MAC News

About MAC

Over the last decade, new technologies and innovations in transportation systems have produced massive amounts of data, which has enabled us to better monitor, evaluate and manage our transportation systems. The rich data from various sources provides unprecedented opportunity for the transportation industry to understand travel behavior and to propose efficient management strategies. However, those data sources are usually established by disparate public agencies and private companies. They rarely communicate with each other and as a result, data is only used and analyzed for a particular piece of the transportation system, such as an intersection, a stretch of freeway or bus routes operated by the same agency. With disparate data sources, each part of the system is individually operated and clearly the entire transportation system is far from being socially optimal.

The Mobility Data Analytics Center (MAC) aims to collect, integrate and learn from the massive amounts of mobility data and contribute to the development of smarter multi-modal multi-jurisdictional transportations systems. The ultimate objective of MAC is to,

MAC is developing a centralized data engine supported by a web application to manage and analyze massive data. The data engine essentially sets protocols for data exchange from various sources, and is necessary to accommodate the needs of data fusion and analytics. The engine offers organization, visualization and analytics of a wide array of mobility data, roadway, incidents, parking, public transit, weather, electric vehicles, mobile, etc. Furthermore, the engine can translate the data into useful information for people who need it: legislators, transportation planners, engineers, researchers, travelers, and companies. Unlike the traditional single computer stand-alone software or tools for data preparation and decision making, the data engine is accessed by users through web-based data sharing and browser-based human-computer interaction. The web application visualizing data and recommending decisions serves the front end of the data engine.

We are now working with various deployment partners to conduct research on mobility data analytics, and to develop decision making tools for facilitating transportation system management. We also work with private sector to develop travel-related products or service that ultimately improves travelers’ experience.


Director: Sean Qian
Porter Hall 111
Carnegie Mellon University
5000 Forbes Ave., Pittsburgh, PA, 15213
Director's email: