Articles with similar analysis goals:
Wang, W. (2016) >i>Forecasting bike rental
demand using New York Citi Bike data. A thesis submitted in fulfilment
of the requirements for the degree of MSc. In Computing (Data Analytics)
in the Dublin Institute of Technology, School of Computing College of
Science of Health, 2016.
Singhvi, Divya, Somya Singhvi, Peter I. Frazier, Shane G. Henderson,
Eoin O’Mahony, David B. Shmoys, and Dawn B. Woodard. ”Predicting Bike
Usage for New York City’s Bike Sharing System.” Predicting Bike Usage
for New York City’s Bike Sharing System (2015): 1-5.
People.orie.cornell.edu. School of Operations Research and Information
Engineering, Department of Computer Science. Cornell University, 2015.
Ib. 25 Nov. 2016.
<https://people.orie.cornell.edu/woodard/SingSingFraz15.pdf>.Copyright:
Association for the Advancement of Artificial Intelligence
O’Mahony, Eoin Daniel. ”Smarter Tools For (Citi)Bike Sharing.” Diss.
Cornell U, 2015. ECommons/Smarter Tools For (Citi)Bike Sharing.
Cornell University, 17 Aug. 2015. Ib. 27 Nov. 2016.
<http://hdl.handle.net/1813/40922>.
Deliverable: I intend to produce a statistical model which
outlines which factors are the greatest determinate of Citi-bike
commuter demand. Additionally, this conclusion will include a list of
Census tracts which are best candidates to receive a Citi-Bike station
in any future program expansion plans by the city, if commuting by
Citi-bike is a policy goal.