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Mathematical Problems in Engineering
Volume 2013, Article ID 473471, 6 pages
Research Article

Development of Fuzzy Logic Forecast Models for Location-Based Parking Finding Services

1Ningbo University of Technology, Ningbo 315211, China
2Department of Spatial Sciences, Curtin University, Perth, WA 6845, Australia

Received 11 November 2012; Revised 11 February 2013; Accepted 11 February 2013

Academic Editor: Valentina E. Balas

Copyright © 2013 Zhirong Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Park-and-ride (PnR) facilities provided by Australian transport authorities have been an effective way to encourage car drivers to use public transport such as trains and buses. However, as populations grow and vehicle running costs increase, the demand for more parking spaces has escalated. Often, PnR facilities are filled to capacity by early morning and commuters resort to parking illegally in streets surrounding stations. This paper reports on the development of a location-based parking finding service for PnR users. Based on their current location, the system can inform users which is the best station to park their cars during peak period. Two criteria—parking availability and the shortest travel time—were used to evaluate the best station. Fuzzy logic forecast models were used to estimate the uncertainty of parking availability during the peak parking demand period. A prototype using these methods has been developed based on a case study of the Oats Street and Carlisle PnR facilities in Perth, Western Australia. The system has proved to be efficacious and has the potential to be applied to other parking systems.