Team: Sean Qian (PI, CMU), Michael Zhang (Co-PI, UCDavis), Ram Rajagopal (CO-PI, Stanford), Shuguan Yang (CMU)
Funding source: Cyber-physical systems program, NSF
Start/End Time:  2015-2018

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Parking can take up a significant amount of the trip costs (time and money) in urban travel. As such, it can considerably influence travelers’ choices of modes, locations, and time of travel. The advent of smart sensors, wireless communication, social media and big data analytics offers a unique opportunity to tap parking’s influence on travel to make the transportation system more efficient, cleaner, and more resilient. A cyber physical social system for parking is proposed to realize parking’s potential in achieving the above goals. This CPS consists of smart parking sensors, a parking and traffic data repository, parking management systems, and dynamic traffic flow control. If successful, the results of our investigation will create a new paradigm for managing parking to reduce traffic congestion, emissions and fuel consumption and to enhance system resilience. These results will be disseminated broadly through publications, workshops and seminars.

Our research probes massive individualized and infrastructure based traffic and parking data to gain a deeper understanding of travel and parking behavior, and develops a novel reservoir-based network flow model that lays the foundation for modeling the complex interactions between parking and traffic flow in large-scale transportation networks. The theories will be investigated at different levels of granularity to reveal how parking information and pricing mechanisms affect network flow in a competitive market of private and public parking. In addition, this research proposes closed-loop control mechanisms to enhance mobility and sustainability of urban networks. Prices, access and information of publicly owned on-street and off-street parking are dynamically controlled to: a) change day-to-day behavior of all commuters through day-to-day travel experience and/or online information systems; b) change travel behavior of a fraction of adaptive travelers on the fly who are aware of time-of-day parking information and comply to the recommendations; and c) influence the market prices of privately owned parking areas through a competitive parking market.

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