@inproceedings{sinetal15,
	abstract = {Bike sharing systems consist of a fleet of bikes placed in a network of docking stations. These bikes can then be rented and returned to any of the docking stations after usage. Predicting unrealized bike demand at locations currently without bike stations is important for effectively designing and expanding bike sharing systems. We predict pairwise bike demand for New York City's Citi Bike system. Since the system is driven by daily commuters we focus only on the morning rush hours between 7:00 AM to 11:00 AM during weekdays. We use taxi usage, weather and spatial variables as covariates to predict bike demand, and further analyze the influence of precipitation and day of week. We show that aggregating stations in neighborhoods can substantially improve predictions. The presented model can assist planners by predicting bike demand at a macroscopic level, between pairs of neighborhoods.},
	author = {Divya Singhvi and Somya Singhvi and Peter I. Frazier and Shane G. Henderson and Eoin O' Mahony and David B. Shmoys and Dawn B. Woodard},
	booktitle = {Association for the Advancement of Artificial Intelligence Proceedings},
	date-added = {2016-01-10 19:19:05 +0000},
	date-modified = {2016-01-10 19:20:09 +0000},
	title = {Predicting bike usage for New York City's bike sharing system},
	url_paper = {pubs/AAAI15.pdf},
	year = {2015}}

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