Smart On-Street Parking System to Predict Parking Occupancy and Provide a Routing Strategy Using Cloud-Based Analytics Ford Motor Company

Format:

Conference/Event

Author/Creator:

Ma, Ma, author.

Contributor:

Clausing, Erin

Liu, Yimin

Conference Name:

WCX 17: SAE World Congress Experience (2017-04-04 : Detroit, Michigan, United States)

Language:

English

Physical Description:

1 online resource

Place of Publication:

Warrendale, PA SAE International 2017

Summary:

AbstractIt 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

Notes:

Vendor supplied data

Publisher Number:

2017-01-0087

Access Restriction:

Restricted for use by site license