ABSTRACT
Dockless shared bikes, which aim at providing a more flexible and convenient solution to the first-and-last mile connection, come into China and expand to other countries at a very impressing speed. The expansion of shared bike business in new cities brings many challenges among which, the most critical one is the parking chaos caused by too many bikes yet insufficient demands. To allow possible actions to be taken in advance, this paper studies the problem of detecting parking hotspots in a new city where no dockless shared bike has been deployed. We propose to measure road hotness by bike density with the help of the Kernal Density Estimation. We extract useful features from multi-source urban data and introduce a novel domain adaption network for transferring hotspots knowledge learned from one city with shared bikes to a new city. The extensive experimental results demonstrate the effectiveness of our proposed approach compared with various baselines.
Supplemental Material
- Jie Bao, Tianfu He, Sijie Ruan, Yanhua Li, and Yu Zheng . 2017. Planning Bike Lanes based on Sharing-Bikes' Trajectories. (2017).Google Scholar
- Glen Van Brummelen . 2012. Heavenly Mathematics:The Forgotten Art of Spherical Trigonometry. Princeton University Press. 1--2 pages.Google Scholar
- Chris Burges, Tal Shaked, Erin Renshaw, Ari Lazier, Matt Deeds, Nicole Hamilton, and Greg Hullender . 2005. Learning to rank using gradient descent. In Proceedings of the 22nd ICML. ACM, 89--96. Google ScholarDigital Library
- Christopher JC Burges . 2010. From ranknet to lambdarank to lambdamart: An overview. Learning, Vol. 11, 23--581 (2010), 81.Google Scholar
- Christopher J Burges, Robert Ragno, and Quoc V Le . 2007. Learning to rank with nonsmooth cost functions. In NIPS. 193--200. Google ScholarDigital Library
- Zhangjie Cao, Mingsheng Long, Jianmin Wang, and Michael I Jordan . 2017. Partial Transfer Learning with Selective Adversarial Networks. (2017).Google Scholar
- Ronan Collobert, Jason Weston, Léon Bottou, Michael Karlen, Koray Kavukcuoglu, and Pavel Kuksa . 2011. Natural language processing (almost) from scratch. Journal of Machine Learning Research Vol. 12, Aug (2011), 2493--2537. Google ScholarDigital Library
- Martin Ester, Hans-Peter Kriegel, Jörg Sander, Xiaowei Xu, et almbox. . 1996. A density-based algorithm for discovering clusters in large spatial databases with noise. KDD, Vol. Vol. 96. 226--231. Google ScholarDigital Library
- Yaroslav Ganin and Victor Lempitsky . 2015. Unsupervised domain adaptation by backpropagation. International Conference on Machine Learning. 1180--1189. Google ScholarDigital Library
- Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, Franccois Laviolette, Mario Marchand, and Victor Lempitsky . 2016. Domain-adversarial training of neural networks. Journal of Machine Learning Research Vol. 17, 59 (2016), 1--35. Google ScholarDigital Library
- Nicolas Gast, Guillaume Massonnet, Daniël Reijsbergen, and Mirco Tribastone . 2015. Probabilistic forecasts of bike-sharing systems for journey planning Proceedings of the 24th CIKM. ACM, 703--712. Google ScholarDigital Library
- Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio . 2014. Generative adversarial nets. In Advances in neural information processing systems. 2672--2680. Google ScholarDigital Library
- G. E. Hinton . 2008. Visualizing High-Dimensional Data Using t-SNE. Vigiliae Christianae, Vol. 9, 2 (2008), 2579--2605.Google Scholar
- Minh X Hoang, Yu Zheng, and Ambuj K Singh . 2016. FCCF: forecasting citywide crowd flows based on big data Proceedings of the 24th SIGSPATIAL. ACM, 6. Google ScholarDigital Library
- Judy Hoffman, Sergio Guadarrama, Eric S Tzeng, Ronghang Hu, Jeff Donahue, Ross Girshick, Trevor Darrell, and Kate Saenko . 2014. LSDA: Large scale detection through adaptation. In NIPS. 3536--3544. Google ScholarDigital Library
- Sergey Ioffe and Christian Szegedy . 2015. Batch normalization: Accelerating deep network training by reducing internal covariate shift ICML. 448--456. Google ScholarDigital Library
- Neal Jean, Marshall Burke, Michael Xie, W Matthew Davis, David B Lobell, and Stefano Ermon . 2016. Combining satellite imagery and machine learning to predict poverty. Science, Vol. 353, 6301 (2016), 790--794.Google ScholarCross Ref
- KhosrowDehnad . 2012. Density Estimation for Statistics and Data Analysis. Technometrics, Vol. 29, 4 (2012), 495--495.Google Scholar
- Diederik Kingma and Jimmy Ba . 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014).Google Scholar
- Min Lin, Qiang Chen, and Shuicheng Yan . 2013. Network In Network. Computer Science (2013).Google Scholar
- Junming Liu, Qiao Li, Meng Qu, Weiwei Chen, Jingyuan Yang, Hui Xiong, Hao Zhong, and Yanjie Fu . 2015. Station site optimization in bike sharing systems. Data Mining (ICDM), 2015 IEEE International Conference on. IEEE, 883--888. Google ScholarDigital Library
- Junming Liu, Leilei Sun, Weiwei Chen, and Hui Xiong . 2016. Rebalancing Bike Sharing Systems: A Multi-source Data Smart Optimization Proceedings of the 22nd ACM SIGKDD. ACM, 1005--1014. Google ScholarDigital Library
- Mingsheng Long, Yue Cao, Jianmin Wang, and Michael Jordan . 2015. Learning transferable features with deep adaptation networks ICML. 97--105. Google ScholarDigital Library
- Mingsheng Long, Han Zhu, Jianmin Wang, and Michael I Jordan . 2016. Unsupervised domain adaptation with residual transfer networks NIPS. 136--144. Google ScholarDigital Library
- Christopher D Manning, Prabhakar Raghavan, Hinrich Schütze, et almbox. . 2008. Introduction to information retrieval. Vol. Vol. 1. Cambridge university press Cambridge. Google ScholarDigital Library
- Luis M Martinez, Lu'ıs Caetano, Tomás Eiró, and Francisco Cruz . 2012. An optimisation algorithm to establish the location of stations of a mixed fleet biking system: an application to the city of Lisbon. Procedia-Social and Behavioral Sciences Vol. 54 (2012), 513--524.Google ScholarCross Ref
- Sinno Jialin Pan, James T Kwok, and Qiang Yang . 2008. Transfer Learning via Dimensionality Reduction.. AAAI, Vol. Vol. 8. 677--682. Google ScholarDigital Library
- Sinno Jialin Pan, Ivor W. Tsang, James T. Kwok, and Qiang Yang . 2011. Domain Adaptation via Transfer Component Analysis. IEEE Transactions on Neural Networks Vol. 22, 2 (2011), 199--210. Google ScholarDigital Library
- Sinno Jialin Pan and Qiang Yang . 2010. A survey on transfer learning. IEEE Transactions on knowledge and data engineering, Vol. 22, 10 (2010), 1345--1359. Google ScholarDigital Library
- David Sculley . 2010. Combined regression and ranking. In Proceedings of the 16th ACM SIGKDD. ACM, 979--988. Google ScholarDigital Library
- Simon J Sheather and Michael C Jones . 1991. A reliable data-based bandwidth selection method for kernel density estimation. Journal of the Royal Statistical Society. Series B (Methodological) (1991), 683--690.Google Scholar
- Adish Singla, Marco Santoni, Gábor Bartók, Pratik Mukerji, Moritz Meenen, and Andreas Krause . 2015. Incentivizing Users for Balancing Bike Sharing Systems. AAAI. 723--729. Google ScholarDigital Library
- Alex Smola, Arthur Gretton, Le Song, and Bernhard Schölkopf . 2007. A Hilbert space embedding for distributions. In International Conference on Algorithmic Learning Theory. Springer, 13--31. Google ScholarDigital Library
- Nitish Srivastava, Geoffrey E Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov . 2014. Dropout: a simple way to prevent neural networks from overfitting. Journal of Machine Learning Research Vol. 15, 1 (2014), 1929--1958. Google ScholarDigital Library
- Michelle Toh . 2017. China's bike-sharing companies have hit a roadblock. http://money.cnn.com/2017/12/29/investing/china-bike-sharing-boom-bust/index.html?from=timeline. (2017).Google Scholar
- Eric Tzeng, Judy Hoffman, Ning Zhang, Kate Saenko, and Trevor Darrell . 2014. Deep domain confusion: Maximizing for domain invariance. (2014).Google Scholar
- Zidong Yang, Ji Hu, Yuanchao Shu, Peng Cheng, Jiming Chen, and Thomas Moscibroda . 2016. Mobility Modeling and Prediction in Bike-Sharing Systems Proceedings of the 14th Annual International Conference on Mobile Systems, Applications, and Services. ACM, 165--178. Google ScholarDigital Library
- Ming Zeng, Tong Yu, Xiao Wang, Vincent Su, Le T Nguyen, et almbox. . 2016. Improving Demand Prediction in Bike Sharing System by Learning Global Features. (2016).Google Scholar
Index Terms
- Where Will Dockless Shared Bikes be Stacked?: --- Parking Hotspots Detection in a New City
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