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March 13, 2017 / or4green

Bicycle Sharing Systems Virtual Seminar

Bicycle sharing systems are growing in urban areas.  Instead of using your own bicycle, you rent one for a short amount of time from one location, and return it to another.


by Marco Verch (Vélo Libre Service: Mieträder in Lille) [CC BY 2.0 (, via Wikimedia Commons


Source: U.S. Department of Transportation, Bureau of Transportation Statistics, Intermodal Passenger Connectivity Database (as of Feb. 2, 2016)

But they pose operational challenges.  For instance, suppose most people rent from location A and drop off at location B.  Some redistribution needs to occur.  Where should bicycles be located and how many at each location?  It sounds like a great problem for O.R. and Analytics, and indeed, it has already received considerable attention.  A quick search turns up an article in Operations Research and a post by Punk Rock OR blogger Laura McLay.

Now David Shmoys from Cornell and the Computational Sustainability group will be giving a virtual seminar on the topic on Friday, March 17.  (Some of his earlier work was described in this greenOR post from 2009.)

Information from the Computational Sustainability listserv follows:

We are pleased that David Shmoys, the Laibe/Acheson Professor at Cornell University in the School of Operations Research and Information Engineering, and also the Department of Computer Science at Cornell University, and currently the Director of the School of Operations Research and Information Engineering will be presenting the next talk in the Computational Sustainability Virtual Seminar Series.

Please register here to receive details on Zoom conferencing (it’s free!) for this 8th seminar in our series. Please distribute this email to interested others so they might register and encourage colleagues to attend the seminar by watching with you.
Friday, March 17, 2017

1:30-2:30 pm Eastern Time (17:30 UTC, 5:30 pm)

“Models and Algorithms for the Operation and Design of Bike-sharing Systems”

The sharing economy has helped to transform many aspects of our day-to-day lives, leveraging the IT revolution in increasingly novel ways. At the same time, the sharing economy presents new computational challenges to provide tools to support the operations of these emerging industries. Although perhaps not quite as visible in impact as Uber and Airbnb (and their competitors), bike-sharing systems have fundamentally changed the urban landscape as well. Even in a city as notoriously inhospitable to cycling as New York, Citibike has emerged as a significant player in the city’s transportation network, supporting more than 1.5 million rides per month for a subscriber base of more than 100,000 individuals. We have been working with Citibike to develop analytics and optimization models and algorithms to help manage this system. The key challenge is to cope with huge rush-hour usage that simultaneously creates stark shortages of bikes in some neighborhoods, and surpluses of bikes (and consequently, shortages of parking docks) elsewhere. We will explain how mathematical models can be used to answer questions such as, how should we position the fleet of bikes at the start of a day, and how should we mitigate the imbalances that develop?  We will survey the analytics we have employed for the former question, where we developed an approach based on continuous-time Markov chains combined with optimization models to compute daily stocking levels for the bikes, as well as methods employed for optimizing the capacity of the stations. For the question of mitigating the imbalances that result, we will describe algorithms that guide both mid-rush hour and overnight rebalancing, as well as for the positioning of corrals, which create “surge capacity” at stations, and have been one of the most effective means of creating adaptive capacity in the system

This is a survey of several papers, but will focus on joint work with Daniel Freund, Shane Henderson, and Eoin O’Mahony.



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