Important Dates

• Paper submission
• June 12, 2015 (PCT 11:59:59PM) Extended
• Notification of acceptance
• July 12, 2015 Extended
• Camera-ready copies due
• July 20, 2015
• Workshop date
• August 10, 2015

Chairs

• Program Chair

• Yu Zheng

• Microsoft Research, China

• General Co-Chairs
• Steven E. Koonin
• New York University
• Ouri E. Wolfson
• University of Illinois at Chicago
• Fang Chen

  NICTA, Australia

  Qiang Yang

  Hong Kong University of Science and Technology

   

  UrbComp 2012

  UrbComp 2013

  UrbComp 2014

• Keynote Speaker: Ramamohanarao Kotagiri, The University of Melbourn

  SMARTS: Scalable Microscopic Adaptive Road Traffic Simulator    

• Two invited talks

  Urban Computing: Using Big Data to Tackle Urban Challenges

  Yu Zheng, Microsoft Research, (MIT TR35; EiC of ACM TIST)

  Urban Insight from Data

  Fang Chen, NICTA

• The notification of acceptance has been extended to July 12 due to the extension of the submission deadline.
• The submission deadline has been extended to June 12, 2015.
• Selected full papers will be published at IEEE Transactions on Big Data!!
• Website launched (April 1, 2015) (1-Page Call For Paper)

Aims and Scope

Urbanization’s rapid progress has 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 [1] 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.

[1] 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

• Understanding urban economy based on big data

• Enhancing public safety and security using big 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