Aims and Scope
Urbanization’s rapid progress has led to many big cities, which have modernized people’s lives but also engendered big challenges, such as air pollution, increased energy consumption and traffic congestion. Tackling these challenges can seem nearly impossible years ago given the complex and dynamic settings of cities. Nowadays, sensing technologies and large-scale computing infrastructures have produced a variety of big data in urban spaces, e.g. human mobility, air quality, traffic patterns, and geographical data. The big data implies rich knowledge about a city and can help tackle these challenges when used correctly.
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.
Some representative projects and literatures can be found from this website.
 Yu Zheng, Licia Capra, Ouri Wolfson, Hai Yang. Urban Computing: concepts, methodologies, and applications. ACM Trans. on Intelligent Systems and Technology. 2014
This workshop provides the professionals, researchers, and practitioners who are interested in sensing/mining/understanding urban data with a platform where they can discuss and share the state-of-the-art of urban computing development and applications, present their ideas and contributions, and set future directions in emerging innovative research for urban computing.
Topics of Interest
Topics of interest include, but not limited to, the following aspects :
Urban informatics: acquisition, aggregation, and analysis of big data
City-wide traffic modeling, visualization, analysis, and prediction
City-wide human mobility modeling, visualization, and understanding
Urban computing for urban planning and city configuration evaluation
Urban environment/pollution/energy consumption monitoring and data analysis
City-wide intelligent transportation systems
Anomaly detection and event discovery in a city
Social behavior modeling, understanding, and patterns mining in urban spaces
Discover regions of interests and regions of different functions
Mining public transportation data, such as ticketing data in bus and subway systems, road pricing data, and taxi data
City-wide mobile social applications in urban areas
Location-based social networks enabling urban computing scenarios
Smart recommendations in urban spaces
Intelligent delivery services in cities
Mining data from the Internet of Things in urban areas
Paper submission due: June 1, 2014
Paper Notification: June 20, 2014
Camera-ready due: July 1, 2014
Yu Zheng, Microsoft Research, China
Steven E. Koonin, New York University
Ouri E. Wolfson, University of Illinois at Chicago
Charlie Catlett, University of Chicago
Jiawei Han, University of Illinois at Urbana-Champaign