow forecasting using time-series analysis [J ] . IEEE Transactions on Intelligent Transportation Systems , 2009 , 10 ( 2 ): 246 - 254 .
HUANG Y P , QIAN L P , FENG A Q , et al . RFID data-driven vehicle speed prediction via adaptive extended Kalman filter [J ] . Sensors (Basel,Switzerland) , 2018 , 18 ( 9 ): 2787 .
LU Z L , LYU W F , CAO Y B , et al . LSTM variants meet graph neural networks for road speed prediction [J ] . Neurocomputing , 2020 , 400 : 34 - 45 .
JIA H W , LUO H Y , WANG H , et al . ADST:forecasting metro flow using attention-based deep spatial-temporal networks with multi-task learning [J ] . Sensors , 2020 , 20 ( 16 ): 4574 .
PENG H , WANG H F , DU B W , et al . Spatial temporal incidence dynamic graph neural networks for traffic flow forecasting [J ] . Information Sciences , 2020 , 521 : 277 - 290 .
DU B W , PENG H , WANG S Z , et al . Deep irregular convolutional residual LSTM for urban traffic passenger flows prediction [J ] . IEEE Transactions on Intelligent Transportation Systems , 2020 , 21 ( 3 ): 972 - 985 .
GEORGE S , SANTRA A K . An improved long short-term memory networks with Takagi-Sugeno fuzzy for traffic speed prediction considering abnormal traffic situation [J ] . Computational Intelligence , 2020 , 36 ( 3 ): 964 - 993 .
YANG X , YUAN Y , LIU Z Y . Short-term traffic speed prediction of urban road with multi-source data [J ] . IEEE Access , 2020 , 8 : 87541 - 87551 .
ALI A , ZHU Y M , ZAKARYA M . Exploiting dynamic spatio-temporal correlations for citywide traffic flow prediction using attention based neural networks [J ] . Information Sciences , 2021 , 577 : 852 - 870 .
YANG X , ZHU Q , LI P H , et al . Fine-grained predicting urban crowd flows with adaptive spatio-temporal graph convolutional network [J ] . Neurocomputing , 2021 , 446 : 95 - 105 .
ZHANG J B , ZHENG Y , SUN J K , et al . Flow prediction in spatiotemporal networks based on multitask deep learning [J ] . IEEE Transactions on Knowledge and Data Engineering , 2020 , 32 ( 3 ): 468 - 478 .
LI W , TAO W , QIU J Y , et al . Densely connected convolutional networks with attention LSTM for crowd flows prediction [J ] . IEEE Access , 2019 , 7 : 140488 - 140498 .
LYU Y S , DUAN Y J , KANG W W , et al . Traffic flow prediction with big data:a deep learning approach [J ] . IEEE Transactions on Intelligent Transportation Systems , 2015 , 16 ( 2 ): 865 - 873 .
LIN L , LI J X , CHEN F , et al . Road traffic speed prediction:a probabilistic model fusing multi-source data [J ] . IEEE Transactions on Knowledge and Data Engineering , 2018 , 30 ( 7 ): 1310 - 1323 .
LIU S Y , QU Q . Dynamic collective routing using crowdsourcing data [J ] . Transportation Research Part B:Methodological , 2016 , 93 : 450 - 469 .
ZHOU T , GAO L X , NI D H . Road traffic prediction by incorporating online information [C ] // Proceedings of the 23rd International Conference on World Wide Web . New York:ACM Press , 2014 : 1235 - 1240 .
FRIEDMAN J H . On bias,variance,0/1—loss,and the curse-ofdimensionality [J ] . Data mining and knowledge discovery , 1997 , 1 ( 1 ): 55 - 77 .
JORDAN M I , GHAHRAMANI Z , JAAKKOLA T S , et al . An introduction to variational methods for graphical models [J ] . Machine Learning , 1999 , 37 ( 2 ): 183 - 233 .
PARSONS S . Probabilistic graphical models:principles and techniques [J ] . The Knowledge Engineering Review , 2011 , 26 ( 2 ): 237 - 238 .
PEARL J . Reverend Bayes on inference engines:a distributed hierarchical approach [M ] .[S.l.:s.n. ] , 1982 .
XUE G , LIU S F , REN L , et al . Forecasting the subway passenger flow under event occurrences with multivariate disturbances [J ] . Expert Systems With Applications , 2022 ,188:116057.
ZHANG Z H , NI M , HE Q , et al . Exploratory study on correlation between twitter concentration and traffic surges [J ] . Transportation Research Record:Journal of the Transportation Research Board , 2016 , 2553 ( 1 ): 90 - 98 .
NI M , HE Q , GAO J . Forecasting the subway passenger flow under event occurrences with social media [J ] . IEEE Transactions on Intelligent Transportation Systems , 2017 , 18 ( 6 ): 1623 - 1632 .
NI M , HE Q , GAO J . Using social media to predict traffic flow under special event conditions [C ] // Proceedings of the 93rd Annual Meeting of Transportation Research Board .[S.l.:s.n. ] , 2014 .
GODBOLE N , SRINIVASAIAH M , SKIENA S . Large-scale sentiment analysis for news and blogs (system demonstration) [J ] . Icwsm , 2007 , 7 ( 21 ): 219 - 222 .
NAGARAJAN M , GAMON M . Automating analysis of social media communication:insights from CMDA [C ] // Proceedings of the Workshop on Languages in Social Media . Oregon:Association for Computational Linguistics , 2011 :1.
ESSIEN A , PETROUNIAS I , SAMPAIO P , et al . A deep-learning model for urban traffic flow prediction with traffic events mined from twitter [J ] . World Wide Web , 2021 , 24 ( 4 ): 1345 - 1368 .
ESSIEN A , PETROUNIAS I , SAMPAIO P , et al . Improving urban traffic speed prediction using data source fusion and deep learning [C ] // Proceedings of 2019 IEEE International Conference on Big Data and Smart Computing . Piscataway:IEEE Press , 2019 : 1 - 8 .
LIU X Y , KONG X N , LI Y H . Collective traffic prediction with partially observed traffic history using location-based social media [C ] // Proceedings of the 25th ACM International on Conference on Information and Knowledge Management . New York:ACM Press , 2016 : 2179 - 2184 .
WONGCHAROEN S , SENIVONGSE T . Twitter analysis of road traffic congestion severity estimation [C ] // Proceedings of 2016 13th International Joint Conference on Computer Science and Software Engineering . Piscataway:IEEE Press , 2016 : 1 - 6 .
CUI Y , MENG C S , HE Q , et al . Forecasting current and next trip purpose with social media data and Google places [J ] . Transportation Research Part C:Emerging Technologies , 2018 , 97 : 159 - 174 .
ROY K C , HASAN S , CULOTTA A , et al . Predicting traffic demand during hurricane evacuation using real-time data from transportation systems and social media [J ] . Transportation Research Part C:Emerging Technologies , 2021 ,131:103339.
ALTMANN A , TOLOŞI L , SANDER O , et al . Permutation importance:a corrected feature importance measure [J ] . Bioinformatics , 2010 , 26 ( 10 ): 1340 - 1347 .
GOODFELLOW I , BENGIO Y , COURVILLE A . Deep learning [M ] .[S.l. ] : MIT press , 2016 .
HASHEMI H , ABDELGHANY K . Real-time traffic network state prediction for proactive traffic management [J ] . Transportation Research Record:Journal of the Transportation Research Board , 2015 , 2491 ( 1 ): 22 - 31 .
ABDELGHANY K , HASHEMI H , KHODAYAR M E . A decision support system for proactive-robust traffic network management [J ] . IEEE Transactions on Intelligent Transportation Systems , 2019 , 20 ( 1 ): 297 - 312 .
HASHEMI H , ABDELGHANY K . End-to-end deep learning methodology for real-time traffic network management [J ] . Computer-Aided Civil and Infrastructure Engineering , 2018 , 33 ( 10 ): 849 - 863 .
WANG D , AL AMIN M T , ABDELZAHER T , et al . Provenanceassisted classification in social networks [J ] . IEEE Journal of Selected Topics in Signal Processing , 2014 , 8 ( 4 ): 624 - 637 .
WANG D , ABDELZAHER T , KAPLAN L . Surrogate mobile sensing [J ] . IEEE Communications Magazine , 2014 , 52 ( 8 ): 36 - 41 .
ZHANG D , VANCE N , WANG D . When social sensing meets edge computing:vision and challenges [C ] // Proceedings of 2019 28th International Conference on Computer Communication and Networks . Piscataway:IEEE Press , 2019 : 1 - 9 .
0
Views
973
下载量
0
CSCD
Publicity Resources
Related Articles
Related Author
Related Institution
京公网安备11010802024621