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

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.

• Record URL:
  https://doi.org/10.4271/2017-01-0087
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/01487191
• Supplemental Notes:
  • Abstract reprinted with permission of SAE International.
• Authors:
  • Ma, Jiaqi
  • Clausing, Erin
  • Liu, Yimin
• Conference:
  • WCX™ 17: SAE World Congress Experience
  • Location: Detroit Michigan, United States
  • Date: 2017-4-4 to 2017-4-6
• Publication Date: 2017-3-28

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Serial:
  • SAE Technical Paper
  • Publisher: Society of Automotive Engineers (SAE)
  • ISSN: 0148-7191
  • EISSN: 2688-3627
  • Serial URL: http://papers.sae.org/

Subject/Index Terms

• TRT Terms: Environmental impacts; Intelligent transportation systems; On street parking; Parking guidance systems; Routing; Traffic congestion
• Geographic Terms: London (England)
• Subject Areas: Highways; Operations and Traffic Management; Vehicles and Equipment;

Filing Info

• Accession Number: 01644750
• Record Type: Publication
• Source Agency: SAE International
• Report/Paper Numbers: 2017-01-0087
• Files: TRIS, SAE
• Created Date: Aug 29 2017 10:10AM