User-Centric Interdependent Urban Systems: Using Energy Use Data and Social Media Data to Improve Mobility

The complex nature of interrelationships among various urban systems is central to smart cities. There may exist clear spatial and temporal correlations among usage patterns of all urban systems. The objective of this research is to fuse and analyze massive data from transportation, energy, and social media systems to discover the spatio-temporal correlations of usage patterns among those systems. Two questions are addressed using energy consumption data collected in the City of Pittsburgh and the Carnegie Mellon University campus. 1) What can be learned about the morning commute by studying households’ energy and social media use the night before, and how can the morning commute be best managed using information? 2) What can be learned about the evening commute by studying building energy use and social media activities during the daytime, and how can the evening commute be best managed using this information?

• Record URL:
  http://ppms.cit.cmu.edu/media/project_files/34_-_user-centric-report.pdf
• 
• Summary URL:
  http://ppms.cit.cmu.edu/projects/detail/34
• Supplemental Notes:
  • This document was sponsored by the U.S. Department of Transportation, University Transportation Centers Program.
• Corporate Authors:

  Technologies for Safe and Efficient Transportation University Transportation Center

  Carnegie Mellon University
  Pittsburgh, PA  United States  15213

  Carnegie Mellon University

  Department of Civil and Environmental Engineering
  5000 Forbes Avenue
  Pittsburgh, PA  United States  15213

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590

  University of Pennsylvania

  Philadelphia, PA  United States  19104
• Authors:
  • Qian, Zhen (Sean)
  • Zhang, Pinchao
  • Yao, Weiran
• Publication Date: 2018-10

Language

• English

Media Info

• Media Type: Digital/other
• Edition: Final Research Report
• Features: Figures; Maps;
• Pagination: 24p

Subject/Index Terms

• TRT Terms: Data analysis; Energy consumption; Logistic regression analysis; Logits; Social media; Traffic congestion; Traffic forecasting; Urban transportation
• Identifier Terms: Carnegie Mellon University
• Geographic Terms: Pittsburgh (Pennsylvania)
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; Planning and Forecasting;

Filing Info

• Accession Number: 01684088
• Record Type: Publication
• Contract Numbers: DTRT12GUTG11
• Files: UTC, TRIS, ATRI, USDOT
• Created Date: Oct 24 2018 11:19AM