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研究生:林泉亨
研究生(外文):Chuan-Heng Lin
論文名稱:基於社群網路資料之捷運運量預測
論文名稱(外文):MRT Demand Prediction through Social Media
指導教授:陳柏華陳柏華引用關係
指導教授(外文):Albert Y. Chen
口試委員:葛宇甯許聿廷張書瑋
口試委員(外文):Louis GeYu-Ting HsuShu-Wei Chang
口試日期:2015-07-01
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:土木工程學研究所
學門:工程學門
學類:土木工程學類
論文種類:學術論文
論文出版年:2015
畢業學年度:103
語文別:英文
論文頁數:45
中文關鍵詞:機器學習社群網路捷運文本探勘影像偵測
外文關鍵詞:Social MediaTopic ModelsSupport Vector MachineRandom ForestStochastic Gradient BoostingTransportation
相關次數:
  • 被引用被引用:0
  • 點閱點閱:256
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  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:1
隨著社群網路的興起和行動裝置之普及,無論人們在社群網路上打卡,發文,或是上傳照片,都可能成為瞭解人群移動的依據。近幾年來,社群網路人數的大量增加使其成為一熱門之研究議題,而其研究課題大多圍繞者旅遊推薦,和使用者之間的關係,皆屬於個人與個人之間的課題研究,較少探討群體或是應用在系統上面的問題,因此本研究旨在將社群網路資料應用在大眾運輸系統運量需求預測之可行性。透過文字探勘方法建立話題特徵及影像偵測方法取得社群網路 - Instagram 中影像資料中人臉的的特徵,將其結合機器學習方法中的支持向量機(Support Vector Machine),隨機森林(Random Forest),及隨機梯度提升方法(Stochastic Gradient Boosting),建立預測短期各捷運站的旅客出站數之模式。本研究利用政府公開的捷運站每日各站資料,作為驗證的依據。驗證結果顯示在本研究對社群網路所提取之特徵中文字特徵具有較好之結果,且其MAPE值落在良好預測之範圍內。初步成果顯示在本研究所建議之特徵提取架構之下,能有良好預測結果並有潛力應用於實務中。

With the technological improvements of mobile devices and the increasing number of social media posts, there are more and more data on human mobility based on which information could potentially be extracted. Current research related to social media are mostly focused on inter-person behaviors. Conversely, related topics on system level performances are rarely discussed. This thesis applies feature extraction methods on quantitative, textual, and image data to retrieve useful features from social media. In addition, a machine learning pipeline based on support vector machine, random forest and stochastic gradient boosting is constructed for a short-term transportation demand forecast. Furthermore, real-world datasets from Instagram together with the demand data of the Taipei Metro Rapid Transit system are demonstrated in this work. Validation results show that social media has the potential to enhance the forecasting accuracy.


口試委員會審定書 i

[1] Tim o’Reilly. What is web 2.0. ” O’Reilly Media, Inc.”, 2009.
[2] BerndResch.Peopleassensorsandcollectivesensing-contextualobservationscom- plementing geo-sensor network measurements. In Progress in Location-Based Ser- vices, pages 391–406. Springer, 2013.
[3] Eunjoon Cho, Seth A Myers, and Jure Leskovec. Friendship and mobility: user movement in location-based social networks. In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 1082–1090. ACM, 2011.
[4] Shoko Wakamiya, Ryong Lee, and Kazutoshi Sumiya. Crowd-based urban char- acterization: extracting crowd behavioral patterns in urban areas from twitter. In Proceedings of the 3rd ACM SIGSPATIAL international workshop on location-based social networks, pages 77–84. ACM, 2011.
[5] Napong Wanichayapong, Wasawat Pruthipunyaskul, Wasan Pattara-Atikom, and Pimwadee Chaovalit. Social-based traffic information extraction and classification. In ITS Telecommunications (ITST), 2011 11th International Conference on, pages 107–112. IEEE, 2011.
[6] SílvioSRibeiroJr,ClodoveuADavisJr,DiogoRennóROliveira,WagnerMeiraJr, Tatiana S Gonçalves, and Gisele L Pappa. Traffic observatory: a system to detect and locate traffic events and conditions using twitter. In Proceedings of the 5th ACM SIGSPATIAL International Workshop on Location-Based Social Networks, pages 5– 11. ACM, 2012.
[7] Raymondus Kosala, Erwin Adi, et al. Harvesting real time traffic information from twitter. Procedia Engineering, 50:1–11, 2012.
[8] Felix Kling and Alexei Pozdnoukhov. When a city tells a story: urban topic analysis. In Proceedings of the 20th International Conference on Advances in Geographic Information Systems, pages 482–485. ACM, 2012.
[9] Laura Ferrari, Alberto Rosi, Marco Mamei, and Franco Zambonelli. Extracting ur- ban patterns from location-based social networks. In Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Location-Based Social Networks, pages 9– 16. ACM, 2011.
[10] JustinCranshaw,RazSchwartz,JasonIHong,andNormanMSadeh.Thelivehoods project: Utilizing social media to understand the dynamics of a city. In ICWSM, 2012.
[11] Samiul Hasan, Xianyuan Zhan, and Satish V Ukkusuri. Understanding urban hu- man activity and mobility patterns using large-scale location-based data from online social media. In Proceedings of the 2nd ACM SIGKDD international workshop on urban computing, page 6. ACM, 2013.
[12] Cathal Coffey and Alexei Pozdnoukhov. Temporal decomposition and semantic en- richment of mobility flows. In Proceedings of the 6th ACM SIGSPATIAL Interna- tional Workshop on Location-Based Social Networks, pages 34–43. ACM, 2013.
[13] David M Blei and John D Lafferty. Topic models. Text mining: classification, clustering, and applications, 10(71):34, 2009.
[14] Sean Massung. Reduction of high-dimensional feature spaces with supervised lda. 2013.
[15] Chengxiang Zhai and John Lafferty. Model-based feedback in the language model- ing approach to information retrieval. In Proceedings of the tenth international con- ference on Information and knowledge management, pages 403–410. ACM, 2001.
[16] David Andrzejewski and David Buttler. Latent topic feedback for information re- trieval. In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 600–608. ACM, 2011.
[17] David M Blei, Andrew Y Ng, and Michael I Jordan. Latent dirichlet allocation. the Journal of machine Learning research, 3:993–1022, 2003.
[18] Daniel D Lee and H Sebastian Seung. Algorithms for non-negative matrix factoriza- tion. In Advances in neural information processing systems, pages 556–562, 2001.
[19] Keith Stevens, Philip Kegelmeyer, David Andrzejewski, and David Buttler. Ex- ploring topic coherence over many models and many topics. In Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pages 952–961. Association for Com- putational Linguistics, 2012.
[20] Mark Steyvers and Tom Griffiths. Probabilistic topic models. Handbook of latent semantic analysis, 427(7):424–440, 2007.
[21] Topic modeling with latent dirichlet allocation. https://github.com/ ariddell/lda. Accessed: 2015-07-27.
[22] Thomas L Griffiths and Mark Steyvers. Finding scientific topics. Proceedings of the National Academy of Sciences, 101(suppl 1):5228–5235, 2004.
[23] V Paul Pauca, Farial Shahnaz, Michael W Berry, and Robert J Plemmons. Text mining using non-negative matrix factorizations. In SDM, volume 4, pages 452– 456, 2004.
[24] Vicki Bruce and Andy Young. In the eye of the beholder: the science of face per- ception. Oxford University Press, 1998.
[25] Wenyi Zhao, Rama Chellappa, P Jonathon Phillips, and Azriel Rosenfeld. Face recognition: A literature survey. ACM computing surveys (CSUR), 35(4):399–458, 2003.
[26] Paul Viola and Michael J Jones. Robust real-time face detection. International journal of computer vision, 57(2):137–154, 2004.
[27] John Wright, Allen Y Yang, Arvind Ganesh, Shankar S Sastry, and Yi Ma. Robust face recognition via sparse representation. Pattern Analysis and Machine Intelli- gence, IEEE Transactions on, 31(2):210–227, 2009.
[28] RobJenkinsandAMBurton.100%accuracyinautomaticfacerecognition.Science, 319(5862):435–435, 2008.
[29] Saeideh Bakhshi, David A Shamma, and Eric Gilbert. Faces engage us: Photos with faces attract more likes and comments on instagram. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pages 965–974. ACM, 2014.
[30] Jin Yea Jang, Kyungsik Han, Patrick C Shih, and Dongwon Lee. Generation like: Comparative characteristics in instagram. In ACM conference on Human factors in computing systems (CHI), 2015.
[31] Vladimir Naumovich Vapnik and Vlamimir Vapnik. Statistical learning theory, vol- ume 1. Wiley New York, 1998.
[32] Vladimir Cherkassky and Filip M Mulier. Learning from data: concepts, theory, and methods. John Wiley & Sons, 2007.
[33] Marti A. Hearst, Susan T Dumais, Edgar Osman, John Platt, and Bernhard Scholkopf. Support vector machines. Intelligent Systems and their Applications, IEEE, 13(4):18–28, 1998.
[34] Jerome Friedman, Trevor Hastie, and Robert Tibshirani. The elements of statistical learning, volume 1. Springer series in statistics Springer, Berlin, 2001.
[35] Christopher M Bishop. Pattern recognition and machine learning. springer, 2006.
[36] Instagram api. https://Instagram.com/developer/. Accessed: 2015- 07-27.
[37] Station geographical information. http://data.taipei.gov.tw/ opendata/apply/NewDataContent?oid=47AB5A0B-FA22-43D4- 8446-3C7C6AA6DCCE. Accessed: 2015-07-27.
[38] Daily numbers of passengers. http://data.taipei/opendata/ datalist/datasetMeta?oid=1d71c478-205f-42c5-8386- 35f86d74fdd1. Accessed: 2015-07-27.
[39] Mrtstationopendata.http://data.taipei.gov.tw/opendata/apply/ NewDataContent?oid=47AB5A0B-FA22-43D4-8446-3C7C6AA6DCC. Accessed: 2015-07-27.
[40] Instagram official site. http://blog.Instagram.com/. Accessed: 2015-07- 27.
[41] Nadav Hochman and Lev Manovich. Zooming into an instagram city: Reading the local through social media. First Monday, 18(7), 2013.
[42] Lydia Manikonda, Yuheng Hu, and Subbarao Kambhampati. Analyzing user activ- ities, demographics, social network structure and user-generated content on insta- gram. arXiv preprint arXiv:1410.8099, 2014.
[43] Yuheng Hu, Lydia Manikonda, Subbarao Kambhampati, et al. What we instagram: A first analysis of instagram photo content and user types. Proc. AAAI ICWSM, 2014.
[44] Leonard Richardson. Beautiful soup documentation, 2015.
[45] Paul McGuire. Getting started with pyparsing. ” O’Reilly Media, Inc.”, 2007.
[46] Chrome devtools. https://developer.chrome.com/devtools. Ac- cessed: 2015-07-27.
[47] Ken Thompson. Programming techniques: Regular expression search algorithm. Communications of the ACM, 11(6):419–422, 1968.
[48] Steven Bird. Nltk: the natural language toolkit. In Proceedings of the COLING/ACL on Interactive presentation sessions, pages 69–72. Association for Computational Linguistics, 2006.
[49] Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, et al. Scikit-learn: Machine learning in python. The Journal of Machine Learning Research, 12:2825–2830, 2011.
[50] Face + +. http://research.faceplusplus.com/2015-face- recognition-technical-report/. Accessed: 2015-07-27.
[51] C. D. Lewis. Industrial and business forecasting methods : a practical guide to exponential smoothing and curve fitting / Colin D. Lewis. Butterworth Scientific London, 1982.
[52] Matthijs Douze, Hervé Jégou, Harsimrat Sandhawalia, Laurent Amsaleg, and Cordelia Schmid. Evaluation of gist descriptors for web-scale image search. In Proceedings of the ACM International Conference on Image and Video Retrieval, page 19. ACM, 2009.
[53] David M Blei and John D Lafferty. Dynamic topic models. In Proceedings of the 23rd international conference on Machine learning, pages 113–120. ACM, 2006.

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