Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Towards automatic extraction of event and place semantics from flickr tags
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Media sharing based on colocation prediction in urban transport
Proceedings of the 14th ACM international conference on Mobile computing and networking
Digital Footprinting: Uncovering Tourists with User-Generated Content
IEEE Pervasive Computing
Spatial-temporal understanding of urban scenes through large camera network
Proceedings of the 1st ACM international workshop on Multimodal pervasive video analysis
Inferring usage characteristics of electric bicycles from position information
Proceedings of the 3rd International Workshop on Location and the Web
Proceedings of 1st international symposium on From digital footprints to social and community intelligence
Prediction of urban human mobility using large-scale taxi traces and its applications
Frontiers of Computer Science in China
The hidden image of the city: sensing community well-being from urban mobility
Pervasive'12 Proceedings of the 10th international conference on Pervasive Computing
A data-driven approach for convergence prediction on road network
W2GIS'13 Proceedings of the 12th international conference on Web and Wireless Geographical Information Systems
From taxi GPS traces to social and community dynamics: A survey
ACM Computing Surveys (CSUR)
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City-wide urban infrastructures are increasingly reliant on network technology to improve and expand their services. As a side effect of this digitalization, large amounts of data can be sensed and analyzed to uncover patterns of human behavior. In this paper, we focus on the digital footprints from one type of emerging urban infrastructure: shared bicycling systems. We provide a spatiotemporal analysis of 13 weeks of bicycle station usage from Barcelona's shared bicycling system, called Bicing. We apply clustering techniques to identify shared behaviors across stations and show how these behaviors relate to location, neighborhood, and time of day. We then compare experimental results from four predictive models of near-term station usage. Finally, we analyze the impact of factors such as time of day and station activity in the prediction capabilities of the algorithms.