Smart On-Street Parking System to Predict Parking Occupancy and Provide a Routing Strategy Using Cloud-Based Analytics 2017-01-0087
It is estimated that up to 30% of traffic in cities is due to drivers searching for parking. Research suggests that drivers spend an average of 6-14 minutes looking for an available space in London. This increases individual stress levels as well as congestion and pollution. Parking Guidance Systems provide an effective way to reduce parking search time by presenting drivers with dynamic information on parking. An accurate prediction and recommendation analytics algorithm is the key part of the system combining real time cloud-based analytics and historical data trends that can be integrated into a smart parking user application. This paper develops a prediction algorithm based on transient queuing theory and Laplace transform to predict parking occupancy thus predicting open parking locations.
Citation: Ma, J., Clausing, E., and Liu, Y., "Smart On-Street Parking System to Predict Parking Occupancy and Provide a Routing Strategy Using Cloud-Based Analytics," SAE Technical Paper 2017-01-0087, 2017, https://doi.org/10.4271/2017-01-0087. Download Citation
Author(s):
Jiaqi Ma, Erin Clausing, Yimin Liu
Affiliated:
Ford Motor Company
Pages: 9
Event:
WCX™ 17: SAE World Congress Experience
ISSN:
0148-7191
e-ISSN:
2688-3627
Related Topics:
Vehicle drivers
Mathematical models
Congestion
Research and development
SAE MOBILUS
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