{"type":"ScholarlyArticle","title":"Representing Urban Functions through Zone Embedding with Human Mobility Patterns","authors":[{"name":"Zijun Yao","slug":"zijun-yao","given":"Zijun","family":"Yao","affiliation":"Rutgers University"},{"name":"Yanjie Fu","slug":"yanjie-fu","given":"Yanjie","family":"Fu","affiliation":"Missouri University of Science and Technology"},{"name":"Bin Liu","slug":"bin-liu","given":"Bin","family":"Liu","affiliation":"IBM Thomas J. Watson Research Center"},{"name":"Wangsu Hu","slug":"wangsu-hu","given":"Wangsu","family":"Hu","affiliation":"Rutgers University"},{"name":"Hui Xiong","slug":"hui-xiong","given":"Hui","family":"Xiong","affiliation":"Rutgers University"}],"abstract":"Urban functions refer to the purposes of land use in cities where each zone plays a distinct role and cooperates with each other to serve people’s various life needs. Understanding zone functions helps to solve a variety of urban related problems, such as increasing traffic capacity and enhancing location-based service. Therefore, it is beneficial to investigate how to learn the representations of city zones in terms of urban functions, for better supporting urban analytic applications. To this end, in this paper, we propose a framework to learn the vector representation (embedding) of city zones by exploiting large-scale taxi trajectories. Specifically, we extract human mobility patterns from taxi trajectories, and use the co-occurrence of origin-destination zones to learn zone embeddings. To utilize the spatio-temporal characteristics of human mobility patterns, we incorporate mobility direction, departure/arrival time, destination attraction, and travel distance into the modeling of zone embeddings. We conduct extensive experiments with real-world urban datasets of New York City. Experimental results demonstrate the effectiveness of the proposed embedding model to represent urban functions of zones with human mobility data.","doi":"10.24963/ijcai.2018/545","url":"https://ijcai.org/proceedings/2018/545","pdf":"https://ijcai.org/proceedings/2018/0545.pdf","markdown":"https://ijcai.org/proceedings/2018/545.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/545","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Multidisciplinary Topics and Applications","track":"Main track","number":545,"pages":{"first":3919,"last":3925},"keywords":["Multidisciplinary Topics and Applications: Multidisciplinary Topics and Applications","Machine Learning Applications: Applications of Unsupervised Learning","Machine Learning: Data Mining"],"videos":[],"references":[{"position":1,"raw":"[Bengio et al., 2003] Yoshua Bengio, Réjean Ducharme, Pascal Vincent, and Christian Jauvin. A neural probabilis- tic language model. JMLR, 3(Feb):1137-1155, 2003.","title":"A neural probabilistic language model","authors":"Yoshua Bengio, Réjean Ducharme, Pascal Vincent, Christian Jauvin","year":"2003","venue":"JMLR","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Cascetta et al., 2007] Ennio Cascetta, Francesca Pagliara, and Andrea Papola. Alternative approaches to trip distri- bution modelling: a retrospective review and suggestions for combining different approaches. Papers in Regional Science, 86(4):597-620, 2007.","title":"Alternative approaches to trip distribution modelling: a retrospective review and suggestions for combining different approaches","authors":"Ennio Cascetta, Francesca Pagliara, Andrea Papola","year":"2007","venue":"Papers in Regional Science","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Cheng et al., 2011] Zhiyuan Cheng, James Caverlee, Kyu- min Lee, and Daniel Z Sui. Exploring millions of foot- prints in location sharing services. ICWSM, 2011:81-88, 2011.","title":"Exploring millions of footprints in location sharing services","authors":"Zhiyuan Cheng, James Caverlee, Kyumin Lee, Daniel Z Sui","year":"2011","venue":"ICWSM","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Collobert and Weston, 2008] Ronan Collobert and Jason Weston. A unified architecture for natural language pro- cessing: Deep neural networks with multitask learning. In ICML, pages 160-167. ACM, 2008.","title":"A unified architecture for natural language processing: Deep neural networks with multitask learning","authors":"Weston ; Collobert, Ronan Collobert, Jason Weston","year":"2008","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Cranshaw et al., 2012] Justin Cranshaw, Raz Schwartz, Ja- son I. Hong, and Norman M. Sadeh. The livehoods project: Utilizing social media to understand the dynamics of a city. In ICWSM, 2012.","title":"The livehoods project: Utilizing social media to understand the dynamics of a city","authors":"Justin Cranshaw, Raz Schwartz, Jason I Hong, Norman M Sadeh","year":"2012","venue":"ICWSM","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Deb et al., 2002] Kalyanmoy Deb, Amrit Pratap, Sameer Agarwal, and TAMT Meyarivan. A fast and elitist mul- tiobjective genetic algorithm: Nsga-ii. IEEE transactions on evolutionary computation, 6(2):182-197, 2002.","title":"A fast and elitist multiobjective genetic algorithm: Nsga-ii","authors":"Kalyanmoy Deb, Amrit Pratap, Sameer Agarwal, Meyarivan","year":"2002","venue":"IEEE transactions on evolutionary computation","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Ge et al., 2010] Yong Ge, Hui Xiong, Alexander Tuzhilin, Keli Xiao, Marco Gruteser, and Michael Pazzani. An energy-efficient mobile recommender system. In KDD, pages 899-908. ACM, 2010.","title":"An energy-efficient mobile recommender system","authors":"Yong Ge, Hui Xiong, Alexander Tuzhilin, Keli Xiao, Marco Gruteser, Michael Pazzani","year":"2010","venue":"KDD","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Goddard, 1970] John Burgess Goddard. Functional regions within the city centre: A study by factor analysis of taxi flows in central london. Transactions of the Institute of British Geographers, pages 161-182, 1970.","title":"Functional regions within the city centre: A study by factor analysis of taxi flows in central london","authors":"John Burgess, Goddard","year":"1970","venue":"Transactions of the Institute of British Geographers","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Kling and Pozdnoukhov, 2012] Felix Kling and Alexei Pozdnoukhov. When a city tells a story: urban topic anal- ysis. In SIGSPATIAL, pages 482-485. ACM, 2012.","title":"When a city tells a story: urban topic analysis","authors":"Pozdnoukhov Kling, Felix Kling, Alexei Pozdnoukhov","year":"2012","venue":"SIGSPATIAL","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Kusner et al., 2015] Matt J. Kusner, Yu Sun, Nicholas I. Kolkin, and Kilian Q. Weinberger. From word embeddings to document distances. In ICML, pages 957-966, 2015.","title":"From word embeddings to document distances","authors":"Matt J Kusner, Yu Sun, Nicholas I Kolkin, Kilian Q Weinberger","year":"2015","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Levy and Goldberg, 2014] Omer Levy and Yoav Goldberg. Neural word embedding as implicit matrix factorization. In NIPS, pages 2177-2185, 2014.","title":"Neural word embedding as implicit matrix factorization","authors":"Goldberg Levy, Omer Levy, Yoav Goldberg","year":"2014","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Levy et al., 2015] Omer Levy, Yoav Goldberg, and Ido Da- gan. Improving distributional similarity with lessons learned from word embeddings. Transactions of the Asso- ciation for Computational Linguistics, 3:211-225, 2015.","title":"Improving distributional similarity with lessons learned from word embeddings","authors":"Omer Levy, Yoav Goldberg, Ido Dagan","year":"2015","venue":"Transactions of the Association for Computational Linguistics","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Liu et al., 2017] Junming Liu, Leilei Sun, Qiao Li, Jingci Ming, Yanchi Liu, and Hui Xiong. Functional zone based hierarchical demand prediction for bike system expansion. In KDD, pages 957-966. ACM, 2017.","title":"Functional zone based hierarchical demand prediction for bike system expansion","authors":"Junming Liu, Leilei Sun, Qiao Li, Jingci Ming, Yanchi Liu, Hui Xiong","year":"2017","venue":"KDD","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[McFadden, 1974] Daniel McFadden. The measurement of urban travel demand. Journal of Public Economics, 3(4):303-328, 1974.","title":"The measurement of urban travel demand","authors":"Daniel Mcfadden","year":"1974","venue":"Journal of Public Economics","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Mikolov et al., 2013a] Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781, 2013.","title":"Efficient estimation of word representations in vector space","authors":"Mikolov","year":"2013","venue":null,"doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Mikolov et al., 2013b] Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. Distributed repre- sentations of words and phrases and their compositionality. In NIPS, pages 3111-3119, 2013.","title":"Distributed representations of words and phrases and their compositionality","authors":"Mikolov","year":"2013","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Pennington et al., 2014] Jeffrey Pennington, Richard Socher, and Christopher D Manning. Glove: Global vectors for word representation. In EMNLP, volume 14, pages 1532-1543, 2014.","title":"Glove: Global vectors for word representation","authors":"Jeffrey Pennington, Richard Socher, Christopher D Manning","year":"2014","venue":"EMNLP","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"Robert D Putnam. Bowling alone: The col- lapse and revival of American community. Simon and Schuster, 2001.","title":"Bowling alone: The collapse and revival of American community","authors":"Putnam Robert","year":"2001","venue":null,"doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Wang et al., 2015] Yingzi Wang, Nicholas Jing Yuan, Defu Lian, Linli Xu, Xing Xie, Enhong Chen, and Yong Rui. Regularity and conformity: Location prediction using het- erogeneous mobility data. In KDD, pages 1275-1284. ACM, 2015.","title":"Regularity and conformity: Location prediction using heterogeneous mobility data","authors":"Yingzi Wang, Nicholas Jing Yuan, Defu Lian, Linli Xu, Xing Xie, Enhong Chen, Yong Rui","year":"2015","venue":"KDD","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Wang et al., 2016] Gang Wang, Sarita Yardi Schoenebeck, Haitao Zheng, and Ben Y. Zhao. \"will check-in for badges\": Understanding bias and misbehavior on location- based social networks. In ICWSM, pages 417-426, 2016.","title":"will check-in for badges\": Understanding bias and misbehavior on locationbased social networks","authors":"Gang Wang, Sarita Yardi Schoenebeck, Haitao Zheng, Ben Y Zhao","year":"2016","venue":"ICWSM","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Yang et al., 2015] Dingqi Yang, Daqing Zhang, Vincent. W. Zheng, and Zhiyong Yu. Modeling user activity prefer- ence by leveraging user spatial temporal characteristics in lbsns. IEEE Transactions on Systems, Man, and Cybernet- ics: Systems, 45(1):129-142, 2015.","title":"Modeling user activity preference by leveraging user spatial temporal characteristics in lbsns","authors":"Yang","year":"2015","venue":"IEEE Transactions on Systems, Man, and Cybernetics: Systems","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Yao et al., 2016] Zijun Yao, Yanjie Fu, Bin Liu, Yanchi Liu, and Hui Xiong. POI recommendation: A temporal match- ing between POI popularity and user regularity. In ICDM, pages 549-558, 2016.","title":"POI recommendation: A temporal matching between POI popularity and user regularity","authors":"Zijun Yao, Yanjie Fu, Bin Liu, Yanchi Liu, Hui Xiong","year":"2016","venue":"ICDM","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Yuan et al., 2011] Jing Yuan, Yu Zheng, Liuhang Zhang, XIng Xie, and Guangzhong Sun. Where to find my next passenger. In Ubicomp, pages 109-118. ACM, 2011.","title":"Where to find my next passenger","authors":"Jing Yuan, Yu Zheng, Liuhang Zhang, Xing Xie, Guangzhong Sun","year":"2011","venue":"Ubicomp","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Yuan et al., 2012] Jing Yuan, Yu Zheng, and Xing Xie. Dis- covering regions of different functions in a city using hu- man mobility and pois. In KDD, pages 186-194. ACM, 2012.","title":"Discovering regions of different functions in a city using human mobility and pois","authors":"Jing Yuan, Yu Zheng, Xing Xie","year":"2012","venue":"KDD","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Yuan et al., 2015] Nicholas Jing Yuan, Yu Zheng, Xing Xie, Yingzi Wang, Kai Zheng, and Hui Xiong. Discovering urban functional zones using latent activity trajectories. TKDE, 27(3):712-725, 2015.","title":"Discovering urban functional zones using latent activity trajectories","authors":"Nicholas Jing Yuan, Yu Zheng, Xing Xie, Yingzi Wang, Kai Zheng, Hui Xiong","year":"2015","venue":"TKDE","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Zheng et al., 2011] Yu Zheng, Yanchi Liu, Jing Yuan, and Xing Xie. Urban computing with taxicabs. In Ubicomp, pages 89-98. ACM, 2011.","title":"Urban computing with taxicabs","authors":"Yu Zheng, Yanchi Liu, Jing Yuan, Xing Xie","year":"2011","venue":"Ubicomp","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}