Renhe Jiang
Xuan Song
Skip Abstract Section
Abstract
Rapidly 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 existing methodologies, it is still difficult to accurately predict millions of peoples’ mobility in a large urban area such as Tokyo, Shanghai, and Hong Kong, especially when collected data used for model training are often limited to a small portion of the total population. Obviously, human activities in city are closely linked with point-of-interest (POI) information, which can reflect the semantic meaning of human mobility. This motivates us to fuse human mobility data and city POI data to improve the prediction performance with limited training data, but current fusion technologies can hardly handle these two heterogeneous data. Therefore, we propose a unique POI-embedding mechanism, that aggregates the regional POIs by categories to generate an artificial POI-image for each urban grid and enriches each trajectory snippet to a four-dimensional tensor in an analogous manner to a short video. Then, we design a deep learning architecture combining CNN with LSTM to simultaneously capture both the spatiotemporal and geographical information from the enriched trajectories. Furthermore, transfer learning is employed to transfer mobility knowledge from one city to another, so that we can fully utilize other cities’ data to train a stronger model for the target city with only limited data available. Finally, we achieve satisfactory performance of human mobility prediction at the citywide level using a limited amount of trajectories as training data, which has been validated over five urban areas of different types and scales.
- Martín 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. Retrieved from http://tensorflow.org.Google Scholar
- Alexandre Alahi, Kratarth Goel, Vignesh Ramanathan, Alexandre Robicquet, Li Fei-Fei, and Silvio Savarese. 2016. Social LSTM: Human trajectory prediction in crowded spaces. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 961--971.Google ScholarCross Ref
- Jie Bao, Yu Zheng, David Wilkie, and Mohamed Mokbel. 2015. Recommendations in location-based social networks: A survey. GeoInformatica 19, 3 (2015), 525--565.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 Scholar
- Po-Ta Chen, Feng Chen, and Zhen Qian. 2014. Road traffic congestion monitoring in social media with hinge-loss Markov random fields. In Proceedings of the IEEE International Conference on Data Mining. IEEE, 80--89.Google Scholar
- Kyunghyun Cho, Bart Van Merriënboer, Dzmitry Bahdanau, and Yoshua Bengio. 2014. On the properties of neural machine translation: Encoder-decoder approaches. Retrieved from https://arXiv:1409.1259.Google Scholar
- Francois Chollet. 2015. keras. Retrieved from https://github.com/fchollet/keras.Google Scholar
- Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio. 2014. Empirical evaluation of gated recurrent neural networks on sequence modeling. Retrieved from https://arXiv:1412.3555.Google Scholar
- Thomas Cover and Peter Hart. 1967. Nearest neighbor pattern classification. IEEE Trans. Info. Theory 13, 1 (1967), 21--27.Google ScholarDigital Library
- Daizong Ding, Mi Zhang, Xudong Pan, Duocai Wu, and Pearl Pu. 2018. Geographical feature extraction for entities in location-based social networks. In Proceedings of the World Wide Web Conference on World Wide Web. International World Wide Web Conferences Steering Committee, 833--842.Google Scholar
- Jeffrey Donahue, Lisa Anne Hendricks, Sergio Guadarrama, Marcus Rohrbach, Subhashini Venugopalan, Kate Saenko, and Trevor Darrell. 2015. Long-term recurrent convolutional networks for visual recognition and description. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2625--2634.Google ScholarCross Ref
- Yuki Endo, Kyosuke Nishida, Hiroyuki Toda, and Hiroshi Sawada. 2017. Predicting destinations from partial trajectories using recurrent neural network. In Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer, 160--172.Google Scholar
- 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 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM, 559--569.Google Scholar
- Zipei Fan, Xuan Song, Ryosuke Shibasaki, Tao Li, and Hodaka Kaneda. 2016. CityCoupling: Bridging intercity human mobility. In Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing. 718--728.Google Scholar
- Zipei Fan, Xuan Song, Tianqi Xia, Renhe Jiang, Ryosuke Shibasaki, and Ritsu Sakuramachi. 2018. Online deep ensemble learning for predicting citywide human mobility. Proceedings of the ACM Conference on Interactive, Mobile, Wearable, and Ubiquitous Technologies 2, 3 (2018), 1--21.Google Scholar
- Jie Feng, Yong Li, Chao Zhang, Funing Sun, Fanchao Meng, Ang Guo, and Depeng Jin. 2018. Deepmove: Predicting human mobility with attentional recurrent networks. In Proceedings of the World Wide Web Conference on World Wide Web. International World Wide Web Conferences Steering Committee, 1459--1468.Google Scholar
- Tianfu He, Jie Bao, Ruiyuan Li, Sijie Ruan, Yanhua Li, Li Song, Hui He, and Yu Zheng. 2020. What is the human mobility in a new city: Transfer mobility knowledge across cities. In Proceedings of the Web Conference. 1355--1365.Google Scholar
- Michiel Hermans and Benjamin Schrauwen. 2013. Training and analysing deep recurrent neural networks. In Advances in Neural Information Processing Systems. MIT Press, 190--198.Google Scholar
- Minh X. Hoang, Yu Zheng, and Ambuj K. Singh. 2016. Forecasting citywide crowd flows based on big data. In Procedings of the ACM International Conference on Advances in Geographic Information Systems (SIGSPATIAL’16).Google Scholar
- Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural Comput. 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. IEEE Trans. Intell. Transport. Syst. 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 Proceedings of the IEEE 24th International Conference on Data Engineering (ICDE’08). Ieee, 70--79.Google Scholar
- Renhe Jiang, Xuan Song, Zipei Fan, Tianqi Xia, Quanjun Chen, Qi Chen, and Ryosuke Shibasaki. 2018. Deep ROI-based modeling for urban human mobility prediction. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2, 1 (2018), 1--29.Google ScholarDigital Library
- Renhe Jiang, Xuan Song, Zipei Fan, Tianqi Xia, Quanjun Chen, Satoshi Miyazawa, and Ryosuke Shibasaki. 2018. Deepurbanmomentum: An online deep-learning system for short-term urban mobility prediction. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence.Google Scholar
- Tatsuya Konishi, Mikiya Maruyama, Kota Tsubouchi, and Masamichi Shimosaka. 2016. CityProphet: City-scale irregularity prediction using transit app logs. In Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM, 752--757.Google Scholar
- Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner. 1998. Gradient-based learning applied to document recognition. Proc. IEEE 86, 11 (1998), 2278--2324.Google ScholarCross Ref
- Zhenhui Li, Bolin Ding, Jiawei Han, Roland Kays, and Peter Nye. 2010. Mining periodic behaviors for moving objects. In Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 1099--1108.Google Scholar
- Defu Lian, Yong Ge, Fuzheng Zhang, Nicholas Jing Yuan, Xing Xie, Tao Zhou, and Yong Rui. 2018. Scalable content-aware collaborative filtering for location recommendation. IEEE Trans. Knowl. Data Eng. 30, 6 (2018), 1122--1135.Google ScholarCross Ref
- 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 Trans. Intell. Syst. Technol. 6, 1 (2015), 8.Google ScholarDigital Library
- Defu Lian, Kai Zheng, Yong Ge, Longbing Cao, Enhong Chen, and Xing Xie. 2018. GeoMF++ scalable location recommendation via joint geographical modeling and matrix factorization. ACM Trans. Info. Syst. 36, 3 (2018), 1--29.Google ScholarDigital Library
- Andy Liaw and Matthew Wiener. 2002. Classification and regression by randomForest. R News 2, 3 (2002), 18--22.Google Scholar
- Bin Liu, Yanjie Fu, Zijun Yao, and Hui Xiong. 2013. Learning geographical preferences for point-of-interest recommendation. In Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 1043--1051.Google ScholarDigital Library
- Qiang Liu, Shu Wu, Liang Wang, and Tieniu Tan. 2016. Predicting the next location: A recurrent model with spatial and temporal contexts. In Proceedings of the 30th AAAI Conference on Artificial Intelligence.Google ScholarDigital Library
- Yisheng Lv, Yanjie Duan, Wenwen Kang, Zhengxi Li, and Fei-Yue Wang. 2015. Traffic flow prediction with big data: A deep learning approach. IEEE Trans. Intell. Transport. Syst. 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. Transport. Res. Part C: Emerg. Technol. 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 Proceedings of the IEEE 12th International Conference on Data Mining (ICDM’12). IEEE, 1038--1043.Google Scholar
- Sinno Jialin Pan and Qiang Yang. 2010. A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22, 10 (2010), 1345--1359.Google ScholarDigital Library
- J. Ross Quinlan. 1987. Simplifying decision trees. Int. J. Man-Mach. Studies 27, 3 (1987), 221--234.Google ScholarDigital Library
- Sachin Ravi and Hugo Larochelle. 2017. Optimization as a model for few-shot learning. In Proceedings of the 5th International Conference on Learning Representations (ICLR'17)Google Scholar
- Salvatore Scellato, Mirco Musolesi, Cecilia Mascolo, Vito Latora, and Andrew T. Campbell. 2011. Nextplace: A spatio-temporal prediction framework for pervasive systems. In Proceedings of the International Conference on Pervasive Computing. Springer, 152--169.Google ScholarDigital Library
- Masamichi Shimosaka, Keisuke Maeda, Takeshi Tsukiji, and Kota Tsubouchi. 2015. Forecasting urban dynamics with mobility logs by bilinear Poisson regression. In Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM, 535--546.Google Scholar
- Richard Socher, John Bauer, Christopher D. Manning, et al. 2013. Parsing with compositional vector grammars. In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Vol. 1. 455--465.Google Scholar
- Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D. Manning, Andrew Ng, and Christopher Potts. 2013. Recursive deep models for semantic compositionality over a sentiment treebank. In Proceedings of the Conference on Empirical Methods in Natural Language Processing. 1631--1642.Google Scholar
- 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 Proceedings of the 29th AAAI Conference on Artificial Intelligence.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
- Min Xie, Hongzhi Yin, Hao Wang, Fanjiang Xu, Weitong Chen, and Sen Wang. 2016. Learning graph-based poi embedding for location-based recommendation. In Proceedings of the 25th ACM International on Conference on Information and Knowledge Management. ACM, 15--24.Google ScholarDigital Library
- Di Yao, Chao Zhang, Jianhui Huang, and Jingping Bi. 2017. SERM: A recurrent model for next location prediction in semantic trajectories. In Proceedings of the ACM Conference on Information and Knowledge Management. ACM, 2411--2414.Google Scholar
- Huaxiu Yao, Fei Wu, Jintao Ke, Xianfeng Tang, Yitian Jia, Siyu Lu, Pinghua Gong, Jieping Ye, and Zhenhui Li. 2018. Deep multi-view spatial-temporal network for taxi demand prediction. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence.Google Scholar
- Jing Yuan, Yu Zheng, and Xing Xie. 2012. Discovering regions of different functions in a city using human mobility and POIs. In Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 186--194.Google ScholarDigital Library
- Wei Zeng, Chi-Wing Fu, Stefan Müller Arisona, Simon Schubiger, Remo Burkhard, and Kwan-Liu Ma. 2017. Visualizing the relationship between human mobility and points of interest. IEEE Trans. Intell. Transport. Syst. 18, 8 (2017), 2271--2284.Google ScholarDigital Library
- Junbo Zhang, Yu Zheng, and Dekang Qi. 2017. Deep spatio-temporal residual networks for citywide crowd flows prediction. In Proceedings of the 31st AAAI Conference on Artificial Intelligence.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 ACM Conference on Ubiquitous Computing. ACM, 153--162.Google Scholar
- Yu Zheng, Licia Capra, Ouri Wolfson, and Hai Yang. 2014. Urban computing: Concepts, methodologies, and applications. ACM Trans. Intell. Syst. Technol. 5, 3 (2014), 1--55.Google ScholarDigital Library
Index Terms
- Transfer Urban Human Mobility via POI Embedding over Multiple Cities
Recommendations
What is the Human Mobility in a New City: Transfer Mobility Knowledge Across Cities
WWW '20: Proceedings of The Web Conference 2020With the advances of web-of-things, human mobility, e.g., GPS trajectories of vehicles, sharing bikes, and mobile devices, reflects people’s travel patterns and preferences, which are especially crucial for urban applications such as urban planning and ...
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 ...
Deep ROI-Based Modeling for Urban Human Mobility Prediction
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 ...
Comments