Projects

Mobility Data Analytics Center


Source Organization:
University:Carnegie Mellon University
Principal Investigator:Sean Qian
PI Contact Information:seanqian@cmu.edu
Project Manager:Courtney Ehrlichman
Funding Source(s) and Amounts Provided (by each agency or organization):$85,000
Total Dollars:
Agency ID/Contract/Grant Number:
Start and End Dates:January 2016 - January 2017
Project Status:Active
Subject Categories:Mobility Analytics
Abstract:Mobility Data Analytics Center aims at developing a centralized data engine supported by a web application to manage and analyze multi-jurisdictional multi-modal data for safety, mobility and sustainability, using the City of Pittsburgh and the City of Philadelphia as case studies. This research is a continuation of UTC funded research ‘Mobility Data Analytics Center’ in the years of 2014 and 2015, where we have built models and prototype tools to analyze various sources of data for public transit, parking, and roadway. In this continuation for the next two years, we set our focus on the following four aspects. First, maintenance and enhancement of the data engine and web application. We continue to collect and archive up-to-date data from various data providers in the Pittsburgh region and enhance the web application. Second, we establish a regional multi-modal trip planner (MUMTIP) incorporating a regional truck adaptive routing and scheduling system (TARS). Prediction of travel time and crash risk is central to MUMTIP. Third, we use I-79 corridor from Pittsburgh to Morgantown as a case study to illustrate the mobility impact of autonomous vehicle adaptation. In particular, we consider two possible highway settings (designated vehicle lanes with less required lane width, and existing highway setting) and various autonomous vehicle penetration rates. Fourth, we will build a sophisticated multi-modal transportation network model (MUMNET) for the Pittsburgh region that describes individual travel activities on roadway systems, transit systems and parking systems. The multi-modal network model is the key to systematic planning and operations of transportation infrastructure.
Describe Implementation of Research Outcomes (or why not implemented):
Impacts/Benefits of Implementation (actual, not anticipated):We plan to seek both industrial and federal funding for implementation based on the initial development. While we focus on several particular applications (transit and roadway) to demonstrate the method and leverage our resources, the methodology can be broadly applicable and scalable to other cities or regions, and Mobility Data Analytics Center can interact with other urban systems in the long run, such as water/sewer system, energy system, air quality, etc. This generality will attract attentions from various groups interested in smart infrastructure, green design, and environmental policies. Potential funding agencies/collaborators include the Department of Energy, Department of Transportation, Federal Highway Administration, National Science Foundation, National Institute of Standards and Technology, and Environmental Protection Agency. Local government, communities and foundations including the Benedum Foundation are also potential funding sources. The proposed research is closely related to on-going research at Carnegie Mellon in the context of data science, such as air quality studies, climate change, connected vehicles, autonomous vehicles, energy policies, and infrastructure life cycle analysis. Interactions with those groups will have synergistic effects.
Project URL: