This involves the application of wireless networks, sensors, computational power, and data to improve the quality of densely populated areas.
[1][2] The term "urban computing" was first introduced by Eric Paulos at the 2004 UbiComp conference[3] and in his paper The Familiar Stranger[4] co-authored with Elizabeth Goodman.
What further differentiates urban computing from traditional remote sensing networks is the variety of devices, inputs, and human interaction involved.
In traditional sensor networks, devices are often purposefully built and specifically deployed for monitoring certain phenomenon such as temperature, noise, and light.
Urban computing connects unobtrusive and ubiquitous sensing technologies, advanced data management and analytics models, and novel visualization methods, to create win-win-win solutions that improve urban environment, human life quality, and city operation systems.Cities are more than a collection of places and people - places are continually reinvented and re-imagined by the people occupying them.
"[10] and the Cleveland Historical project which aims to create a shared history of the city by allowing people to contribute stories through their own digital devices.
[15] This discovery spurred a collaboration between the CDC and Google to create a map of predicted flu outbreaks based on this data.
In the context of urban computing, the ability to place proximity beacons in the environment, the density of population, and infrastructure available enables digitally facilitated interaction.
Urban computing can help select better driving routes, which is important for applications like Waze, Google Maps, and trip planning.
They solve the problems: one, not all road segments will have data from GPS in the last 30 minutes or ever; two, some paths will be covered by several car records, and it's necessary to combine those records to create the most accurate estimate of travel time; and three, a city can have tens of thousands of road segments and an infinite amount of paths to be queried, so providing an instantaneous real time estimate must be scalable.
They used various techniques and tested it out on 32670 taxis over two months in Beijing, and accurately estimated travel time to within 25 seconds of error per kilometer.
[8] Bicycle counters are an example of computing technology to count the number of cyclists at a certain spot in order to help urban planning with reliable data.
Placing displays at bus stops to give information is expensive, but developing several interfaces (apps, website, phone response, SMS) to OneBusAway was comparatively cheap.
[24] Based on the patterns and characteristics of a bicycle sharing system, the implications for data-driven decision supports have been studied for transforming urban transportation to be more sustainable.