Tianfu He
Yu Zheng
ABSTRACT
With 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 business location selection. However, collecting a large set of human mobility data is not easy because of the privacy and commercial concerns, as well as the high cost to deploy sensors and a long time to collect the data, especially in newly developed cities. Realizing this, in this paper, based on the intuition that the human mobility is driven by the mobility intentions reflected by the origin and destination (or OD) features, as well as the preference to select the path between them, we investigate the problem to generate mobility data for a new target city, by transferring knowledge from mobility data and multi-source data of the source cities. Our framework contains three main stages: 1) mobility intention transfer, which learns a latent unified mobility intention distribution across the source cities, and transfers the model of the distribution to the target city; 2) OD generation, which generates the OD pairs in the target city based on the transferred mobility intention model, and 3) path generation, which generates the paths for each OD pair, based on a utility model learned from the real trajectory data in the source cities. Also, a demo of our trajectory generator is publicly available online for two city regions. Extensive experiment results over four regions in China validate the effectiveness of the proposed solution. Besides, an on-field case study is presented in a newly developed region, i.e., Xiongan, China. With the generated trajectories in the new city, many trajectory mining techniques can be applied.
- 2018. Mobility Transfer System. http://t.cn/EKKzTre.Google Scholar
- Jie Bao, Tianfu He, Sijie Ruan, Yanhua Li, and Yu Zheng. 2017. Planning bike lanes based on sharing-bikes’ trajectories. In KDD. ACM, 1377–1386.Google Scholar
- Louise Barkhuus and Anind K Dey. 2003. Location-Based Services for Mobile Telephony: a Study of Users’ Privacy Concerns.. In Interact, Vol. 3. Citeseer, 702–712.Google Scholar
- Karsten M Borgwardt, Arthur Gretton, Malte J Rasch, Hans-Peter Kriegel, Bernhard Schölkopf, and Alex J Smola. 2006. Integrating structured biological data by kernel maximum mean discrepancy. Bioinformatics 22, 14 (2006), e49–e57.Google ScholarDigital Library
- Nitesh V Chawla, Kevin W Bowyer, Lawrence O Hall, and W Philip Kegelmeyer. 2002. SMOTE: synthetic minority over-sampling technique. JAIR 16(2002).Google Scholar
- Erhan Erkut and Vedat Verter. 1998. Modeling of transport risk for hazardous materials. Operations research 46, 5 (1998), 625–642.Google Scholar
- Zipei Fan, Xuan Song, Ryosuke Shibasaki, Tao Li, and Hodaka Kaneda. 2016. CityCoupling: Bridging Intercity Human Mobility. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing (Heidelberg, Germany) (UbiComp ’16). ACM, New York, NY, USA, 718–728. https://doi.org/10.1145/2971648.2971737Google ScholarDigital Library
- 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 2018 World Wide Web Conference. International World Wide Web Conferences Steering Committee, 1459–1468.Google ScholarDigital Library
- Chuang Gan, Tianbao Yang, and Boqing Gong. 2016. Learning attributes equals multi-source domain generalization. In CVPR. 87–97.Google Scholar
- Muhammad Ghifary, W Bastiaan Kleijn, Mengjie Zhang, and David Balduzzi. 2015. Domain generalization for object recognition with multi-task autoencoders. In ICCV. 2551–2559.Google Scholar
- Thomas Grubinger, Adriana Birlutiu, Holger Schöner, Thomas Natschläger, and Tom Heskes. 2017. Multi-domain transfer component analysis for domain generalization. Neural processing letters 46, 3 (2017), 845–855.Google Scholar
- Chenjuan Guo, Bin Yang, Jilin Hu, and Christian Jensen. 2018. Learning to route with sparse trajectory sets. In ICDE. IEEE, 1073–1084.Google Scholar
- Tianfu He, Jie Bao, Ruiyuan Li, Sijie Ruan, Yanhua Li, Chao Tian, and Yu Zheng. 2018. Detecting Vehicle Illegal Parking Events using Sharing Bikes’ Trajectories.. In KDD. 340–349.Google Scholar
- Tianfu He, Jie Bao, Sijie Ruan, Ruiyuan Li, Yanhua Li, Hui He, and Yu Zheng. 2019. Interactive Bike Lane Planning using Sharing Bikes’ Trajectories. IEEE Transactions on Knowledge and Data Engineering (2019).Google Scholar
- Jeffrey Hood, Elizabeth Sall, and Billy Charlton. 2011. A GPS-based bicycle route choice model for San Francisco, California. Transportation letters 3, 1 (2011).Google ScholarCross Ref
- D. Huang, X. Song, Z. Fan, R. Jiang, R. Shibasaki, Y. Zhang, H. Wang, and Y. Kato. 2019. A Variational Autoencoder Based Generative Model of Urban Human Mobility. In 2019 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR). 425–430. https://doi.org/10.1109/MIPR.2019.00086Google 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
- George Rosario Jagadeesh and Thambipillai Srikanthan. 2014. Robust real-time route inference from sparse vehicle position data. In ITSC. IEEE, 296–301.Google Scholar
- Mehdi Katranji, Etienne Thuillier, Sami Kraiem, Laurent Moalic, and Fouad Hadj Selem. 2016. Mobility data disaggregation: A transfer learning approach. In ITSC. IEEE, 1672–1677.Google Scholar
- Ruiyuan Li, Huajun He, Rubin Wang, Yuchuan Huang, Junwen Liu, Sijie Ruan, Tianfu He, Jie Bao, and Yu Zheng. 2020. JUST: JD Urban Spatio-Temporal Data Engine. In ICDE. IEEE.Google Scholar
- Ruiyuan Li, Huajun He, Rubin Wang, Sijie Ruan, Yuan Sui, Jie Bao, and Yu Zheng. 2020. TrajMesa: A Distributed NoSQL Storage Engine for Big Trajectory Data. In ICDE. IEEE.Google Scholar
- Yuhong Li, Jie Bao, Yanhua Li, Yingcai Wu, Zhiguo Gong, and Yu Zheng. 2018. Mining the Most Influential k-Location Set from Massive Trajectories. IEEE Transactions on Big Data 4, 4 (2018), 556–570.Google ScholarCross Ref
- Ziheng Lin, Mogeng Yin, Sidney Feygin, Madeleine Sheehan, Jean-Francois Paiement, and Alexei Pozdnoukhov. 2017. Deep generative models of urban mobility. IEEE Transactions on Intelligent Transportation Systems (2017).Google Scholar
- Yiding Liu, Kaiqi Zhao, Gao Cong, and Zhifeng Bao. 2020. Online Anomalous Trajectory Detection with Deep Generative Sequence Modeling. In ICDE. IEEE.Google Scholar
- Zhaoyang Liu, Yanyan Shen, and Yanmin Zhu. 2018. Where Will Dockless Shared Bikes be Stacked?:—Parking Hotspots Detection in a New City. In KDD. ACM.Google Scholar
- Laurens van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-SNE. JMLR 9, Nov (2008), 2579–2605.Google Scholar
- Krikamol Muandet, David Balduzzi, and Bernhard Schölkopf. 2013. Domain generalization via invariant feature representation. In ICML. 10–18.Google Scholar
- Kun Ouyang, Reza Shokri, David S Rosenblum, and Wenzhuo Yang. 2018. A Non-Parametric Generative Model for Human Trajectories.. In IJCAI. 3812–3817.Google Scholar
- Sinno Jialin Pan, Ivor W Tsang, James T Kwok, and Qiang Yang. 2011. Domain adaptation via transfer component analysis. IEEE Transactions on Neural Networks 22, 2 (2011), 199–210.Google ScholarDigital Library
- Sinno Jialin Pan, Qiang Yang, 2010. A survey on transfer learning. TKDE 22, 10 (2010), 1345–1359.Google ScholarDigital Library
- Carlo Giacomo Prato and Shlomo Bekhor. 2007. Modeling route choice behavior: how relevant is the choice set composition?TRB 2003, 2003 (2007), 64–73.Google Scholar
- Sijie Ruan, Ruiyuan Li, Jie Bao, Tianfu He, and Yu Zheng. 2018. CloudTP: A Cloud-based Flexible Trajectory Preprocessing Framework. In 2018 IEEE 34th International Conference on Data Engineering (ICDE). IEEE, 1601–1604.Google Scholar
- Sijie Ruan, Cheng Long, Jie Bao, Chunyang Li, Zisheng Yu, Ruiyuan Li, Yuxuan Liang, Tianfu He, and Yu Zheng. 2020. Learning to generate maps from trajectories. AAAI.Google Scholar
- Ying Wei, Yu Zheng, and Qiang Yang. 2016. Transfer knowledge between cities. In KDD. ACM, 1905–1914.Google ScholarDigital Library
- Fei Wu and Zhenhui Li. 2016. Where did you go: Personalized annotation of mobility records. In CIKM. ACM, 589–598.Google Scholar
- Hao Wu, Ziyang Chen, Weiwei Sun, Baihua Zheng, and Wei Wang. 2017. Modeling trajectories with recurrent neural networks. IJCAI.Google Scholar
- Hao Wu, Jiangyun Mao, Weiwei Sun, Baihua Zheng, Hanyuan Zhang, Ziyang Chen, and Wei Wang. 2016. Probabilistic robust route recovery with spatio-temporal dynamics. In KDD. ACM, 1915–1924.Google ScholarDigital Library
- Zidong Yang, Ji Hu, Yuanchao Shu, Peng Cheng, Jiming Chen, and Thomas Moscibroda. 2016. Mobility modeling and prediction in bike-sharing systems. In Proceedings of the 14th annual international conference on mobile systems, applications, and services. ACM, 165–178.Google ScholarDigital Library
- Huaxiu Yao, Yiding Liu, Ying Wei, Xianfeng Tang, and Zhenhui Li. 2019. Learning from Multiple Cities: A Meta-Learning Approach for Spatial-Temporal Prediction. In The World Wide Web Conference. ACM, 2181–2191.Google ScholarDigital Library
- 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 AAAI.Google Scholar
- Jin Y Yen. 1971. Finding the k shortest loopless paths in a network. management Science 17, 11 (1971), 712–716.Google Scholar
- Jing Yuan, Yu Zheng, Chengyang Zhang, Xing Xie, and Guang-Zhong Sun. 2010. An interactive-voting based map matching algorithm. In MDM. IEEE Computer Society, 43–52.Google Scholar
- Nicholas Jing Yuan, Yu Zheng, Xing Xie, Yingzi Wang, Kai Zheng, and Hui Xiong. 2015. Discovering urban functional zones using latent activity trajectories. TKDE 27, 3 (2015), 712–725.Google ScholarDigital Library
- Desheng Zhang, Jun Huang, Ye Li, Fan Zhang, Chengzhong Xu, and Tian He. 2014. Exploring human mobility with multi-source data at extremely large metropolitan scales. In Proceedings of the 20th annual international conference on Mobile computing and networking. ACM, 201–212.Google ScholarDigital Library
- Yu Zheng, Licia Capra, Ouri Wolfson, and Hai Yang. 2014. Urban computing: concepts, methodologies, and applications. TIST 5, 3 (2014), 38.Google ScholarDigital Library
- Yuan Zhong, Nicholas Jing Yuan, Wen Zhong, Fuzheng Zhang, and Xing Xie. 2015. You are where you go: Inferring demographic attributes from location check-ins. In WSDM. ACM, 295–304.Google Scholar
Index Terms
- What is the Human Mobility in a New City: Transfer Mobility Knowledge Across Cities
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 ...
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 ...
Transfer Knowledge between Cities
KDD '16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data MiningThe rapid urbanization has motivated extensive research on urban computing. It is critical for urban computing tasks to unlock the power of the diversity of data modalities generated by different sources in urban spaces, such as vehicles and humans. ...
Comments