The Swivl is like having your own personal cameraman!
Just stick your iPhone, Android or camera in the stand, and it’ll follow your every move via a sensor that you wear!
Shoot videos of yourself or your friends without worrying about who’s going to hold the camera.
As the web and urban continue to collide and build on each other, post-industrial concerns like parking will be managed in very different ways. Instead of the 20th century hunter/gatherer model — where people search for empty spaces to park — we’ll see hotel reservation models, autonomous vehicles parking themselves, and dynamic pricing algorithms:
The Networked Urban Environment - Jan Chipchase via design mind
Urban infrastructures are increasingly being equipped with sensors and other means of collecting information and channeling our everyday actions, from energy use to parking patterns, into software and networks that analyze data and act upon it. Cities—and communities— are becoming “smarter” as “the internet of things” evolves. What this means is that more and more people and things, including parking spaces are becoming connected, allowing for better prediction models of traffic and energy usage thanks to real-time data flows, leading to better awareness of current resource statuses and more practical matters such as more dependable payment mechanisms.
The smart-parking scenarios will arrive more quickly than you think—in fact, they’re already nearly here. On the most basic level, anyone can get free driving directions and an instant, estimated time of arrival from Google Maps, when they agree to share where they are at a given moment via GPS. Throughout Europe now, you can reserve public parking spots via SMS messages. In San Francisco, you can time a meeting so that you don’t pay peak-prices for parking, determined by a dynamic market pricing system launched as a pilot program this fall (and running through summer 2012) by the San Francisco Municipal Transportation Agency to help alleviate congested streets. It uses real-time data tracking to determining the cost of parking at 7,000 of San Francisco’s 28,000 metered spots, as well as 12,250 spaces in three-quarters of the parking garages owned by the cities.
And then there are much more intricate examples, on epic scales. In September, the technology company Pegasus Holdings announced it is building a $200 million test city on a city scale in New Mexico—from scratch, where it will try out networked parking and transportation systems among other infrastructure innovations. In Asia and the Middle East, smart cities are being built from scratch: Tianjin Eco City in China; Songdo, South Korea; and Masdar in Abu Dhabi. In each of these examples, developers are working to implement traffic-solutions that will make use of new, networked technologies, all as part of creating more energy-efficient communities.
These optimistic visions aren’t just about making parking a more pleasant experience. They’re largely about solving urgent problems in a time of economic and sustainability-related challenges. According to a report by IBM, the economic impact of traffic congestion is $4 billion per year in New York alone, in terms of estimated lost work hours, pollution-related costs, and wasted fuel. In the United States, traffic congestion losses are growing at 8 percent a year, the most recent estimate being $78 billion in 2005. Worldwide, in both developed and developing-world cities, traffic congestion-related expenses represent between 1 percent and 3 percent of most cities’ GDP.
And on a larger scale, beyond parking and traffic, a recent report by Ericsson (published earlier this year) found that the more networked, or “smart,” a city is, the more that city sees benefits to its “triple bottom line” (its financial, societal, and sustainability-related successes). For every 10 percentage points increase in broadband penetration, the report found, the isolated economic effect on GDP growth is approximately 1% of GDP.
As I wrote about not long ago, the percentage of major cities given over to parking (and cars in general) is preposterous. All these schemes for dealing with parking of cars are transitional, because ultimately the payback for eliminating parking is so high that cities will eliminate cars, or change them into something so different they drastically diminish parking (like stackable, foldable, autonomous cars).
MassMotion puts a large, intelligent, 3-D crowd into a building’s design and finds out where you need bigger doors or more escalators.
Humans react in a seemingly irrational manner during emergency evacuations; sometimes, people even get trampled in the process. But what if architects and developers could predict how large crowds might move through their buildings in the event of an emergency—and then tweak the designs to ensure that everything runs smoothly?
That’s the premise of MassMotion, a crowd simulator that allows users to view hundreds of thousands of simulated 3-D people moving through urban environments (i.e. train stations and airports), all by programming each individual with a distinct personality and agenda.
The city’s Metropolitan Transportation Authority has been trying to provide a better sense of predictability in recent years by adding displays in stations that state when the next train is expected. Now, a Web development firm called Densebrain says that it can do the same thing at practically no cost, by analyzing how people lose phone service when they head underground.
Urban planners, technology companies and officials from local governments see potential in projects like these that mine data collected from phones to provide better public services.
Boston is developing a system called Street Bump that uses a smartphone’s accelerometer and GPS system to detect when a driver hits a pothole and then sends that information to city officials.
Techniques like this may help cities collect data that until recently would have required expensive network sensors.
Interesting idea: the intelligent devices we carry around become a grid of sensors, a shifting network of points. But the network is persistent, and plays the role of a pervasive and ubiquitous matrix collecting data and providing contextualized information.