Renhe Jiang
Xuan Song
Skip Abstract Section
Abstract
Rapidly developing location acquisition technologies have provided us with big GPS trajectory data, which offers a new means of understanding people's daily behaviors as well as urban dynamics. With such data, predicting human mobility at the city level will be of great significance for transportation scheduling, urban regulation, and emergency management. In particular, most urban human behaviors are related to a small number of important regions, referred to as Regions-of-Interest (ROIs). Therefore, in this study, a deep ROI-based modeling approach is proposed for effectively predicting urban human mobility. Urban ROIs are first discovered from historical trajectory data, and urban human mobility is designated using two types of ROI labels (ISROI and WHICHROI). Then, urban mobility prediction is modeled as a sequence classification problem for each type of label. Finally, a deep-learning architecture built with recurrent neural networks is designed as an effective sequence classifier. Experimental results demonstrate that the superior performance of our proposed approach to the baseline models and several real-world practices show the applicability of our approach to real-world urban computing problems.
- Martin Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dan Mané, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng. 2015. TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. (2015). http://tensorflow.org/ Software available from tensorflow.org.Google Scholar
- Rakesh Agrawal and Ramakrishnan Srikant. 1995. Mining sequential patterns. In Data Engineering, 1995. Proceedings of the Eleventh International Conference on. IEEE, 3--14. Google ScholarDigital Library
- Pablo Samuel Castro, Daqing Zhang, and Shijian Li. 2012. Urban traffic modelling and prediction using large scale taxi GPS traces. In Pervasive Computing. Springer, 57--72. Google ScholarDigital Library
- Po-Ta Chen, Feng Chen, and Zhen Qian. 2014. Road traffic congestion monitoring in social media with hinge-loss Markov random fields. In 2014 IEEE International Conference on Data Mining. IEEE, 80--89. Google ScholarDigital Library
- FranÃğois Chollet. 2015. keras. https://github.com/fchollet/keras. (2015).Google Scholar
- Dorin Comaniciu and Peter Meer. 2002. Mean shift: A robust approach toward feature space analysis. IEEE Transactions on pattern analysis and machine intelligence 24, 5 (2002), 603--619. Google ScholarDigital Library
- Thomas Cover and Peter Hart. 1967. Nearest neighbor pattern classification. IEEE transactions on information theory 13, 1 (1967), 21--27. Google ScholarDigital Library
- Howard B Demuth, Mark H Beale, Orlando De Jess, and Martin T Hagan. 2014. Neural network design. Martin Hagan. Google ScholarDigital Library
- Zipei Fan, Xuan Song, and Ryosuke Shibasaki. 2014. CitySpectrum: a non-negative tensor factorization approach. In Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM, 213--223. Google ScholarDigital Library
- Zipei Fan, Xuan Song, Ryosuke Shibasaki, and Ryutaro Adachi. 2015. CityMomentum: an online approach for crowd behavior prediction at a citywide level. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM, 559--569. Google ScholarDigital Library
- Alex Graves. 2012. Supervised Sequence Labeling with Recurrent Neural Networks. Studies in Computational Intelligence, Vol. 385. Springer. 1--131 pages.Google Scholar
- Alex Graves, Abdel-rahman Mohamed, and Geoffrey Hinton. 2013. Speech recognition with deep recurrent neural networks. In Acoustics, speech and signal processing (icassp), 2013 ieee international conference on. IEEE, 6645--6649.Google Scholar
- Jiawei Han, Jian Pei, Behzad Mortazavi-Asl, Helen Pinto, Qiming Chen, Umeshwar Dayal, and MC Hsu. 2001. Prefixspan: Mining sequential patterns efficiently by prefix-projected pattern growth. In proceedings of the 17th international conference on data engineering. 215--224. Google ScholarDigital Library
- Minh X Hoang, Yu Zheng, and Ambuj K Singh. 2016. Forecasting citywide crowd flows based on big data. ACM SIGSPATIAL (2016). Google ScholarDigital Library
- Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural computation 9, 8 (1997), 1735--1780. Google ScholarDigital Library
- Wenhao Huang, Guojie Song, Haikun Hong, and Kunqing Xie. 2014. Deep architecture for traffic flow prediction: Deep belief networks with multitask learning. Intelligent Transportation Systems, IEEE Transactions on 15, 5 (2014), 2191--2201.Google ScholarCross Ref
- Sibren Isaacman, Richard Becker, Ramón Cáceres, Margaret Martonosi, James Rowland, Alexander Varshavsky, and Walter Willinger. 2012. Human mobility modeling at metropolitan scales. In Proceedings of the 10th international conference on Mobile systems, applications, and services. Acm, 239--252. Google ScholarDigital Library
- Hoyoung Jeung, Qing Liu, Heng Tao Shen, and Xiaofang Zhou. 2008. A hybrid prediction model for moving objects. In Data Engineering, 2008. ICDE 2008. IEEE 24th International Conference on. Ieee, 70--79. Google ScholarDigital Library
- Tatsuya Konishi, Mikiya Maruyama, Kota Tsubouchi, and Masamichi Shimosaka. 2016. CityProphet: city-scale irregularity prediction using transit app logs. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM, 752--757. Google ScholarDigital Library
- Jae-Gil Lee, Jiawei Han, and Kyu-Young Whang. 2007. Trajectory clustering: a partition-and-group framework. In Proceedings of the 2007 ACM SIGMOD international conference on Management of data. ACM, 593--604. Google ScholarDigital Library
- Defu Lian, Xing Xie, Vincent W Zheng, Nicholas Jing Yuan, Fuzheng Zhang, and Enhong Chen. 2015. CEPR: A collaborative exploration and periodically returning model for location prediction. ACM Transactions on Intelligent Systems and Technology (TIST) 6, 1 (2015), 8. Google ScholarDigital Library
- Defu Lian, Cong Zhao, Xing Xie, Guangzhong Sun, Enhong Chen, and Yong Rui. 2014. GeoMF: joint geographical modeling and matrix factorization for point-of-interest recommendation. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 831--840. Google ScholarDigital Library
- Andy Liaw and Matthew Wiener. 2002. Classification and regression by randomForest. R news 2, 3 (2002), 18--22.Google Scholar
- Yisheng Lv, Yanjie Duan, Wenwen Kang, Zhengxi Li, and Fei-Yue Wang. 2015. Traffic flow prediction with big data: a deep learning approach. Intelligent Transportation Systems, IEEE Transactions on 16, 2 (2015), 865--873.Google Scholar
- Xiaolei Ma, Zhimin Tao, Yinhai Wang, Haiyang Yu, and Yunpeng Wang. 2015. Long short-term memory neural network for traffic speed prediction using remote microwave sensor data. Transportation Research Part C: Emerging Technologies 54 (2015), 187--197.Google ScholarCross Ref
- Xiaolei Ma, Haiyang Yu, Yunpeng Wang, and Yinhai Wang. 2015. Large-scale transportation network congestion evolution prediction using deep learning theory. PloS one 10, 3 (2015), e0119044.Google ScholarCross Ref
- Anna Monreale, Fabio Pinelli, Roberto Trasarti, and Fosca Giannotti. 2009. Wherenext: a location predictor on trajectory pattern mining. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 637--646. Google ScholarDigital Library
- Anastasios Noulas, Salvatore Scellato, Neal Lathia, and Cecilia Mascolo. 2012. Mining user mobility features for next place prediction in location-based services. In Data mining (ICDM), 2012 IEEE 12th international conference on. IEEE, 1038--1043. Google ScholarDigital Library
- J. Ross Quinlan. 1987. Simplifying decision trees. International journal of man-machine studies 27, 3 (1987), 221--234. Google ScholarDigital Library
- Salvatore Scellato, Mirco Musolesi, Cecilia Mascolo, Vito Latora, and Andrew T Campbell. 2011. Nextplace: a spatio-temporal prediction framework for pervasive systems. In International Conference on Pervasive Computing. Springer, 152--169. Google ScholarDigital Library
- Chaoming Song, Tal Koren, Pu Wang, and Albert-László Barabási. 2010. Modelling the scaling properties of human mobility. Nature Physics 6, 10 (2010), 818--823.Google ScholarCross Ref
- Chaoming Song, Zehui Qu, Nicholas Blumm, and Albert-László Barabási. 2010. Limits of predictability in human mobility. Science 327, 5968(2010), 1018--1021.Google Scholar
- Xuan Song, Hiroshi Kanasugi, and Ryosuke Shibasaki. 2016. Deeptransport: Prediction and simulation of human mobility and transportation mode at a citywide level. IJCAI. Google ScholarDigital Library
- Xuan Song, Quanshi Zhang, Yoshihide Sekimoto, and Ryosuke Shibasaki. 2014. Intelligent System for Urban Emergency Management during Large-Scale Disaster. In Twenty-Eighth AAAI Conference on Artificial Intelligence. https://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8208 Google ScholarDigital Library
- Xuan Song, Quanshi Zhang, Yoshihide Sekimoto, Ryosuke Shibasaki, Nicholas Jing Yuan, and Xing Xie. 2015. A Simulator of Human Emergency Mobility following Disasters: Knowledge Transfer from Big Disaster Data. In Twenty-Ninth AAAI Conference on Artificial Intelligence. Google ScholarDigital Library
- Han Su, Kai Zheng, Haozhou Wang, Jiamin Huang, and Xiaofang Zhou. 2013. Calibrating trajectory data for similarity-based analysis. In Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data. ACM, 833--844. Google ScholarDigital Library
- Yingzi Wang, Nicholas Jing Yuan, Defu Lian, Linli Xu, Xing Xie, Enhong Chen, and Yong Rui. 2015. Regularity and conformity: Location prediction using heterogeneous mobility data. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 1275--1284. Google ScholarDigital Library
- Junbo Zhang, Yu Zheng, and Dekang Qi. 2017. Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction. Thirty-First AAAI Conference on Artificial Intelligence (2017).Google Scholar
- Jiangchuan Zheng and Lionel M Ni. 2012. An unsupervised framework for sensing individual and cluster behavior patterns from human mobile data. In Proceedings of the 2012 ACM Conference on Ubiquitous Computing. ACM, 153--162. Google ScholarDigital Library
- Vincent W Zheng, Yu Zheng, Xing Xie, and Qiang Yang. 2010. Collaborative location and activity recommendations with GPS history data. In Proceedings of the 19th international conference on World wide web. ACM, 1029--1038. Google ScholarDigital Library
- Yu Zheng, Licia Capra, Ouri Wolfson, and Hai Yang. 2014. Urban computing: concepts, methodologies, and applications. ACM Transactions on Intelligent Systems and Technology (TIST) 5, 3 (2014), 38. Google ScholarDigital Library
Index Terms
- Deep ROI-Based Modeling for Urban Human Mobility Prediction
Recommendations
Transfer Urban Human Mobility via POI Embedding over Multiple Cities
Survey Paper, Special Issue on Urban Computing and Smart Cities and Regular PaperRapidly developing location acquisition technologies provide a powerful tool for understanding and predicting human mobility in cities, which is very significant for urban planning, traffic regulation, and emergency management. However, with the ...
Urban Computing: Concepts, Methodologies, and Applications
Special Section on Urban ComputingUrbanization's rapid progress has modernized many people's lives but also engendered big issues, such as traffic congestion, energy consumption, and pollution. Urban computing aims to tackle these issues by using the data that has been generated in ...
Discovering regions of different functions in a city using human mobility and POIs
KDD '12: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data miningThe development of a city gradually fosters different functional regions, such as educational areas and business districts. In this paper, we propose a framework (titled DRoF) that Discovers Regions of different Functions in a city using both human ...
Comments