A Solution for Community-Based Parking: Vehicle Information Systems

                                                                               Ultrasonic sensor on car detects parked cars and vacant spaces.

Invention Summary:

Searching for available on-street parking is a persistent urban challenge, leading to increased congestion, wasted time, and higher emissions. 

Creating a spot-accurate map of parking availability challenges GPS location accuracy limits. To address this need, inventors at Rutgers University have devised an environmental fingerprinting approach to achieve improved location accuracy. Based on 500 miles of road-side parking data collected over two months, they found that parking spot counts are 95% accurate and occupancy maps can achieve over 90% accuracy. Finally, they quantified the amount of sensors needed to provide adequate coverage in a city. Using extensive GPS traces from over 500 San Francisco taxicabs, it was demonstrated that if ParkNet were deployed in city taxicabs, the resulting mobile sensors would provide adequate coverage and be more cost-effective by a factor of roughly 10-15 when compared to a sensor network with a dedicated sensor at every parking space, as is currently being tested in San Francisco.

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Market Application:

• Automobile Manufacturers  

• Parking information aggregators and providers 

• Navigation Systems and Applications (in automobiles and mobile phones) 

• Parking lot management Systems 

• Automobile component, sensors and technologies 

Advantages:

• Low-cost, time-saving method, cost effective user, less traffic in big cities, less pollution, less illegal parking,  

• Does not require installation of external sensors into parking spaces. 

Intellectual Property & Development Status:  

1 patent family with 8 issued U.S. Patents and 1 published application: US9123245B2, US9564052B2, US10043389B2, US10657815B2, US11113966B2, US11663916B2, US11984030B2, US11995986B2, US20210398425A1 

Available for licensing and/or research collaboration. For any business development and other collaborative partnerships contact marketingbd@research.rutgers.edu

Relevant Publications:

• Suhas Mathur, Sanjit Kaul, Marco Gruteser, Wade Trappe. ParkNet: Harvesting Real-Time Vehicular Parking Information Using a Mobile Sensor Network. The S3 Workshop at the 10th ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc), 2009.
• Suhas Mathur, Tong Jin, Nikhil Kasturirangan, Janani Chandrashekharan, Wenzhi Xue, Marco Gruteser and Wade Trappe, "ParkNet: Drive-by Sensing of Road-side Parking Statistics", Proceedings of the 8th ACM/USENIX Annual International Conference on Mobile Systems, Applications and Services (MobiSys), to appear 2010.