| NSF Org: |
CNS Division Of Computer and Network Systems |
| Recipient: |
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| Initial Amendment Date: | September 16, 2015 |
| Latest Amendment Date: | September 16, 2015 |
| Award Number: | 1544826 |
| Award Instrument: | Standard Grant |
| Program Manager: |
David Corman
CNS Division Of Computer and Network Systems CSE Directorate for Computer and Information Science and Engineering |
| Start Date: | September 15, 2015 |
| End Date: | August 31, 2019 (Estimated) |
| Total Intended Award Amount: | $279,998.00 |
| Total Awarded Amount to Date: | $279,998.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
5000 FORBES AVE PITTSBURGH PA US 15213-3815 (412)268-8746 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
5000 Forbes Avenue Pittsburgh PA US 15213-3890 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | CPS-Cyber-Physical Systems |
| Primary Program Source: |
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| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.070 |
ABSTRACT
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 communications, 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 cyber-physical system consists of smart parking sensors, a parking and traffic data repository, parking management systems, and dynamic traffic flow control. If successful, the results of the 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. The research will provide interdisciplinary training to both graduate and undergraduate students. The results of this research also fills a void in our graduate transportation curriculum in which parking management gets little coverage. The investigators will organize an online short training course in Coursera and National Highway Institute to bring results to a broader audience. The investigators will also collaborate with Carnegie Museum of Natural History to develop an online digital map and related educational programs, which will be presented in the museum galleries during public events.
Technically, new theories, algorithms and systems for efficient management of transportation infrastructure through parking will be developed in this research, leveraging cutting-edge sensing technology, communication technology, big data analytics and feedback control. The 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 theory 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.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
This research project works with multi-year time-varying parking data, along with other system-level traffic related data, to demonstrate that we are able to better understand travelers? choices on parking locations based on parking availability, accessibility and prices. We show that parking availability, accessibility and prices may be updated over time of day to effectively reduce traffic congestion, emissions and energy consumption for the entire transportation networks. We developed and implemented a comprehensive parking management system enabling easy parking payment, effective parking reservation, dynamic pricing and 60-min ahead occupancy prediction, without necessarily deploying parking sensors. We designed a dynamic algorithm for optimizing the parking spot allocation for the reservation system. We utilized the features of parking spot reservations and greatly improve the computational efficiency of the algorithm. In addition, based on the parking reservation system, we also built a platform for efficient parking enforcement where crowdsourcing unpermitted parking can be reported by general public. We also built an application for the parking managers to view and resolve all the reports on unpermitted parking. We integrated the dynamic pricing mechanism into the parking reservation system such that the social benefits will be maximized. In the system, the parking rate is updated/controlled based on the parking demand forecast 30-60min ahead of time. The parking rate will go up if there are more reservations (or more parking demand) than the parking spots and go down if the opposite is true. The goal is to keep the number of reservations (or upcoming parking demand) approximately the same as the number of parking spots at any time, such that no cruising nor congestion is likely to occur.
With the support of this project, four papers were published in academic journals with five relevant presentations made to academic conferences. One provisional patent is filed. In addition, we have made over 10 presentations at national and international conferences to disseminate the results to various cities, communities and stakeholders. The research outcomes have been incorporated into a smart cities course developed by the PI that were taken by over 100 undergraduate and graduate students. We developed a digital interactive educational toolkit in the Future Thinking Lab with the Carnegie Museum of Natural History that shows how individual parking or traveling actions can be amplified through collective impacts. Information is available within the kiosk about how parking sensors are improving efficiency and reducing emissions for parking in Pittsburgh. We hosted a workshop to develop educational toolkits that connected work from this research to community concerns about air quality attributed to traffic congestion. Those educational toolkits will be used in many years to come, not only during events hosted by the Carnegie Museum of Natural History but also for K-12 local teachers to use in daily classes.
Last Modified: 11/18/2019
Modified by: Zhen (Sean) Qian
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