Project: #34 User-centric interdependent urban systems: using energy use data and social media data to improve mobility Progress Report - Reporting Period Ending: March 30, 2018 Principal Investigator: Sean Qian Status: Active Start Date: July 1, 2017 End Date: June 30, 2018 Research Type: Basic Grant Type: Research Grant Program: FAST Act Grant Cycle: 2017 Mobility21 UTC Progress Report (Last Updated: March 25, 2018, 11:25 a.m.) % Project Completed to Date: 75 % Grant Award Expended: 75 % Match Expended & Document: 75 USDOT Requirements Accomplishments The congestion starting time and duration for all modes of transportation in the morning commute were predicted based upon their spatio-temporal relationship with energy use and social media activities. This information enables efficient transportation management strategies to optimally reduce congestion and emissions. A methodology was developed to estimate the travel time on main roads using hours-ahead energy data and/or social media data. Impacts 1. This research has been presented to the City of Pittsburgh, and Cranberry Township. The resultant models and tools have great potential to improve the accuracy of real-time traffic prediction. 2. This research was presented to three international conferences, TRB annual meeting, INFORMS annual meeting, and COTA international Conference of Transportation Professionals. 3. This research was presented to students events at both CMU and Univ of Pittsburgh, which has great potential to educate the next generation of engineers, economists and scientists. Other 1. A methodology estimates the travel time on main roads using hours-ahead energy data and/or social media data. 2. 2. An open-source web application implementing the methodology. The tools, programs and data will be made available in the public domain. New Partners PennDOT, Cranberry Township Issues n/a