Archive for the ‘Bicing’ Category

IBM to Make Iowa City Smart(er)

Wednesday, October 21st, 2009

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It’s great to see major technology companies like Microsoft, Google and IBM place an emphasis on finding solutions to mitigate climate change. These companies have some very talented engineering staff that could likely make a big difference. Recently, IBM has poured a lot of money into marketing their “smarter cities” program. The website, unfortunately, reads like a giant heap of cleantech-utopia used-car salesman babble. “Safe neighborhoods. Quality schools. Affordable housing. Traffic that flows. It’s all possible…” with IBM! Case in point, this lovely vacuous pitch about IBM’s vision for “Smarter Cities.”



However, the New York Times recently detailed an IBM Smarter Cities program that is, apparently, more than just hype: they are starting a project in Dubuque, Iowa that, “over the next several years will use sensors, software and Internet computing to give the city’s government and citizens the digital tools to measure, monitor and alter the way they use water, electricity and transportation.”

From the article:

I.B.M. already has a number of computer-services projects with cities around the world, from traffic management systems in Stockholm and London to a smart-grid electricity system in Amsterdam, to water management in Shenyang, China. A goal in each is to conserve resources and reduce energy consumption and carbon emissions.

The Dubuque effort stands out not only because it is in the United States, but also because it marks I.B.M.’s most comprehensive approach to these digitally enhanced public services — water, electricity and transportation. “We’re trying to make Dubuque into the first integrated, smart city,” said Robert Morris, vice president of services research at I.B.M.

The benefits, Mr. Morris added, could well extend beyond water, electricity and transportation. For example, housing development and traffic management could be modeled and policies adopted for other goals like “making sure you have a walkable city.”

The first phase will involve installing digital water and electricity meters in 250 homes and businesses. The smart water meters include special low-flow sensing technology from a local manufacturer, A.Y. McDonald, that will help the public works department and residences reduce water use and detect leaks. An estimated 30 percent of households use water unnecessarily because of undetected leakage in faucets and toilets.

The smart electric meters will help households track their energy use and conserve. They will be able to tap into a Web site and, for example, set household temperatures a few degrees cooler in the winter or warmer in the summer — and model the savings in energy use and monthly bills.

Sounds very technocentric but worth keeping an eye on. In particular, the water sensing stuff seems very relevant to our recent work with HydroSense–a water sensing system that can identify water usage down to the source (e.g., dishwasher, kitchen sink). We have also begun looking at leak detection and identification.

“Smart cities” have recently also emerged as a topic of academic inquiry–the key idea being that traffic sensors, cameras, and even mobile phones all potentially provide data that can be used to understand and model the city. We did a bit of this work on shared bicycling–i.e., what does shared bicycling data reveal about a city? Marcus Foth has a book called Handbook of Research on Urban Informatics: The Practice and Promise of the Real-Time City, which is a collection of essays on “smart cities” research. The senseable city lab directed by Carlo Ratti is also a great place to check out for work in this area.

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Sensing and Predicting the Pulse of the City through Shared Bicycling

Friday, July 17th, 2009

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I just got done presenting at IJCAI09 on the shared bicycling research I conducted while a visiting researcher in the summer of 2008 at Telefonica Research in Barcelona, Spain. This is joint work with Joachim Neumann and Nuria Oliver (both of Telefonica Research). You can download the talk (PowerPoint slides) here.

Community shared bicycling programs offer an environmentally friendly, healthy, and inexpensive alternative to automobile transportation. Recent technological advances have led to a third generation shared bicycling system whose real-time usage data can be collected, archived, and analyzed. Currently, there are over forty such programs in the world including SmartBikeDC in Washington D.C. and Vélib’ in Paris, which has 20,000 bicycles and 1,450 stations (approximately 1 station every 300 meters). Barcelona’s shared bicycle program, Bicing, was launched in March of 2007. It currently has 390 stations with 6,000 bicycles and over 150,000 yearly subscribers.

Abstract
City-wide urban infrastructures are increasingly reliant on network technology to improve and ex-pand their services. As a side effect of this digitali-zation, 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.

Some pictures (with captions) from the talk:
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Our focus was on utilizing existing urban infrastructure to sense data about human behavior that is *freely* available (e.g., not proprietary data but data that we can freely access). In this case, we use shared bicycling usage to uncover spatiotemporal patterns of human mobility in the city of Barcelona.

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We have reached a pivotal point in time where city infrastructures are transitioning from mechanical/analog systems to digital systems thereby creating digital traces of human activity. Bruno Latour notes the potential to access the masses of data that are of the same order of magnitude as that of the natural sciences.

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Our main contributions were: (1) demonstrating the potential of using shared bicycling as a data source to gain insights into city dynamics and aggregated human be-havior; (2) exploring the relationship between spatiotemporal patterns of bicycle usage and underlying city behavior and geography; and (3) studying patterns in bicycle station usage, including the prediction of usage patterns and an analysis of how factors such as the time of the day affect this prediction.

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We obtained our data by scraping the bicing website once every two minutes. We downloaded station geolocation information as well as the number of current free parking spots and number of currently available bicycles.

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One of our motivations to explore prediction was the fact that 66% respondents to an online survey about Bicing stated that they had difficulty finding a free parking slot when trying to drop off a bicycle. This is a major impediment to Barcelona residents adopting Bicing as a primary form of transportation as searching for a station with a free parking spot takes time. Indeed, 50% of respondents avoid Bicing when they are traveling to a place where they must be on time.

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We used dendrogram clustering on station temporal usage data to see how Bicing usage patterns are shared across the city. We also explored how our prediction algorithms performed in relation to these clusters.

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One longterm goal of our work is to explore ways to make shared bicycling more self-sustainable. Current shared bicycling systems rely on trucks to load balance the bicycles (i.e., to make sure they are well distributed throughout the city). We are looking at ways to incentivize bicing users to drop off/pick up bicycles slightly out of their way to reduce the maintenance/operating overhead on the city. A mobile phone application could recommend a station close to a user’s final destination that is predicted to have a need for bicycles.

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This work would not have been possible without my colleagues Joachim Neumann and Nuria Oliver. Joachim, in particular, worked tirelessly on this project for six months and was absolutely essential to many parts of the project including data logging, model building, and evaluation.

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Shared Bicycling Visualizations

Tuesday, December 2nd, 2008

We have worked extensively with Barcelona’s shared bicycling system to understand and predict human mobility patterns (see some preliminary work in our UrbanSense08 paper, the presentation may be more interesting to the lay reader). This work was done in collaboration with Joachim Neumann and Nuria Oliver at Telefonica Research in Barcelona, Spain. Joachim, Nuria, and I have expanded this work significantly since the UrbanSense08 publication.

More broadly, in our research group at UW, we are interested in thinking about how existing urban infrastructures can be utilized to learn about human behavior and use that information to promote more sustainable activities. As urban infrastructures such as traffic lights, parking meters, electric meters, etc. become digitized, they provide the opportunity to be sensed and analyzed on a massive scale. This can not only reveal otherwise imperceptible qualities about human behavior, but also be used to feedback to citizens so that they have a better understanding of their cities and its underlying dynamics. As it currently stands, these Bicing visualizations tend to be visually interesting but not motivated by a specific objective. One key question for us is how can we use this dataset to promote shared bicycling on a person-to-person level (e.g., by making real-time mobile applications that tell users where the best bike route is to the next free station close to their destination, via a facebook plugin that uses social competition to drive usage, etc.) as well as how can share our findings with shared bicycling operators to optimize their systems?

Fabien Giradin made one of the first, and most famous, visualizations of the Bicing data.

From Fabien’s website:

Bicing is a community bicycle rental service in Barcelona (similar to the Vélo’v service in Lyon and Vélib’ in Paris). The stations deployed in the city offer bikes people can use for their small and medium daily routes within the city (max 30min). As part our Tracing the Visitor’s Eye project, we collected minute-by-minute data on the infrastructure status (i.e. number of available bikes for each station) over a weekend. The resulting animation shows the spatio-temporal state of the system and the mobility patterns of its users. For instance, it reveals a quantity of “cyclists” hanging out at the beach on Sunday afternoon and then returning downtown in the evening (video below).

Here is a new Bicing visualization by BCNoids, which extends Fabien’s visualizations into more sophisticated renderings such as topographical heatmaps:


BCNoids Reel from enrique soriano on Vimeo.

From Flowing Data:

Taking advantage of the data generated by Barcelona’s community bicycle program Bicing, BCNoids aims to explore the movement of 6000 bikes across a network of 400 stations. Developed in VVVV, the research draws inspiration from Craig Reynolds’ 1980s experiments with simulated flocking behviour and aspires to deliver “a tool for the analysis of human mobility patterns”.

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