Interesting post, and it's very nice that the author decided to share all the scripts. Forecasting the demand is the first fundamental step to actually optimize

Interesting post, and it's very nice that the author decided to share all the scripts. Forecasting the demand is the first fundamental step to actually optimize the rebalancing operations that usually happen overnight. There is a lot of academic and applied research in this field. Prof. David Shmoys (Cornell University) is working on optimising the rebalancing operations of the of CitiBike NYC (press article http://www.news.cornell.edu/stories/2015/01/cornell-research-steers-nyc-bikes-needy-stations http://www.news.cornell.edu/stories/2015/01/cornell-research...), Prof. Günther Raidl has been doing the same for a while for the CityBike Vienna (Austria) network (http://www.citylab.com/commute/2014/08/balancing-bike-share-stations-has-become-a-serious-scientific-endeavor/379188 http://www.citylab.com/commute/2014/08/balancing-bike-share-...), and three researchers from Udine (Italy), and Vienna (Austria), including myself, have been working on the same problem on similar data sets (summary paper http://link.springer.com/article/10.1007/s10601-015-9182-1 http://link.springer.com/article/10.1007/s10601-015-9182-1, or preprint http://www.tunnuz.net/documents/digaspero_rendl_urli_constraints2014.pdf http://www.tunnuz.net/documents/digaspero_rendl_urli_constra...).