Bike sharing systems are booming globally as a green and flexible transportationmode, but the flexibility also brings difficulties in keeping the bike stations balanced with enough bikes and docks. Understanding the spatio-temporal bike trip patterns in a bike sharing system, such as the popular trip origins and destinations during rush hours, is important for researchers to design models for bike scheduling and station management. However, due to privacy and operational concerns, bike trip data are usually not publicly available in many cities. Instead, the station feeds about real-time bike and dock number in stations are usually public, which we refer to as bike sharing system open data. In this paper, we propose an approach to infer the spatio-temporal bike trip patterns from the public station feeds. Since the number of possible trips (i.e., origin-destination station pairs) is much larger than the number of stations, we define the trip inference as an ill-posed inverse problem. To solve this problem, we identify the sparsity and locality properties of bike trip patterns, and propose a sparse and weighted regularization model to impose both properties in the solution. We evaluate our method using real-world data fromWashington, D.C. and New York City. Results show that our method can effectively infer the spatio-temporal bike trip patterns and outperform the baselines in both cities.
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Longbiao Chen received the BS degree in computer science from Zhejiang University (ZJU), China in 2010. He is currently pursuing the PhD degree in Department of Computer Science, ZJU and visiting Institut Mines-TELECOM / TELECOM Sud- Paris in France. His research interests are mainly in urban computing, big data applications, and ubiquitous computing.
Xiaojuan Ma is an assistant professor of human-computer interaction (HCI) at the Department of Computer Science and Engineering (CSE), Hong Kong University of Science and Technology (HKUST), China. She received the PhD degree in computer science at Princeton University, USA. She was a post-doctoral researcher at the Human-Computer Interaction Institute (HCII) of Carnegie Mellon University (CMU), USA and before that a research fellow in the Information Systems Department, National University of Singapore (NUS), Singapore. Before joining HKUST, she was a researcher of Human-Computer Interaction at Noah’s Ark Lab, Huawei Tech. Investment Co., Ltd. in Hong Kong, China.
Thi-Mai-Trang Nguyen is an associate professor at University Pierre and Marie Curie (Paris 6) and doing research at Laboratoire d’Informatique de Paris 6 (LIP6), France. She received the PhD degree in computer science from University of Paris 6, France in 2003. The PhD thesis was co-supervised and carried-out at Ecole Nationale Superieure des Telecommunications (ENST-Paris). From 2004 to 2006, She was a postdoctoral researcher at France Telecom in Rennes, France and at University of Lausanne, Switzerland. Her research interests include network architecture, network protocol design, and network data analytics.
Gang Pan received the BS and PhD degrees in computer science from Zhejiang University (ZJU), China in 1998 and 2004, respectively. He is currently a professor with the College of Computer Science and Technology, ZJU. He has published more than 90 refereed papers. He visited the University of California, USA during 2007 and 2008. His research interests include pervasive computing, computer vision, and pattern recognition.
Jérémie Jakubowicz received the MS and PhD degrees in applied mathematics from the Ecole Normale Supérieure de Cachan, France in 2004 and 2007, respectively. He was an assistant professor with Télécom ParisTech, France. Since 2011, he has been an assistant professor with Télécom Sud-Paris, Evry, France and an associate researcher with the CNRS, France. His current research interests include distributed statistical signal processing, image processing, and data mining.
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Chen, L., Ma, X., Nguyen, TMT. et al. Understanding bike trip patterns leveraging bike sharing system open data. Front. Comput. Sci. 11, 38–48 (2017). https://doi.org/10.1007/s11704-016-6006-4