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  • The new MAC website is launched on April 15, 2023.



Data-driven large-scale network simulation and optimization


Understand inter-relations among various infrastructure systems

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Developing ITS through cutting-edge technologies


Design transportation infrastructure and operational schemes


Integrate multi-modal datasets to support holistic decision making of multi-modal transportation systems


Use economics instruments to incentivize travelers’ choices towards systems optimum


MAC trains the next generation of engineers, planners, and decision makers. We work with undergraduate and graduate students, teach executive-level professional courses in smart communities, and collaborate with non-profits on various STEM events and educational tool development for K-12 students.

The semester-long capstone course will allow students to engage with industry partners and develop a solution to real-world, data-driven issues.

Matching Parking Supply to Travel Demand Towards Sustainability: Cyber Physical Social Systems for Sensing Driven Parking


The Mobility Data Analytics Center (MAC) 

Over the last decade, new technologies and innovations in the transportation industry 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 an unprecedented opportunity for the transportation industry to understand characteristics of infrastructure and services, to learn travelers’ behavior, and ultimately provide efficient solutions to address challenges in mobility, safety, sustainability, and equity.


However, data sources are usually disparate among 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 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 transportation systems. The ultimate objective of MAC is to:


·    Provide archived and real-time traffic data of every element of multi-modal transportation systems;

·    Reveal the behavior information for both passenger transportation and freight transportation;

·    Serve as a key instrument for managing transportation systems.

·    Target a range of users including legislators, transportation planners, engineers, researchers, travelers, consultants, and technology companies.

MAC has been actively developing a centralized data engine supported by a web application to manage and analyze massive data. The engine has been customized and applied to many applications in transportation, e.g. predictive analytics for incident management, optimal curbside management, work zone safety improvement, first-/last-mile mobility services, and multi-modal transportation network design, just to name a few. The engine offers organization, visualization, and analytics of a wide array of mobility data, roadway, incidents, parking, public transit, weather, electric vehicles, social media, telematics, etc. Furthermore, the engine can translate the data into useful information for people who need it: legislators, transportation planners, engineers, researchers, travelers, and companies. MAC also works with private sector to develop travel-related products or service that ultimately improves travelers’ experience.

White Structure

Hamburg Hall 3044, Carnegie Mellon University

4800 Forbes Ave, Pittsburgh, PA, 15213



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