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Urban Computing


Urban computing is a process of acquisition, integration, and analysis of big and heterogeneous data generated by a diversity of sources in urban spaces, such as sensors, devices, vehicles, buildings, and human, to tackle the major issues that cities face, e.g. air pollution, increased energy consumption and traffic congestion. 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. Urban computing also helps us understand the nature of urban phenomena and even predict the future of cities.


Urban Air


Using a diversity of big data to infer and predict fine-grained air quality throughout a city, and finally tackle air pollutions. Many countries are suffering from air pollutions. Cities have built a few air quality monitoring stations to inform people urban air quality every hour. Influenced by multiple complex factors, however, urban air quality is highly skewed in a city, varying by locations significantly and changing over time differently in different places. Thus, we cannot infer the air quality of a location without a monitoring station by simply using a linear interpolation. The first step of this project is to infer the real-time and fine-grained air quality of arbitrary location by using the real-time and historical air quality data from existing monitoring stations and five additional data sources we observed in a city, consisting of meteorological data, traffic, human mobility, POIs, and road network data.



T-Drive: Driving Directions Based on Taxi Traces

GPS-equipped taxis can be regarded as mobile sensors probing traffic flows on road surfaces, and taxi drivers are usually experienced in finding the fastest (quickest) route to a destination based on their knowledge. In this project, we mine smart driving directions from the historical GPS trajectories of a large number of taxis, and provide a user with the practically fastest route to a given destination at a given departure time. We build our system based on a real world trajectory dataset generated by over 33,000 taxis in a period of 3 months, and evaluate the system by conducting both synthetic experiments and in-the-field evaluations. As a result, 60-70% of the routes suggested by our method are faster than the competing methods, and 20% of the routes share the same results. On average, 50% of our routes are at least 20% faster than the competing approaches.

TechFest 2010 Demo




GeoLife 2.0: Building social networks using human location history

GeoLife is a location-based social-networking service on Microsoft Virtual Earth. It enables users to share life experiences and build connections among each other using human location history.

Techfest 2009 Public Day Demo (Rank 10 out of 320+ demos)




GeoLife 1.0: Sharing Life Experiences Using GPS Traces

Techfest 2008 Public Day Demo (Rank 16 out of 250+ demos)



Photo2Search: Searching Maps Using Street-Side Photos

Photo2Search is a project that enables people to search for information by using a photo as a query. In this work, a user can take a snapshot of her surrounding building by using a camera-phone and send the photo to a backend system via MMS. Photo2Search will match the received photo against milllions of indexed street view images and find out the most similar one. Later, the information, such as the location and the sevices like the food quality, associated with this phone is returned to the user.

Techfest 2007 Public Day Demo (Rank 16 out of 250+ demos)


Magic Photo2search (in Chinese), China Internet Weekly, Apr. 2007