| [1] | Lau R Y K. Toward a social sensor based framework for intelligent transportation. In: Proceeding of 2017 IEEE 18th International Symposium on A World of Wireless, Mobile and Multimedia Networks. Macau, China: IEEE, 2017. 1−6 |
| [2] | 陈圆圆. 基于平行数据的交通预测和社会交通信息提取方法研究[博士学位论文], 中国科学院大学, 中国, 2018. Chen Yuan-Yuan. Traffic Prediction Based on Parallel Data and Traffic Information Sensing from Social Media[Ph. D. dissertation], University of Chinese Academy of Sciences, China, 2018 |
| [3] | Aramaki E, Maskawa S, Morita M. Twitter catches the flu: detecting influenza epidemics using twitter. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing. Edinburgh, Scotland, United Kingdom: Association for Computational Linguistics, 2011. 1568−1576 |
| [4] | 4 Bollen J, Mao H, Zeng X J. Twitter mood predicts the stock market. Journal of computational science, 2011, 2(1): 1−8 doi: 10.1016/j.jocs.2010.12.007 |
| [5] | Sasaki K, Nagano S, Ueno K, Cho K. Feasibility study on detection of transportation information exploiting twitter as a sensor. In: Proceedings of Sixth International AAAI Conference on Weblogs and Social Media. Dublin, Ireland: ACM, 2012. 30−35 |
| [6] | 6 Wang F Y. Scanning the issue and beyond: Real-time social transportation with online social signals. IEEE Transactions on Intelligent Transportation Systems, 2014, 15(3): 909−914 doi: 10.1109/TITS.2014.2323531 |
| [7] | 7 Zheng X H, Chen W, Wang P, Shen D Y, Chen S H, Wang X, et al. Big data for social transportation. IEEE Transactions on Intelligent Transportation Systems, 2016, 17(3): 620−630 doi: 10.1109/TITS.2015.2480157 |
| [8] | 陶汉卿, 李文勇. 基于感应线圈车辆检测器的车辆转弯信息获取. 桂林电子科技大学学报, 2008, 28(5): 387−391 doi: 10.3969/j.issn.1673-808X.2008.05.002 8 Tao Han-Qing, Li Wen-Yong. Acquisition of turning vehicles information based on induction loop detector. Journal of Guilin University of Electronic Technology, 2008, 28(5): 387−391 doi: 10.3969/j.issn.1673-808X.2008.05.002 |
| [9] | 王川童. 基于视频处理的城市道路交通拥堵判别技术研究[硕士学位论文], 重庆大学, 中国, 2010. Wang Chuan-Tong. Study on Video-based Traffic Congestion Identiflcation Technology of City Road[Master thesis], Chongqing University, China, 2010. |
| [10] | Paul R, Hamilton M, D'Souza D. A cloud model for distributed transport system integration. In: Proceedings of 2014 IEEE Fourth International Conference on Big Data and Cloud Computing. Sydney, Australia: IEEE, 2014. 77−84 |
| [11] | Zhou T, Gao L X, Ni D H. Road traffic prediction by incorporating online information. In: Proceedings of Proceedings of the 23rd International Conference on World Wide Web. Seoul, Republic of Korea: ACM, 2014. 1235−1240 |
| [12] | Zhang Y, Lu Y W, Zhang D, Shang L Y, Wang D. Risksens: A multi-view learning approach to identifying risky traffic locations in intelligent transportation systems using social and remote sensing. In: Proceedings of 2018 IEEE International Conference on Big Data. Seattle, WA, USA: IEEE, 2018. 1544−1553 |
| [13] | 董均宇. 基于GPS浮动车的城市路段平均速度估计技术研究[硕士学位论文], 重庆大学, 中国, 2006. Dong Jun-Yu. Study on Link Speed Estimation in Urban Arteries Based on GPS Equipped Floating Vehicle[Master thesis], Chongqing University, China, 2006 |
| [14] | 翁剑成, 荣建, 于泉, 任福田. 基于浮动车数据的行程速度估计算法及优化. 北京工业大学学报, 2007, 33(5): 459−464 14 Weng Jian-Cheng, Rong Jian, Yu Quan, Ren Fu-Tian. Optimization on estimation algorithms of travel speed based on the real-time floating car data. Journal of Beijing University of Technology, 2007, 33(5): 459−464 |
| [15] | Zheng Y, Liu Y C, Yuan J, Xie X. Urban computing with taxicabs. In: Proceedings of the 13th International Conference on Ubiquitous Computing. Beijing, China: ACM, 2011. 89−98 |
| [16] | Wang X Y, Chen C l, Min Y, He J p, Yang B, Zhang Y. Efficient metropolitan traffic prediction based on graph recurrent neural network. preprint arXiv: 1811.00740, 2018 |
| [17] | Koukoumidis E, Peh L S, Martonosi M. Signalguru: leveraging mobile phones for collaborative traffic signal schedule advisory. In: Proceedings of the 9th International Conference on Mobile Systems, Applications, and Services. Bethesda, Maryland, USA: ACM, 2011. 127−140 |
| [18] | Yin P F, Ye M, Lee W C, Li Z H. Mining gps data for trajectory recommendation. In: Proceedings of Pacific-Asia Conference on Knowledge Discovery and Data Mining. Macau, China: Springer, 2014. 50−61 |
| [19] | 19 Parsafard M, Chi G Q, Qu X B, Li X P, Wang H Z. Error measures for trajectory estimations with geo-tagged mobility sample data. IEEE Transactions on Intelligent Transportation Systems, 2018, PP(9): 1−18 |
| [20] | Yang Z D, Hu J, Shu Y C, Cheng P, Chen J M, Moscibroda T. Mobility modeling and prediction in bike-sharing systems. In: Proceedings of the 14th Annual International Conference on Mobile Systems, Applications, and Services. Singapore, Singapore: ACM, 2016. 165−178 |
| [21] | He T F, Bao J, Li R Y, Ruan S J, Li Y H, Tian C, Zheng Y. Detecting vehicle illegal parking events using sharing bikes' trajectories. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. London, United Kingdom: ACM, 2018. 340−349 |
| [22] | 22 Rodrigues F, Borysov S S, Ribeiro B, Pereira F C. A bayesian additive model for understanding public transport usage in special events. IEEE transactions on pattern analysis and machine intelligence, 2017, 39(11): 2113−2126 doi: 10.1109/TPAMI.2016.2635136 |
| [23] | 23 Lu Y, Wu H Y, Xin L, Chen P H, Zhang J Y. Toursense: A framework for tourist identification and analytics using transport data. IEEE Transactions on Knowledge and Data Engineering, 2019, PP(99): 1−1 |
| [24] | Itoh M, Yokoyama D, Toyoda M, Tomita Y, Kawamura S, Kitsuregawa M. Visual fusion of mega-city big data: an application to traffic and tweets data analysis of metro passengers. In: Proceedings of 2014 IEEE International Conference on Big Data. Washinton DC, USA: IEEE, 2014. 431−440 |
| [25] | 25 Itoh M, Yokoyama D, Toyoda M, Tomita Y, Kawamura S, Kitsuregawa M. Visual exploration of changes in passenger flows and tweets on mega-city metro network. IEEE Transactions on Big Data, 2016, 2(1): 85−99 doi: 10.1109/TBDATA.2016.2546301 |
| [26] | 26 Zhao J J, Qu Q, Zhang F, Xu C Z, Liu S Y. Spatio-temporal analysis of passenger travel patterns in massive smart card data. IEEE Transactions on Intelligent Transportation Systems, 2017, 18(11): 3135−3146 doi: 10.1109/TITS.2017.2679179 |
| [27] | Mashhadi A, Quattrone G, Capra L. Putting ubiquitous crowd-sourcing into context. In: Proceedings of the 2013 Conference on Computer Supported Cooperative Work. San Antonio, TX, USA: ACM, 2013. 611−622 |
| [28] | 28 Morgul E F, Yang H, Kurkcu A, Ozbay K, Bartin B, Kamga C, et al. Virtual sensors: Web-based real-time data collection methodology for transportation operation performance analysis. Transportation Research Record: Journal of the Transportation Research Board, 2014, 2442(1): 106−116 doi: 10.3141/2442-12 |
| [29] | Cui J, Fu R, Dong C H, Zhang Z. Extraction of traffic information from social media interactions: Methods and experiments. In: Proceedings of 17th International IEEE Conference on Intelligent Transportation Systems. Qingdao, China: IEEE, 2014. 1549−1554 |
| [30] | 30 Kurkcu A, Morgul E F, Ozbay K. Extended implementation method for virtual sensors: web-based real-time transportation data collection and analysis for incident management. Transportation Research Record: Journal of the Transportation Research Board, 2015, (2528): 27−37 |
| [31] | Meng C S, Cui Y, He Q, Su L, Gao J. Travel purpose inference with gps trajectories, pois, and geo-tagged social media data. In: Proceedings of 2017 IEEE International Conference on Big Data. Boston, MA, USA: IEEE, 2017. 1319−1324 |
| [32] | Santos B P, Rettore P H L, Ramos H S, Vieira L F M, Loureiro A A F. Enriching traffic information with a spatiotemporal model based on social media. In: Proceedings of 2018 IEEE Symposium on Computers and Communications. Natal, Brazil: IEEE, 2018. 00464−00469 |
| [33] | 33 Cui Y, Meng C S, He Q, Gao J. Forecasting current and next trip purpose with social media data and google places. Transportation Research Part C: Emerging Technologies, 2018, 97: 159−174 doi: 10.1016/j.trc.2018.10.017 |
| [34] | Cranshaw J, Schwartz R, Hong J I, Sadeh N. The livehoods project: Utilizing social media to understand the dynamics of a city. In: Proceedings of Sixth International AAAI Conference on Weblogs and Social Media. Dublin, Ireland: ACM 2012. 58−65 |
| [35] | Zhang A X, Noulas A, Scellato S, Mascolo C. Hoodsquare: Modeling and recommending neighborhoods in location-based social networks. In: Proceedings of 2013 International Conference on Social Computing. Alexandria, VA, USA: IEEE, 2013. 69−74 |
| [36] | Peng X F, Pan Y M, Luo J B. Predicting high taxi demand regions using social media check-ins. In: Proceedings of 2017 IEEE International Conference on Big Data. Boston, MA, USA: IEEE, 2017. 2066−2075 |
| [37] | 37 Çelikten E, Le Falher G, Mathioudakis M. Modeling urban behavior by mining geotagged social data. IEEE Transactions on Big Data, 2017, 3(2): 220−233 doi: 10.1109/TBDATA.2016.2628398 |
| [38] | 38 Hasan S, Ukkusuri S V. Reconstructing activity location sequences from incomplete check-in data: A semi-markov continuous-time bayesian network model. IEEE Transactions on Intelligent Transportation Systems, 2018, 19(3): 687−698 doi: 10.1109/TITS.2017.2700481 |
| [39] | He J R, Shen W, Divakaruni P, Wynter L, Lawrence R. Improving traffic prediction with tweet semantics. In: Proceedings of Twenty-Third International Joint Conference on Artificial Intelligence. Beijing, China: ACM, 2013. 1387−1393 |
| [40] | 张献力. 互联网网页蕴含高动态交通信息的实时搜索与语义理解技术研究[硕士学位论文], 浙江工业大学, 中国, 2014. Zhang Xian-Li. The Research of Real-time Search and Semantic Understanding of Dynamci Traffic Information Internet Web Page Contains[Master thesis], Zhejiang University of Technology, China, 2014. |
| [41] | 仇培元, 张恒才, 陆锋. 互联网文本蕴含道路交通信息抽取的模式匹配方法. 地球信息科学学报, 2015, 17(4): 416−422 41 Qiu Pei-Yuan, Zhang Heng-Cai, Lu Feng. A Pattern Matching Method for Extracting Road Traffic Information from Internet Texts. Journal of Geo-information Science, 2015, 17(4): 416−422 |
| [42] | Abidin A F, Kolberg M. Towards improved vehicle arrival time prediction in public transportation: integrating sumo and kalman filter models. In: Proceedings of 201517th UKSim-AMSS International Conference on Modelling and Simulation. Cambridge, United Kingdom: IEEE, 2015. 147−152 |
| [43] | Semwal D, Patil S, Galhotra S, Arora A, Unny N. Star: real-time spatio-temporal analysis and prediction of traffic insights using social media. In: Proceedings of Proceedings of the 2nd IKDD Conference on Data Sciences. Bangalore, India: ACM, 2015. 7 |
| [44] | Georgiou T, Abbadi A E, Yan X F, George J. Mining complaints for traffic-jam estimation: a social sensor application. In: Proceedings of Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015. Paris, France: ACM, 2015. 330-335 |
| [45] | 45 Zhang P, Deng Q, Liu X D, Yang R, Zhang H. Emergency-oriented spatiotemporal trajectory pattern recognition by intelligent sensor devices. IEEE Access, 2017, 5: 3687−3697 doi: 10.1109/ACCESS.2017.2678471 |
| [46] | Bichu N, Panangadan A. Analyzing social media communications for correlation with freeway vehicular traffic. In: Proceedings of 2017 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation. San Fransisco, CA, USA: IEEE, 2017. 1−7 |
| [47] | 47 Wang S Z, Zhang X M, Li F X, Philip S Y, Huang Z Q. Efficient traffic estimation with multi-sourced data by parallel coupled hidden markov model. IEEE Transactions on Intelligent Transportation Systems, 2018, : 1−7 |
| [48] | 48 Lin L, Li J X, Chen F, Ye J P, Huai J P. Road traffic speed prediction: a probabilistic model fusing multi-source data. IEEE Transactions on Knowledge and Data Engineering, 2018, 30(7): 1310−1323 doi: 10.1109/TKDE.2017.2718525 |
| [49] | Ali F, Shaker E S, Khan P, Kwak K S. Feature-based transportation sentiment analysis using fuzzy ontology and sentiwordnet. In: Proceedings of 2018 International Conference on Information and Communication Technology Convergence. Jeju, Island, Korea: IEEE, 2018. 1350−1355 |
| [50] | 50 Moriya K, Matsushima S, Yamanishi K. Traffic risk mining from heterogeneous road statistics. IEEE Transactions on Intelligent Transportation Systems, 2018, 19(11): 3662−3675 doi: 10.1109/TITS.2018.2856533 |
| [51] | 51 Neuhold R, Gursch H, Kern R, Cik M. Driver's dashboard-using social media data as additional information for motorway operators. IET Intelligent Transport Systems, 2018, 12(9): 1116−1122 doi: 10.1049/iet-its.2018.5337 |
| [52] | 52 Zeng K, Liu W L, Wang X, Chen S h. Traffic congestion and social media in china. IEEE Intelligent Systems, 2013, 28(1): 72−77 |
| [53] | Wayasti R A, Surjandari I, Zulkarnain. Mining customer opinion for topic modeling purpose: Case study of ride-hailing service provider. In: Proceedings of 20186th International Conference on Information and Communication Technology. Bandung, Indonesia: Telkom University, 2018. 305−309 |
| [54] | Giancristofaro G T, Panangadan A. Predicting sentiment toward transportation in social media using visual and textual features. In: Proceedings of 2016 IEEE 19th International Conference on Intelligent Transportation Systems. Rio de Janeiro, Brazil: IEEE, 2016. 2113−2118 |
| [55] | Hasnat M M, Hasan S. Understanding tourist destination choices from geo-tagged tweets. In: Proceedings of 201821st International Conference on Intelligent Transportation Systems. Maui, USA: IEEE, 2018. 3391−3396 |
| [56] | Wang D, Al-Rubaie A, Davies J, Clarke S S. Real time road traffic monitoring alert based on incremental learning from tweets. In: Proceedings of 2014 IEEE Symposium on Evolving and Autonomous Learning Systems. Orlando, USA: IEEE, 2014. 50−57 |
| [57] | 57 Wang D, Al-Rubaie A, Clarke S S, Davies J. Real-time traffic event detection from social media. ACM Transactions on Internet Technology, 2017, 18(1): 9 |
| [58] | Hsiao H J, Huang Y F, Deng H S, Hsu Y F, Hu C L. Intelligent bus information service with the support of mobile social community on the internet. In: Proceedings of 20158th International Conference on Ubi-Media Computing. Colombo, Sri Lanka: IEEE, 2015. 61−65 |
| [59] | Sinnott R O, Yin S C. Accident black spot identification and verification through social media. In: Proceedings of 2015 IEEE International Conference on Data Science and Data Intensive Systems. Sydney, NSW, Australia: IEEE, 2015. 17−24 |
| [60] | 60 Chen S M, Yuan X R, Wang Z H, Guo C, Liang J, Wang Z C, et al. Interactive visual discovering of movement patterns from sparsely sampled geo-tagged social media data. IEEE Transactions on Visualization and Computer Graphics, 2016, 22(1): 270−279 doi: 10.1109/TVCG.2015.2467619 |
| [61] | 61 Zhang S X, Wang Y, Zhang S Y, Zhu G L. Building associated semantic representation model for the ultra-short microblog text jumping in big data. Cluster Computing, 2016, 19(3): 1399−1410 doi: 10.1007/s10586-016-0602-9 |
| [62] | Maghrebi M, Abbasi A, Waller S T. Transportation application of social media: Travel mode extraction. In: Proceedings of 2016 IEEE 19th International Conference on Intelligent Transportation Systems. Rio de Janeiro, Brazil: IEEE, 2016. 1648−1653 |
| [63] | Pimpale P, Panangadan A, Abellera L V. Analyzing spread of influence in social networks for transportation applications. In: Proceedings of 2018 IEEE 8th Annual Computing and Communication Workshop and Conference. Las Vegas, NV, USA: IEEE, 2018. 763−768 |
| [64] | Yao H X, Wu F, Ke J T, Tang X F, Jia Y T, Lu S Y, et al. Deep multi-view spatial-temporal network for taxi demand prediction. In: Proceedings of Thirty-Second AAAI Conference on Artificial Intelligence. New Orleans, USA: IEEE, 2018. 2588−2595 |
| [65] | Liao Y, Yeh S. Predictability in human mobility based on geographical-boundary-free and long-time social media data. In: Proceedings of 201821st International Conference on Intelligent Transportation Systems. Hawaii, USA: IEEE, 2018. 2068−2073 |
| [66] | 崔健, 冯璇, 张佐. 基于微博的交通事件提取与文本分析系统. 交通信息与安全, 2013, 31(6): 132−135 doi: 10.3963/j.issn.1674-4861.2013.06.025 66 Cui Jian, Feng Xuan, Zhang Zuo. Extraction and analysis system of traffic incident based on microblog. Journal of Transport Information and Safety, 2013, 31(6): 132−135 doi: 10.3963/j.issn.1674-4861.2013.06.025 |
| [67] | Ni M, He Q, Gao J. Using social media to predict traffic flow under special event conditions. In: Proceedings of The 93rd Annual Meeting of Transportation Research Board. Washington, DC, USA: NAS 2014 |
| [68] | Pan B, Zheng Y, Wilkie D, Shahabi C. Crowd sensing of traffic anomalies based on human mobility and social media. In: Proceedings of Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. Orlando, FL, USA: ACM, 2013. 344−353 |
| [69] | 69 Cao J P, Zeng K, Wang H, Cheng J J, Qiao F C, Wen D, et al. Web-based traffic sentiment analysis: Methods and applications. IEEE Transactions on Intelligent Transportation systems, 2014, 15(2): 844−853 doi: 10.1109/TITS.2013.2291241 |
| [70] | 70 Grant-Muller S M, Gal-Tzur A, Minkov E, Nocera S, Kuflik T, Shoor I. Enhancing transport data collection through social media sources: methods, challenges and opportunities for textual data. IET Intelligent Transport Systems, 2014, 9(4): 407−417 |
| [71] | Gutierrez C, Figuerias P, Oliveira P, Costa R, Jardim-Goncalves R. Twitter mining for traffic events detection. In: Proceedings of 2015 Science and Information Conference. London, United Kingdom: IEEE, 2015. 371−378 |
| [72] | 72 D'Andrea E, Ducange P, Lazzerini B, Marcelloni F. Real-time detection of traffic from twitter stream analysis. IEEE Transactions on Intelligent Transportation Systems, 2015, 16(4): 2269−2283 doi: 10.1109/TITS.2015.2404431 |
| [73] | Fu K Q, Lu C T, Nune R, Tao J X. Steds: Social media based transportation event detection with text summarization. In: Proceedings of 2015 IEEE 18th International Conference on Intelligent Transportation Systems. Las Palmas, Spain: IEEE, 2015. 1952−1957 |
| [74] | Maghrebi M, Abbasi A, Rashidi T H, Waller S T. Complementing travel diary surveys with twitter data: application of text mining techniques on activity location, type and time. In: Proceedings of 2015 IEEE 18th International Conference on Intelligent Transportation Systems. Las Palmas, Spain: IEEE, 2015. 208−213 |
| [75] | Salas A, Georgakis P, Petalas Y. Incident detection using data from social media. In: Proceedings of 2017 IEEE 20th International Conference on Intelligent Transportation Systems. Yokohama, Japan: IEEE, 2017. 751-755 |
| [76] | Saragih M H, Girsang A S. Sentiment analysis of customer engagement on social media in transport online. In: Proceedings of 2017 International Conference on Sustainable Information Engineering and Technology. Malang, Indonesia: IEEE, 2017. 24−29 |
| [77] | Kulkarni G, Abellera L, Panangadan A. Unsupervised classification of online community input to advance transportation services. In: Proceedings of 2018 IEEE 8th Annual Computing and Communication Workshop and Conference. Las Vegas, NV, USA: IEEE, 2018. 261−267 |
| [78] | 郑治豪, 吴文兵, 陈鑫, 胡荣鑫, 柳鑫, 王璞. 基于社交媒体大数据的交通感知分析系统. 自动化学报, 2018, 44(4): 656−666 78 Zheng Zhi-Hao, Wu Wen-Bing, Chen Xin, Hu Rong-Xin, Liu Xin, Wang Pu. A traffic sensing and analyzing system using social media data. Acta Automatica Sinica, 2018, 44(4): 656−666 |
| [79] | Abalı G, Karaarslan E, Hürriyetoğlu A, Dalkılıç F. Detecting citizen problems and their locations using twitter |
| [80] | Alamsyah A, Rizkika W, Nugroho D D A, Renaldi F, Saadah S. Dynamic large scale data on twitter using sentiment analysis and topic modeling. In: Proceedings of 20186th International Conference on Information and Communication Technology. Bandung, Indonesia: IEEE, 2018. 254−258 |
| [81] | Alkouz B, Al Aghbari Z. Leveraging cross-lingual tweets in location recognition. In: Proceedings of 2018 IEEE International Conference on Electro/Information Technology. Rochester, MI, USA: IEEE, 2018. 0084−0089 |
| [82] | 82 Elevant K. Trust-networks for changing driver behaviour during severe weather. IET Intelligent Transport Systems, 2013, 7(4): 415−424 doi: 10.1049/iet-its.2012.0042 |
| [83] | Huang C, Zou Z Y. People's intention towards public bicycle system in wuhan. In: Proceedings of 20158th International Symposium on Computational Intelligence and Design. Hangzhou, China: IEEE, 2015. 148−151 |
| [84] | Chaniotakis E, Antoniou C, Grau J M S, Dimitriou L. Can social media data augment travel demand survey data? In: Proceedings of 2016 IEEE 19th International Conference on Intelligent Transportation Systems. Rio de Janeiro, Brazil: IEEE, 2016. 1642−1647 |
| [85] | Panadea H, Handayani P W, Pinem A A. The analysis of tourism information to enhance information quality in e-tourism. In: Proceedings of 2017 Second International Conference on Informatics and Computing. Jayapura, Indonesia: IEEE, 2017. 1−6 |
| [86] | 86 Peng Y X, Zhu W W, Zhao Y, Xu C S, Huang Q M, Lu H Q, et al. Cross-media analysis and reasoning: advances and directions. Frontiers of Information Technology & Electronic Engineering, 2017, 18(1): 44−57 |
| [87] | Shrivastava A, Pfister T, Tuzel O, Susskind J, Wang W D, Webb R. Learning from simulated and unsupervised images through adversarial training. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, HI, USA: IEEE, 2017. 2107−2116 |
| [88] | Ledig C, Theis L, Huszár F, Caballero J, Wang W D, Webb R, et al. Photo-realistic single image super-resolution using a generative adversarial network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, HI, USA: IEEE, 2017. 4681−4690 |
| [89] | Yeh R A, Chen C, Lim T Y, Schwing A G, Johnson M H, Do M N. Semantic image inpainting with deep generative models. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, HI, USA: IEEE, 2017. 5485−5493 |
| [90] | Isola P, Zhu J Y, Zhou T H, Efros A A. Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, HI, USA: IEEE, 2017. 1125−1134 |
| [91] | Zhu J Y, Park T, Isola P, Efros A A. Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision. Venice, Italy: IEEE, 2017. 2223−2232 |
| [92] | Santana E, Hotz G. Learning a driving simulator. preprint arXiv: 1608.01230, 2016 |
| [93] | 93 Wang K F, Gou C, Zheng N N, Rehg J M, Wang F Y. Recent Advances of Generative Adversarial Networks in Computer Vision. IEEE Access, 2019, 48(3): 299−219 |
| [94] | 94 Cao Y J, Jia L L, Chen Y X, Yang R, Zhang H. Parallel vision for perception and understanding of complex scenes: methods, framework, and perspectives. IEEE Access, 2019, 7: 14985−15006 doi: 10.1109/ACCESS.2018.2886814 |
| [95] | 95 Wang X, Zheng X H, Zhang Q P, Wang T, Shen D Y. Crowdsourcing in its: The state of the work and the networking. IEEE transactions on intelligent transportation systems, 2016, 17(6): 1596−1605 doi: 10.1109/TITS.2015.2513086 |
| [96] | Zimmerman J, Tomasic A, Garrod C, Yoo D, Hiruncharoenvate C, Aziz R, et al. Field trial of tiramisu: crowd-sourcing bus arrival times to spur co-design. In: Proceedings of the 2011 Annual Conference on Human Factors in Computing Systems. Vancouver, BC, Canada: ACM, 2011. 1677−1686 |
| [97] | 97 Xu Z, Zhang H, Sugumaran V, Choo K K R, Mei L, Zhu Y W. Participatory sensing-based semantic and spatial analysis of urban emergency events using mobile social media. EURASIP Journal on Wireless Communications and Networking, 2016, 2016(1): 44 doi: 10.1186/s13638-016-0553-0 |
| [98] | 王飞跃, 史蒂夫·兰森. 从人工生命到人工社会-复杂社会系统研究的现状和展望. 复杂系统与复杂性科学, 2004, 1(1): 33−41 doi: 10.3969/j.issn.1672-3813.2004.01.007 98 Wang Fei-Yue, Lansing J S. From artificial societies-New methods for studies of complex social systems. Complex Systems and Complexity Science, 2004, 1(1): 33−41 doi: 10.3969/j.issn.1672-3813.2004.01.007 |
| [99] | 王飞跃, 汤淑明. 人工交通系统的基本思想与框架体系. 复杂系统与复杂性科学, 2004, 1(2): 52−59 doi: 10.3969/j.issn.1672-3813.2004.02.008 99 Wang Fei-Yue, Tang Shu-Ming. Concepts and frameworks of artificial transportation systems. Complex Systems and Complexity Science, 2004, 1(2): 52−59 doi: 10.3969/j.issn.1672-3813.2004.02.008 |
| [100] | 王飞跃. 计算实验方法与复杂系统行为分析和决策评估. 系统仿真学报, 2004, 16(5): 893−897 doi: 10.3969/j.issn.1004-731X.2004.05.009 100 Wang Fei-Yue. Computational experiments for behavior analysis and decision evaluation of complex systems. Journal of System Simulation, 2004, 16(5): 893−897 doi: 10.3969/j.issn.1004-731X.2004.05.009 |
| [101] | 王飞跃. 平行系统方法与复杂系统的管理和控制. 控制与决策, 2004, 19(5): 485−489 doi: 10.3321/j.issn:1001-0920.2004.05.002 101 Wang Fei-Yue. Parallel system methods for management and control of complex systems. Control and Decision, 2004, 19(5): 485−489 doi: 10.3321/j.issn:1001-0920.2004.05.002 |
| [102] | 王飞跃. 关于复杂系统研究的计算理论与方法. 中国基础科学, 2004, 6(5): 5−12 102 Wang Fei-Yue. Computational theory and method on complex system. China Basic Science, 2004, 6(5): 5−12 |
| [103] | Wang F Y, Tang S M. A framework for artificial transportation systems: From computer simulations to computational experiments. In: Proceedings of 2005 IEEE Intelligent Transportation Systems. Vienna, Austria: IEEE, 2005. 1130−1134 |
| [104] | 104 Tang S M, Wang F Y. A preliminary study for basic approaches in artificial transportation systems. Journal of the Graduate School of the Chinese Academy of Sciences, 2006, 23(4): 569−575 |
| [105] | Zhu F H, Wang Z X, Wang F Y, Tang S M. Modeling interactions in artificial transportation systems using petri net. In: Proceedings of 2006 IEEE Intelligent Transportation Systems Conference. Toronto, Canada: IEEE, 2006. 1131−1136 |
| [106] | He F, Miao Q H, Li Y T, Wang F Y, Tang S M. Modeling and analysis of artificial transportation system based on multi-agent technology. In: Proceedings of 2006 IEEE Intelligent Transportation Systems Conference. Toronto, Canada: IEEE, 2006. 1120−1124 |
| [107] | Li J Y, Tang S M, Wang X Q, Wang F Y. A software architecture for artificial transportation systems-principles and framework. In: Proceedings of 2007 IEEE Intelligent Transportation Systems Conference. Seattle, WA, USA: IEEE, 2007. 229−234 |
| [108] | 108 Wang F Y. Parallel control and management for intelligent transportation systems: Concepts, architectures, and applications. IEEE Transactions on Intelligent Transportation Systems, 2010, 11(3): 630−638 doi: 10.1109/TITS.2010.2060218 |
| [109] | 109 Li J Y, Tang S M, Wang X Q, Duan W, Wang F Y. Growing artificial transportation systems: A rule-based iterative design process. IEEE Transactions on Intelligent Transportation Systems, 2011, 12(2): 322−332 doi: 10.1109/TITS.2011.2110646 |
| [110] | 110 Miao Q H, Zhu F H, Lv Y S, Cheng C J, Chen C, Qiu X G. A game-engine-based platform for modeling and computing artificial transportation systems. IEEE Transactions on Intelligent Transportation Systems, 2011, 12(2): 343−353 doi: 10.1109/TITS.2010.2103400 |
| [111] | Fei X, Eisenman S, Mahmassani H, Zhou X S. Application of dynasmart-x to the maryland chart network for real-time traffic management center decision support. In: Proceedings of 12th World Congress on Intelligent Transport Systems 2005. San Francisco, CA, USA: IEEE, 2009. 4944−4954 |
| [112] | Fellendorf M, Vortisch P. Microscopic traffic flow simulator vissim. In: Collections of Fundamentals of Traffic Simulation. Springer, 2010. 63−93 |
| [113] | Smith M, Duncan G, Druitt S. Paramics: microscopic traffic simulation for congestion management. In: Proceedings of IEE Colloquium on Dynamic Control of Strategic Inter-Urban Road Networks. London, UK: IEEE, 1995. 1−4 |
| [114] | 李力, 林懿伦, 曹东璞, 郑南宁, 王飞跃. 平行学习-机器学习的一个新型理论框架. 自动化学报, 2017, 43(1): 1−8 114 Li Li, Lin Yi-Lun, Cao Dong-Pu, Zheng Nan-Ning, Wang Fei-Yue. Parallel learning-a new framework for machine learning. Acta Automatica Sinica, 2017, 43(1): 1−8 |
| [115] | Santana E, Hotz G. Learning a driving simulator. preprint arXiv: 1608.01230, 2016 |
| [116] | 116 Lv Y Y, Chen Y Y, Li L, Wang F Y. Generative adversarial networks for parallel transportation systems. IEEE Intelligent Transportation Systems Magazine, 2018, 10(3): 4−10 doi: 10.1109/MITS.2018.2842249 |
| [117] | Chen Y Y, Lv Y Y, Wang X, Wang F Y. Traffic Flow Prediction with Parallel Data. In: Proceedings of the 21st International Conference on Intelligent Transportation Systems. Hawaii, USA: IEEE, 2018. 614−619 |
| [118] | 118 Han J W, Pei J, Kamber M. Data mining: concepts and techniques. Elsevier, 2011, |
| [119] | 姜桂艳, 李琦, 董硕. 基于k-NN和SCATS交通数据的路段行程时间估计方法. 西南交通大学学报, 2013, 48(2): 343−349 doi: 10.3969/j.issn.0258-2724.2013.02.024 119 Jiang Gui-Yan, Li Qi, Dong Shuo. Travel Time Estimation Method Using SCATS traffic Data Based on k-NN Algorithm. Journal of Southwest Jiaotong University, 2013, 48(2): 343−349 doi: 10.3969/j.issn.0258-2724.2013.02.024 |
| [120] | 120 Zheng Z D, Su D C. Short-term traffic volume forecasting: A k-nearest neighbor approach enhanced by constrained linearly sewing principle component algorithm. Transportation Research Part C: Emerging Technologies, 2014, 43: 143−157 doi: 10.1016/j.trc.2014.02.009 |
| [121] | Abidin A F, Kolberg M, Hussain A. Improved traffic prediction accuracy in public transport using trusted information in social networks. In: Proceedings of 7th York Doctoral Symposium on Computer Science & Electronics. York, UK: University of York, 2014. 19 |
| [122] | Zhang Z H. Fusing social media and traditional traffic data for advanced traveler information and travel behavior analysis[Ph. D. dissertation], State University of New York at Buffalo, USA, 2017 |
| [123] | 123 Lv Y S, Duan Y J, Kang W W, Li Z X, Wang F Y. Traffic flow prediction with big data: a deep learning approach. IEEE Transactions on Intelligent Transportation Systems, 2015, 16(2): 865−873 |
| [124] | Duan Y J, Lv Y S, Wang F Y. Travel time prediction with lstm neural network. In: Proceedings of 2016 IEEE 19th International Conference on Intelligent Transportation Systems. Rio de Janeiro, Brazil: IEEE, 2016. 1053−1058 |
| [125] | Chen Y Y, Lv Y S, Li Z J, Wang F Y. Long short-term memory model for traffic congestion prediction with online open data. In: Proceedings of 2016 IEEE 19th International Conference on Intelligent Transportation Systems. Rio de Janeiro, Brazil: IEEE, 2016. 132−137 |
| [126] | Kang D Q, Lv Y S, Chen Y Y. Short-term traffic flow prediction with lstm recurrent neural network. In: Proceedings of 2017 IEEE 20th International Conference on Intelligent Transportation Systems. Yokohama, Japan: IEEE, 2017. 1−6 |
| [127] | Chen Y Y, Lv Y S, Wang X, Wang F Y. A convolutional neural network for traffic information sensing from social media text. In: Proceedings of 2017 IEEE 20th International Conference on Intelligent Transportation Systems. Yokohama, Japan: IEEE, 2017. 1−6 |
| [128] | 128 Chen Y Y, Lv Y S, Wang X, Li L X, Wang F Y. Detecting traffic information from social media texts with deep learning approaches. IEEE Transactions on Intelligent Transportation Systems, 2018, PP(99): 1−10 |
| [129] | 叶佩军, 吕宜生, 吉竟初. 基于社会网络视角的交通仿真和计算实验研究分析. 自动化学报, 2013, 39(9): 1402−1412 129 Ye Pei-Jun, Lv Yi-Sheng, Ji Jing-Chu. Literature analysis for traffic simulation and computational experiments based on social networks. Acta Automatica Sinica, 2013, 39(9): 1402−1412 |
| [130] | 130 Zheng Y, Capra L, Wolfson O, Yang H. Urban computing: concepts, methodologies, and applications. ACM Transactions on Intelligent Systems and Technology, 2014, 5(3): 38 |
| [131] | 孟祥冰, 王蓉, 张梅, 王飞跃. 平行感知: ACP理论在视觉SLAM技术中的应用. 指挥与控制学报, 2017, 3(4): 350−358 doi: 10.3969/j.issn.2096-0204.2017.04.0350 131 Meng Xiang-Bing, Wang Rong, Zhang Mei, Wang Fei-Yue. Parallel Perception: an ACP-based Approach to Visual SLAM. Journal of Command and Control, 2017, 3(4): 350−358 doi: 10.3969/j.issn.2096-0204.2017.04.0350 |
| [132] | 刘昕, 王晓, 张卫山, 汪建基, 王飞跃. 平行数据: 从大数据到数据智能. 模式识别与人工智能, 2017, 30(8): 673−681 132 Liu Xin, Wang Xiao, Zhang Wei-Shan, Wang Fei-Yue. Parallel Data: From Big Data to Data Intelligence. Pattern Recognition and Artificial Intelligence, 2017, 30(8): 673−681 |
| [133] | 133 Chen M, Yu X H, Liu Y. Pcnn: Deep convolutional networks for short-term traffic congestion prediction. IEEE Transactions on Intelligent Transportation Systems, 2018, PP(99): 1−10 |
| [134] | Perhac J, Zeng W, Asada S, Burkhard R, Klein B, Arisona S M, Schubiger S. Urban fusion: visualizing urban data fused with social feeds via a game engine. In: Proceedings of 201721st international conference information visualisation. London, United Kingdom: IEEE, 2017. 312−317 |
| [135] | Endarnoto S K, Pradipta S, Nugroho A S, Purnama J. Traffic condition information extraction & visualization from social media twitter for android mobile application. In: Proceedings of Proceedings of the 2011 International Conference on Electrical Engineering and Informatics. Bandung, Indonesia: IEEE, 2011. 1−4 |
| [136] | 136 Zhang S, Tang J J, Wang H, Wang Y H. Enhancing traffic incident detection by using spatial point pattern analysis on social media. Transportation Research Record: Journal of the Transportation Research Board, 2015, (2528): 69−77 |
| [137] | Zhang S. Using twitter to enhance traffic incident awareness. In: Proceedings of 2015 IEEE 18th International Conference on Intelligent Transportation Systems. Las Palmas, Spain: IEEE, 2015. 2941−2946 |
| [138] | Tejaswin P, Kumar R, Gupta S. Tweeting traffic: Analyzing twitter for generating real-time city traffic insights and predictions. In: Proceedings of the 2nd IKDD Conference on Data Sciences. Bangalore, India: ACM, 2015. 9 |
| [139] | Ulloa D, Saleiro P, Rossetti R J, Silva E R. Mining social media for open innovation in transportation systems. In: Proceedings of 2016 IEEE 19th International Conference on Intelligent Transportation Systems. Rio de Janeiro, Brazil: IEEE, 2016. 169−174 |
| [140] | Guo W S, Gupta N, Pogrebna G, Jarvis S. Understanding happiness in cities using twitter: jobs, children, and transport. In: Proceedings of 2016 IEEE International Smart Cities Conference. Trento, Italy: IEEE, 2016. 1−7 |
| [141] | Singh B S R B J. Real time prediction of road traffic condition in london via twitter and related sources[Master thesis], Middlesex University, United Kingdom, 2012. |
| [142] | Lécué F, Tallevi-Diotallevi S, Hayes J, Tucker R, Bicer V, Sbodio M L, Tommasi P. Star-city: semantic traffic analytics and reasoning for city. In: Proceedings of the 19th International Conference on Intelligent User Interfaces. Haifa, Israel: ACM, 2014. 179−188 |
| [143] | Lécué F, Tucker R, Bicer V, Tommasi P, Tallevi-Diotallevi S, Sbodio M. Predicting severity of road traffic congestion using semantic web technologies. In: Proceedings of the 11th European Semantic Web Conference. Anissaras, Crete, Greec: Springer, 2014. 611−627 |
| [144] | Zhao L, Chen F, Lu C T, Ramakrishnan N. Spatiotemporal event forecasting in social media. In: Proceedings of the 2015 SIAM International Conference on Data Mining. Vancouver Canada: SIAM, 2015. 963−971 |
| [145] | 熊佳茜. 基于CRF的中文微博交通信息事件抽取[硕士学位论文], 上海交通大学, 中国, 2014. Xiong Jia-Xi. Civil Transportation Event Extraction from Chinese Microblogs Based on CRF[Master thesis], Shanghai Jiao Tong University, China, 2014. |
| [146] | Lécué F, Tucker R, Tallevi-Diotallevi S, Nair R, Gkoufas Y, Liguori G, et al. Semantic traffic diagnosis with star-city: Architecture and lessons learned from deployment in dublin, bologna, miami and rio. In: Proceedings of 2014 International Semantic Web Conference. Riva del Garda, Italy: Springer, 2014. 292−307 |
| [147] | Lécué F, Tallevi-Diotallevi S, Hayes J, Tucker R, Bicer V, Sbodio M, Tommasi P. Smart traffic analytics in the semantic web with star-city: Scenarios, system and lessons learned in dublin city. Web Semantics: Science, Services and Agents on the World Wide Web, 2014. 27: 26−33 |
| [148] | Bajaj G, Bouloukakis G, Pathak A, Singh P, Georgantas N, Issarny V. Toward enabling convenient urban transit through mobile crowdsensing. In: Proceedings of 2015 IEEE 18th International Conference on Intelligent Transportation Systems. Las Palmas, Spain: IEEE, 2015. 290−295 |
| [149] | Ngai E C H, Brandauer S, Shrestha A, Vandikas K. Personalized mobile-assisted smart transportation. In: Proceedings of 2016 Digital Media Industry & Academic Forum. Santorini, Greece: IEEE, 2016. 158−160 |
| [150] | Akilesh B, Kumar N, Reddy B, Singh M. Trafan: road traffic analysis using social media web pages. In: Proceedings of 201810th International Conference on Communication Systems & Networks. Bengaluru, India: IEEE, 2018. 655−659 |
| [151] | Wang F Y, Yang L Q, Yang J, Zhang Y L, Han S S, Zhao K. Urban intelligent parking system based on the parallel theory. In: Proceedings of 2016 International Conference on Computing, Networking and Communications. Kauai, HI, USA: IEEE, 2016. 1−5 |
| [152] | 152 Xiong G, Zhu F H, Liu X W, Dong X S, Huang W L, Chen S H, et al. Cyber-physical-social system in intelligent transportation. IEEE/CAA Journal of Automatica Sinica, 2015, 2(3): 320−333 doi: 10.1109/JAS.2015.7152667 |
| [153] | 153 Wang F Y. Scanning the Issue and Beyond: Computational Transportation and Transportation 5.0. IEEE Transactions on Intelligent Transportation Systems, 2014, 15(5): 1861−1868 doi: 10.1109/TITS.2014.2353831 |
| [154] | Wang F Y, Zhang J J. Transportation 5.0 in cpss: Towards acp-based society-centered intelligent transportation. In: Proceedings of 2017 IEEE 20th International Conference on Intelligent Transportation Systems. Yokohama, Japan: IEEE, 2017. 762−767 |