The world's urban population is growing -- leaving cities to face a variety of challenges, ranging from environmental pollution to providing community health to safety. This doctoral seminar course focuses on understanding and undertaking urban challenges using computing technologies. Cities and their residents produce large scale traces of data including communication data, social proximity data, or mobility data. This course covers three major areas on (1) understanding how these traces are generated, managed, and processed; (2) how they can be modeled to address urban challenges; and (3) what are the ethical issues associated with it. This is a project-based, hands-on course which involves applying solutions from state-of-the-art literature to address a real-world problem. A number of guest lectures will provide additional perspectives to the discussions of the class.


This course does not have any formal prerequisites. However, this is a doctoral seminar course which assumes critical thinking, high ambition, and desire to learn and being challenged with new topics.


We will be reading from a large number of articles. Electronic copies of all papers will be posted on CourseWeb



Thursday 12:30-2:00 pm, or by appointment, Room 709 (135 North Bellefield Avenue)



12017-08-30Introduction and overview
  • UbiComp in the urban frontier.
  • The familiar stranger: anxiety, comfort, and play in public places.
  • Urban Computing: concepts, methodologies, and applications
introduction to course, instructor, and students
course overview and logistics
introduction to Urban Computing and its foundation
22017-09-06Work session 1: Urban sensing
  • A review of urban computing for mobile phone traces: current methods, challenges and opportunities
  • Daily travel behavior: lessons from a week-long survey for the extraction of human mobility motifs related information
  • Urban sensing: Out of the woods
What methods are used to collect data?
What sources are being used to collect data?
Challenges of collecting urban data
32017-09-13Instructor out of town
    Meet to discuss the improvement of the Wikipedia article on Urban Computing
    42017-09-20Guest lecture 1: Bob Gradeck and David Walker
    from Western PA Regional Data Center
      Introduction to WPRDC activities
      Available datasets and potential challenges targeted by WPRDC
      Privacy issues associated with each dataset
      52017-09-27Work session 2: Urban data representation
      Guest lecture 2: Dr. Stephen Hirtle
      School of Computing and Information
      • SPUD - Semantic processing of urban data
      • A survey of traffic data visualization
      • Illuminating LEGOs with digital information to create urban data observatory and intervention simulator
      Guest lecture on urban routes and urban navigation
      Processing of urban data
      What methods and approaches are used to represent urban data?
      Visualization methods for urban data
      62017-10-04Instructor out of town
        Wikipedia working session
        72017-10-11Work session 3: modeling
          Modeling linked data
          Features of urban data
          82017-10-18Guest Lecture 3: Dr. Konstantinos Pelechrinis
          Associate Professor
          School of Computing and Information
            Analytical techniques to mine urban data
            Urban shared transportation
            92017-10-25Work session 4: modeling
            • Dissecting urban noises from heterogeneous geo-social media and sensor data
            Approaches to modeling of urban data
            Challenges of modeling urban data
            102017-11-01Work session 5: Privacy and Ethics
            • Participatory privacy in urban sensing
            • Protecting privacy in public? Surveillance technologies and the value of public places
            • Smart environments \& the convergence of the veillances: Privacy violations to consider
            What are the potential privacy risks?
            What are the ethical concerns?
            Approaches in addressing ethical concerns
            Impact of surveillance technologies
            112017-11-08Guest Lecture 4: Dr. Stephen Smith
            Research Professor, Robotics
            Carnegie Mellon University
              Smart Infrastructure for Urban Mobility
              Adaptive traffic signal control
              122017-11-15Work session 6: project
                Work on IEEE submission
                132017-11-22Thanksgiving break
                  No class
                  142017-11-29Work session 7: project
                    Deadline for IEEE submission on Social Computing for Smart Cities
                    152017-12-06Work session 8: project
                      Finishing touches on the project
                      162017-12-13Final project session
                        Open presentation to the school

                        COURSE POLICIES

                        Academic Integrity: You are expected to be fully aware of your responsibility to maintain a high quality of integrity in all of your work. All work must be your own, unless collaboration is specifically and explicitly permitted as in the course group project. Any unauthorized collaboration or copying will at minimum result in no credit for the affected assignment and may be subject to further action under the University Guidelines for Academic Integrity. You are expected to have read and understood these Guidelines. A document discussing these guidelines was included in your orientation materials.

                        Attendance: Class attendance, while not mandatory, is required if you want to succeed in this course, especially since the course does not have any course book and involves a lot of in-class discussions. The class participation credit is engineered to encourage your attendance.

                        Concerning Students with Disabilities: If you have a disability for which you are or may be requesting an accommodation, you are encouraged to contact both your instructor and Disability Resources and Services, 216 William Pitt Union, (412) 648-7890/(412) 383-7355 (TTY), as early as possible in the term. DRS will verify your disability and determine reasonable accommodations for this course.

                        An important note on plagiarism: Cheating and plagiarism will not be tolerated. Students caught cheating or plagiarizing will receive no credit for the assignment on which the cheating occurred. Additional actions -- including assigning the student a failing grade in the class or referring the case for disciplinary action -- may be taken at the discretion of the instructors. You may incorporate excerpts from publications by other authors, but they must be clearly marked as quotations and properly attributed. You may obtain copy editing assistance, and you may discuss your ideas with others, but all substantive writing and ideas must be your own or else be explicitly attributed to another, using a citation sufficiently detailed for someone else to easily locate your source.