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NYU’s CUSP to Turn Hudson Yards into New York City’s First Smart Development

By David Sokol
May 02, 2014

By collecting data from advanced sensing tools, so-called smart cities are improving the delivery of municipal services. In northern Spain, the city of Santander installed 10,000 sensors to report events like streetlight outages, trashcan capacity, and even street trees’ thirstiness to an information hub at the University of Cantabria.

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Smart cities are achieving environmental efficiency gains as well. New York traffic engineers have begun making congestion adjustments in midtown Manhattan according to measurements taken from microwave sensors and E-ZPass tags, reducing rush-hour greenhouse gas emissions by 9 percent. Since 2009 residents of Dubuque, Iowa, have been optimizing their resource consumption thanks to meters that IBM installed in homes as part of Dubuque 2.0.

Call these and many other environmental data efforts the “Nest Effect,” referring to the Internet-enabled home-device company that Google purchased for $3.2 billion in January. Yet Nest’s namesake thermostat also suggests one of the drawbacks of this current wave of technology adoption. Most remote tools measure only one or two phenomena, and the devices themselves are largely retrofitted to existing structures. Installation of a multisensory system in ground-up development has been limited to small-scale undertakings like LED streetlight replacements, and then on the other end of the spectrum, to entirely new, large-scale places like China’s Taihu New City or Masdar in the United Arab Emirates.

Carving a space within this exclusive sphere, the Related Companies and Oxford Properties Group recently announced that it would make Hudson Yards the first “quantified community” in the United States. The 28-acre Hudson Yards, located mostly above the railroad yard of Manhattan’s Penn Station, is billed as the largest-ever private real estate project in America. The maiden building in this 17 million-square-foot mixed-use redevelopment will open next year.

While details are not yet final, Hudson Yards will be embedded with myriad data points monitoring an array of conditions, including air quality, energy consumption, recovery of recyclables, and occupant movements. Some measurements, such as personal health stats, will be collected via opt-in smartphone application. Related and Oxford are teaming with New York University’s Center for Urban Science and Progress (CUSP) on the rollout. CUSP deputy director Constantine Kontokosta initially proposed this program of tracking real-time environmental performance and occupant activity a year ago, as an alternative to a more complex plan in which CUSP would have retrofitted similar data points to existing buildings in Brooklyn.

According to Charlotte Matthews, global head sustainability of Related, the developers required little convincing to sign on. The initiative extends Related’s recent practice of “continuous commissioning” of its green buildings, she says, and leverages the building management systems that were already planned for Hudson Yards. Currently CUSP is determining the factors and behaviors to be observed as well as the exact types of technology required to gauge them.

Potential uses of the data are equally varied. Related may choose future HVAC packages based on the energy monitoring of Hudson Yards buildings’ different systems or, more immediately, standardize the operations of similar spaces according to observed best practice; in another example, pedestrian flow metrics can show retailers whether or not a particular promotion was successful. Matthews says that participants are particularly interested in the relationship between noise and other readings in light of Hudson Yards’ substitution of vacuum-tube disposal for garbage trucks.

CUSP, meanwhile, will disseminate data and analysis from its monitoring of Hudson Yards to wider industry and public-sector colleagues. Matthews, noting that “knowledge sharing is absolutely crucial,” says that big data could shift maxims of urban planning or revise assumptions that underlie energy modeling software.

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